AUXILIARY BERTHING METHOD AND SYSTEM FOR VESSEL

Abstract

The present invention provides an auxiliary berthing method and system for a vessel. Position information of a vessel relative to a berth is determined by a solar blind ultraviolet imaging method; meanwhile, by a GPS method, an attitude angle of the vessel relative to the berth is determined by at least two GPS receivers. Thus, the vessel can be berthed safely when getting close to the shore at low visibility. Further, in the method and device of the present invention, it can be preferable to integrate coordinate data and angle data received by a solar blind ultraviolet imaging module and GPS signal receiving modules by a normalized correlation algorithm and a data fusion algorithm, so as to improve the positioning accuracy.

Claims

1. An auxiliary berthing method for a vessel, comprising providing a solar blind ultraviolet imaging module and a data processing module on a vessel, the solar blind ultraviolet imaging module measuring, according to received optical signals transmitted by a solar blind ultraviolet light source array arranged in advance on the shore, information about a position relationship between the vessel and a related berth, characterized in that the method further comprises the steps of: 1) providing at least two GPS signal receiving modules, at least one of which is arranged on the vessel, used for receiving a position signal of the vessel from a related satellite; and 2) designing the data processing module to comprise a signal receiving element which can be matched with the solar blind ultraviolet imaging module and the GPS signal receiving module in a wired and/or wireless manner, receiving data related to the position of the vessel from the solar blind ultraviolet imaging module and the GPS signal receiving module, calculating coordinate values of a reference point of the vessel, and determining an attitude angle of the vessel relative to a shoreline of the berth according to the position data from the solar blind ultraviolet imaging module and the GPS signal receiving module mounted on the vessel.

2. The auxiliary berthing method for a vessel according to claim 1, characterized in that more than two GPS signal receiving modules are mounted on the vessel, which are respectively used for receiving a positioning signal from the related satellite and determining an attitude angle of the vessel relative to a shoreline of the berth according to a connection line between the GPS signal receiving modules on the vessel.

3. The auxiliary berthing method for a vessel according to claim 1, characterized in that at least one GPS signal receiving module is arranged on the shore; the GPS signal receiving modules on each vessel and the GPS signal receiving module on the shore work cooperatively to form a GPS differential system, wherein the GPS signal receiving module on the shore is used as a master GSP station, the GPS signal receiving modules on the vessel are used as slave GPS stations, and the accuracy of measurement of the position and attitude angle data of the vessel by the slave GPS stations is improved by the master GPS station; upon receiving position data from the related satellite, the master GPS station can directly transmit the position data to the data processing module to obtain the position data of the vessel; or, the master GPS station can transmit, to at least one slave GPS station, the position data and other data helpful in improving the accuracy of measurement of the position data by the slave GPS stations, and the slave GPS station integrates the received GPS position data, then processes the data and transmits the data to the data processing module so as to obtain the position data of the vessel.

4. The auxiliary berthing method for a vessel according to claim 3, characterized in that the master GPS station first transmits the position data to a transmission point in a wireless or wired manner, and then wirelessly transmits the position data from the transmission point to the slave GPS stations at a frequency identical to or different from the previous frequency.

5. The auxiliary berthing method for a vessel according to claim 2, characterized in that more than two GPS signal receiving modules are mounted on the vessel; the data processing module performs normalized correlation on N pieces of position data of the vessel obtained from the solar blind ultraviolet imaging module and the GPS signal receiving modules on the vessel: a threshold for an average confidence value of a detection system consisting of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the confidence of each module are obtained by global error analysis, positioning data with a lower confidence is filtered by using the threshold, a final confidence weight for each module is then obtained, and weighted averaging is performed on each module by using the confidence weight so as to obtain final data.

6. The auxiliary berthing method for a vessel according to claim 5, characterized in that the coordinate values of the positions of the solar blind ultraviolet imaging module and the GPS signal receiving modules are represented by x, y and z, respectively; a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent the i.sup.th group of positioning data among N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N, and N is equal to the number of the GPS signal receiving modules plus 1; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through spatially geometric transformation; and the data processing module performs normalized correlation by the following specific steps: using a Normalized Correlation Coefficient (NCC) to represent the confidence of the positioning data returned by the N groups of detection subsystems: $\begin{array}{cc}\begin{array}{c}\mathrm{NCC}\left(p_i\right)=\underset{\begin{array}{c}j=1\\ i\ne j\end{array}}{\overset{N}{.Math.}}.Math.\frac{p_i.Math.p_j}{.Math.p_i.Math..Math..Math.p_j.Math.}\\ =\underset{\begin{array}{c}j=1\\ i\ne j\end{array}}{\overset{N}{.Math.}}.Math.\frac{x_i.Math.x_j+y_i.Math.y_j+z_i.Math.z_j}{\sqrt{x_i^2+y_i^2+z_i^2}.Math.\sqrt{x_j^2+y_j^2+z_j^2}}\end{array}.Math..Math.j=1,2,3,\mathrm{.Math.}.Math.,N;& \left(1\right)\end{array}$ setting a threshold G for the average confidence value of the detection system consisting of all the solar blind ultraviolet imaging module and the GPS signal receiving modules, and filtering the positioning data with a lower NCC according to the threshold G to obtain a final system confidence weight w, which is expressed as follows: $\begin{array}{cc}w\left(p_i\right)=\{\begin{array}{c}\mathrm{NCC}\left(p_i\right),\mathrm{NCC}\left(p_i\right)>G\\ 0,\mathrm{NCC}\left(p_i\right)\le G\end{array};& \left(2\right)\end{array}$ then, obtaining the final fitted positioning data on the position of the vessel: $\begin{array}{cc}{p}_{\mathrm{result}}=\frac{\underset{i=1}{\overset{N}{.Math.}}.Math.w\left(p_i\right)\times p_i}{\underset{i=1}{\overset{N}{.Math.}}.Math.w\left(p_i\right)};& \left(3\right)\end{array}$ and calculating the fitted attitude angle data of the vessel according to the fitted coordinate values of the N−1 GPS signal receiving modules.

7. The auxiliary berthing method for a vessel according to claim 2, characterized in that the data processing module integrates the positioning data or the attitude angle data by a data fusion method, respectively; and, the data fusion method comprises the following specific steps: (I) when the data to be integrated is positioning data, a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through spatially geometric transformation; a) the confidence of the data returned by each subsystem is judged by using a root-mean-square-error rmse actually calculated by the measured data of each detection subsystem, where the formula for calculating the root-mean-square-error of the measured data of each subsystem is as follows: $\begin{array}{cc}\mathrm{rmse}=\sqrt{\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(x_i-x_f\right)}^2/\left(n+1\right)}& \left(4\right)\end{array}$ where rmse represents the root-mean-square-error, x.sub.i represents the measured data of each measurement subsystem on the X-axis coordinate at a moment i, x.sub.f represents the filtered value of the data x.sub.i at the moment i, n represents the total number of the measured data, i.e., the number of the subsystems, and the filtered value at the moment i is obtained by Kalman filtering; b) determination of a weight: weight assignment is performed by curve fitting on a segment basis: $\begin{array}{cc}\omega =\{\begin{array}{c}0,.Math.e.Math.\ge b\\ f\left(.Math.e.Math.\right),b\ge .Math.e.Math.\ge a\\ 1,.Math.e.Math.\le a\end{array}& \left(5\right)\end{array}$ where ω is the weight, the parameter b is the minimum limit for judging outliers, and the parameter a is a boundary value between a valid numerical value and an available numerical value; if the error is greater than b, the error is considered as an outlier and the corresponding weight is 0; if the error is less than a, the error is considered as a valid value and the corresponding weight is 1; the weight of an intermediate available value is given according to a curve y=f(x), and f(x) must fulfill the condition that, within an interval (a,b), f(x) decreases rapidly with the increase of the error; the f(x) is expressed as follows: $\begin{array}{cc}f\left(x\right)=\frac{1}{\sqrt{2.Math.\pi }.Math.\sigma }.Math.\exp \left(-\frac{{\left(x-\mu \right)}^2}{{}^2\sigma ^2}\right)& \left(6\right)\end{array}$ where μ and σ are a mean and a square for the normal distribution, respectively, since a normal curve exhibits the characteristics of a decreasing function within a region of x>μ, then μ=0; actually, a half-normal curve is applied; and the f(x) is further expressed as follows: $\begin{array}{cc}f\left(x\right)=\frac{1}{\sqrt{2.Math.\pi }.Math.\sigma }.Math.\exp \left(-\frac{x^2}{\hspace{0.17em}2.Math.{\sigma }^2}\right)& \left(7\right)\end{array}$ the value of σ is given according to the 3σ rule, and can be obtained by a normal curve fitting weight assignment method through the following formula: $\begin{array}{cc}{a}_{\mathrm{ki}}=\frac{f\left({\mathrm{rmse}}_{\mathrm{ki}}\right)}{\underset{i=1}{\overset{n}{.Math.}}.Math.f\left({\mathrm{rmse}}_{\mathrm{ki}}\right)}& \left(8\right)\end{array}$ furthermore, $\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}=1,$ where rmse.sub.ki represents the root-mean-square-error of the i.sup.th system at a moment k, and a.sub.ki represents the weight of the i.sup.th system at the moment k; c) the final result of data fusion is as follows: $\begin{array}{cc}{\hat{X}}_{\mathrm{ki}}=\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}.Math.X_{\mathrm{ki}}& \left(9\right)\end{array}$ where {circumflex over (X)}.sub.ki is the fused value at the moment k, and x.sub.ki represents the measured data obtained by each subsystem at the moment k; and d) by the same method as in the steps a) to c), a final result of data fusion of the Y-axis coordinate value y and the Z-axis coordinate value z is calculated; and (II) when the data to be integrated is the attitude angle data, a vector q.sub.i(α.sub.i,β.sub.i,γ.sub.i) is used to represent N groups of attitude angle data returned by the N measurement subsystems, where i=1, 2, 3 . . . N; and, by the same method as in the step (I), the integrated attitude angle data is calculated.

8. The auxiliary berthing method for a vessel according to claim 2, characterized in that the data processing module integrates the positioning data or the attitude angle data by a data fusion method, respectively; and, the data fusion method comprises the following specific steps: (I) when the data to be integrated is the positioning data, a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through a spatially geometric transformation; a) a standard deviation of each coordinate sequence in the position data is calculated: the standard deviation of each coordinate sequence in the N groups of positioning data returned by the N groups of detection subsystems is calculated as the basis for judging outliers in each coordinate sequence in the N groups of data, where the standard deviation of each coordinate sequence is as follows:
σ.sub.index=√{square root over ((X.sub.index−X.sub.index).sup.2/)}.sub.N  (10) where, if indexε(x,y,z), σ.sub.index presents the standard deviation of each coordinate sequence in the N groups of data, X.sub.index represents the N groups of measured data, each of which contains a coordinate value (x,y,z), and X.sub.index represents the average value of the N groups of data, i.e., a one-dimensional vector formed by the average value of each coordinate sequence; b) outliers in each coordinate sequence are obtained according to the calculated standard deviation, wherein the outliers can be judged by the following formula:
outliters=|X.sub.index−X.sub.index|>c*σ.sub.index  (11) where outliers represent the obtained outliers; once a coordinate value in a group of coordinate data consisting of x,y,z is judged as an outlier in its sequence, this group of coordinate values is judged as an outliner in the N groups of coordinate data; C is a constant coefficient determined according to experimental experiences and requirements; and the constant can be determined by: judging a fluctuation range of test values through lots of tests, selecting a symmetric range by using a mean of the test values as a center with lots of unreasonable points going beyond this range, and using half of the length of this range as C; c) the outliers are removed from the N groups of original measured data to obtain a new positioning data sequence X′ having a dimensionality of N′, and performing equally-weighted average data fusion on X′ to obtain final fused data, as follows: $\begin{array}{cc}{\hat{X}}^{\prime }=\frac{1}{N^{\prime }}.Math.\underset{i=1}{\overset{N}{.Math.}}.Math.X^{\prime }& \left(12\right)\end{array}$ where {circumflex over (X)}′ is the final positioning data after data fusion; and d) by the same method as in the steps a) to c), a final result of data fusion of the Y-axis coordinate value y and the Z-axis coordinate value z is calculated; and (II) when the data to be integrated is the attitude angle data, a vector q.sub.i(α.sub.i,β.sub.i,γ.sub.i) is used to represent N groups of attitude angle data returned by the N groups of detection subsystems, where i=1, 2, 3 . . . N; and, by the same method as in the step (I), the integrated attitude angle data is calculated.

9. The auxiliary berthing method for a vessel according to claim 1, characterized in that, before the measurement, the solar blind ultraviolet imaging module is calibrated to determine measurement-related photoelectric parameters of the solar blind ultraviolet imaging module.

10. The auxiliary berthing method for a vessel according to claim 9, characterized in that the measurement-related photoelectric parameters of the solar blind ultraviolet imaging module comprise focal lengths f.sub.x and f.sub.y in pixels in the x-axis direction and the y-axis direction, positions c.sub.x and c.sub.y of reference points in an image plane, and radial distortion coefficients k.sub.x and k.sub.y in the x-axis direction and the y-axis direction.

11. The auxiliary berthing method for a vessel according to claim 1, characterized in that a power control system of the vessel receives a berthing distance signal of the solar blind ultraviolet light source array transmitted by the data processing module, and thereby automatically adjusts the attitude of the vessel for berthing.

12. An auxiliary berthing system for a vessel, comprising: a solar blind ultraviolet imaging module, which is arranged on a vessel and configured to measure, according to received optical signals transmitted by a solar blind ultraviolet light source array arranged in advance on the shore, information about a position relationship between the vessel and a related berth; and, a data processing module, which is electrically connected to the solar blind ultraviolet imaging module and configured to process the received data of the solar blind ultraviolet imaging module to obtain coordinates of the vessel, characterized in that the system further comprises at least two GPS signal receiving modules, at least one GPS signal receiving module is mounted on the vessel, and each GPS signal receiving module comprises a satellite signal receiving portion used for receiving a positioning signal from a related satellite and a signal transmission portion for transmitting the received satellite signal to the data processing module; the data processing module is electrically connected to the GPS signal receiving modules, and processes the positioning data received from the related satellite by the GPS signal receiving modules and thereby determines an attitude angle of the vessel.

13. The auxiliary berthing system for a vessel according to claim 12, characterized in that the GPS signal receiving module mounted on the vessel and the GPS signal receiving module arranged on the shore work cooperatively to form a GPS differential system, wherein the GPS signal receiving module on the shore is used as a master GSP station, and the GPS signal receiving module on the vessel is used as a slave GPS station; the slave GPS station receives, from the related satellite, its own position data, receives, from the master GPS station, the position data of the master GPS station and other data helpful in improving the accuracy of measurement of the position data by the slave GPS station, and processes the data or transmits the data to the data processing module for processing to obtain data representing the position and attitude angle of the vessel.

14. The auxiliary berthing system for a vessel according to claim 13, characterized in that all the GPS signal receiving modules are mounted on the vessel.

15. The auxiliary berthing system for a vessel according to claim 14, characterized in that the data processing module integrates coordinate values obtained by the solar blind ultraviolet imaging module and the GPS signal receiving modules by a normalized correlation algorithm; x, y and z represent three-axis coordinates of the solar blind ultraviolet imaging module and the two GPS signal receiving modules, respectively; a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent the i.sup.th group of positioning data among N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N, and N is equal to the number of the GPS signal receiving modules plus 1; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through spatially geometric transformation; and the data processing module performs normalized correlation by the following specific steps: using a Normalized Correlation Coefficient (NCC) to represent the confidence of the positioning data returned by the N groups of detection subsystems: $\begin{array}{cc}\begin{array}{c}\mathrm{NCC}\left(p_i\right)=\underset{\begin{array}{c}j=1\\ i\ne j\end{array}}{\overset{N}{.Math.}}.Math.\frac{p_i.Math.p_j}{.Math.p_i.Math..Math..Math.p_j.Math.}\\ =\underset{\begin{array}{c}j=1\\ i\ne j\end{array}}{\overset{N}{.Math.}}.Math.\frac{x_i.Math.x_j+y_i.Math.y_j+z_i.Math.z_j}{\sqrt{x_i^2+y_i^2+z_i^2}.Math.\sqrt{x_j^2+y_j^2+z_j^2}}\end{array}.Math..Math.j=1,2,3,\mathrm{.Math.}.Math.,N;& \left(13\right)\end{array}$ setting a threshold G for the average confidence value of the detection system consisting of all the solar blind ultraviolet imaging module and the GPS signal receiving modules, and filtering the positioning data with a lower NCC according to the threshold G to obtain a final system confidence weight w, which is expressed as follows: $\begin{array}{cc}w\left(p_i\right)=\{\begin{array}{c}\mathrm{NCC}\left(p_i\right),\mathrm{NCC}\left(p_i\right)>G\\ 0,\mathrm{NCC}\left(p_i\right)\le G\end{array};& \left(14\right)\end{array}$ then, obtaining the final fitted positioning data on the position of the vessel: $\begin{array}{cc}{p}_{\mathrm{result}}=\frac{\underset{i=1}{\overset{N}{.Math.}}.Math.w\left(p_i\right)\times p_i}{\underset{i=1}{\overset{N}{.Math.}}.Math.w\left(p_i\right)};& \left(15\right)\end{array}$ and calculating the fitted attitude angle data of the vessel according to the fitted coordinate values of the N−1 GPS signal receiving modules.

16. The auxiliary berthing system for a vessel according to 14, characterized in that, by using a data fusion method, the data processing module integrates the coordinate data received by the GPS signal receiving modules, or integrates the coordinate data measured by the GPS signal receiving modules with the coordinate data measured by the solar blind ultraviolet imaging module, or integrates the attitude angle received by the GPS signal receiving modules; and, the data fusion method comprises the following specific steps: (I) when the data to be integrated is the positioning data, a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through spatially geometric transformation; a) the confidence of the data returned by each subsystem is judged by using a root-mean-square-error rmse actually calculated by the measured data of each detection subsystem, where the formula for calculating the root-mean-square-error of the measured data of each subsystem is as follows: $\begin{array}{cc}\mathrm{rmse}=\sqrt{\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(x_i-x_f\right)}^2/\left(n+1\right)}& \left(16\right)\end{array}$ where rmse represents the root-mean-square-error, x.sub.i represents the measured data of each measurement subsystem on the X-axis coordinate at a moment i, x.sub.f represents the filtered value of the data x.sub.i at the moment i, n represents the total number of the measured data, i.e., the number of the subsystems, and the filtered value at the moment i is obtained by Kalman filtering; b) determination of a weight: weight assignment is performed by curve fitting on a segment basis: $\begin{array}{cc}\omega =\{\begin{array}{c}0,.Math.e.Math.\ge b\\ f\left(.Math.e.Math.\right),b\ge .Math.e.Math.\ge a\\ 1,.Math.e.Math.\le a\end{array}& \left(17\right)\end{array}$ where ω is the weight, the parameter b is the minimum limit for judging outliers, and the parameter a is a boundary value between a valid numerical value and an available numerical value; if the error is greater than b, the error is considered as an outlier and the corresponding weight is 0; if the error is less than a, the error is considered as a valid value and the corresponding weight is 1; the weight of an intermediate available value is given according to a curve y=f(x), and f(x) must fulfill the condition that, within an interval (a,b), f(x) decreases rapidly with the increase of the error; the f(x) is expressed as follows: $\begin{array}{cc}f\left(x\right)=\frac{1}{\sqrt{2.Math.\pi }.Math.\sigma }.Math.\exp \left(-\frac{{\left(x-\mu \right)}^2}{{}^2\sigma ^2}\right)& \left(18\right)\end{array}$ where μ and σ are a mean and a square for the normal distribution, respectively, since a normal curve exhibits the characteristics of a decreasing function within a region of x>μ, then μ=0; actually, a half-normal curve is applied; and the f(x) is further expressed as follows: $\begin{array}{cc}f\left(x\right)=\frac{1}{\sqrt{2.Math.\pi }.Math.\sigma }.Math.\exp \left(-\frac{x^2}{\hspace{0.17em}2.Math.{\sigma }^2}\right)& \left(19\right)\end{array}$ the value of σ is given according to the 3σ rule, and can be obtained by a normal curve fitting weight assignment method through the following formula: $\begin{array}{cc}{a}_{\mathrm{ki}}=\frac{f\left({\mathrm{rmse}}_{\mathrm{ki}}\right)}{\underset{i=1}{\overset{n}{.Math.}}.Math.f\left({\mathrm{rmse}}_{\mathrm{ki}}\right)}& \left(20\right)\end{array}$ furthermore, $\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}=1,$ where rmse.sub.ki represents the root-mean-square-error of the i.sup.th system at a moment k, and a.sub.ki represents the weight of the i.sup.th system at the moment k; c) the final result of data fusion is as follows: $\begin{array}{cc}{\hat{X}}_{\mathrm{ki}}=\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}.Math.X_{\mathrm{ki}}& \left(21\right)\end{array}$ where {circumflex over (X)}.sub.ki is the fused value at the moment k, and x.sub.ki represents the measured data obtained by each subsystem at the moment k; d) by the same method as in the steps a) to c), a final result of data fusion of the Y-axis coordinate value y and the Z-axis coordinate value z is calculated; and (II) when the data to be integrated is the attitude angle data, a vector q.sub.i(α.sub.i,β.sub.i,γ.sub.i) is used to represent N groups of attitude angle data returned by the N groups of detection subsystems, where i=1, 2, 3 . . . N; and, by the same method as in the step (I), the integrated attitude angle data is calculated.

17. The auxiliary berthing system for a vessel according to claim 13, characterized in that a power control system of the vessel receives a berthing distance signal of the solar blind ultraviolet light source array transmitted by the data processing module, and thereby automatically adjusts the attitude of the vessel for berthing.

18. The auxiliary berthing method for a vessel according to claim 3, characterized in that more than two GPS signal receiving modules are mounted on the vessel; the data processing module performs normalized correlation on N pieces of position data of the vessel obtained from the solar blind ultraviolet imaging module and the GPS signal receiving modules on the vessel: a threshold for an average confidence value of a detection system consisting of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the confidence of each module are obtained by global error analysis, positioning data with a lower confidence is filtered by using the threshold, a final confidence weight for each module is then obtained, and weighted averaging is performed on each module by using the confidence weight so as to obtain final data.

19. The auxiliary berthing method for a vessel according to claim 3, characterized in that the data processing module integrates the positioning data or the attitude angle data by a data fusion method, respectively; and, the data fusion method comprises the following specific steps: (I) when the data to be integrated is positioning data, a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through spatially geometric transformation; a) the confidence of the data returned by each subsystem is judged by using a root-mean-square-error rmse actually calculated by the measured data of each detection subsystem, where the formula for calculating the root-mean-square-error of the measured data of each subsystem is as follows: $\begin{array}{cc}\mathrm{rmse}=\sqrt{\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(x_i-x_f\right)}^2/\left(n+1\right)}& \left(4\right)\end{array}$ where rmse represents the root-mean-square-error, x.sub.i represents the measured data of each measurement subsystem on the X-axis coordinate at a moment i, x.sub.f represents the filtered value of the data x.sub.i at the moment i, n represents the total number of the measured data, i.e., the number of the subsystems, and the filtered value at the moment i is obtained by Kalman filtering; b) determination of a weight: weight assignment is performed by curve fitting on a segment basis: $\begin{array}{cc}\omega =\{\begin{array}{c}0,.Math.e.Math.\ge b\\ f\left(.Math.e.Math.\right),b\ge .Math.e.Math.\ge a\\ 1,.Math.e.Math.\le a\end{array}& \left(5\right)\end{array}$ where ω is the weight, the parameter b is the minimum limit for judging outliers, and the parameter a is a boundary value between a valid numerical value and an available numerical value; if the error is greater than b, the error is considered as an outlier and the corresponding weight is 0; if the error is less than a, the error is considered as a valid value and the corresponding weight is 1; the weight of an intermediate available value is given according to a curve y=f(x), and f(x) must fulfill the condition that, within an interval (a,b), f(x) decreases rapidly with the increase of the error; the f(x) is expressed as follows: $\begin{array}{cc}f\left(x\right)=\frac{1}{\sqrt{2.Math.\pi }.Math.\sigma }.Math.\exp \left(-\frac{{\left(x-\mu \right)}^2}{{}^2\sigma ^2}\right)& \left(6\right)\end{array}$ where μ and σ are a mean and a square for the normal distribution, respectively, since a normal curve exhibits the characteristics of a decreasing function within a region of x>μ, then μ=0; actually, a half-normal curve is applied; and the f(x) is further expressed as follows: $\begin{array}{cc}f\left(x\right)=\frac{1}{\sqrt{2.Math.\pi }.Math.\sigma }.Math.\exp \left(-\frac{x^2}{\hspace{0.17em}2.Math.{\sigma }^2}\right)& \left(7\right)\end{array}$ the value of σ is given according to the 3σ rule, and can be obtained by a normal curve fitting weight assignment method through the following formula: $\begin{array}{cc}{a}_{\mathrm{ki}}=\frac{f\left({\mathrm{rmse}}_{\mathrm{ki}}\right)}{\underset{i=1}{\overset{n}{.Math.}}.Math.f\left({\mathrm{rmse}}_{\mathrm{ki}}\right)}& \left(8\right)\end{array}$ furthermore, $\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}=1,$ where rmse.sub.ki represents the root-mean-square-error of the i.sup.th system at a moment k, and a.sub.ki represents the weight of the i.sup.th system at the moment k; c) the final result of data fusion is as follows: $\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}=1,$ where {circumflex over (X)}.sub.ki is the fused value at the moment k, and x.sub.ki represents the measured data obtained by each subsystem at the moment k; and d) by the same method as in the steps a) to c), a final result of data fusion of the Y-axis coordinate value y and the Z-axis coordinate value z is calculated; and (II) when the data to be integrated is the attitude angle data, a vector q.sub.i(α.sub.i,β.sub.i,γ.sub.i) is used to represent N groups of attitude angle data returned by the N measurement subsystems, where i=1, 2, 3 . . . N; and, by the same method as in the step (I), the integrated attitude angle data is calculated.

20. The auxiliary berthing method for a vessel according to claim 3, characterized in that the data processing module integrates the positioning data or the attitude angle data by a data fusion method, respectively; and, the data fusion method comprises the following specific steps: (I) when the data to be integrated is the positioning data, a vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent N groups of positioning data, which are subjected to angular and spatial transformation, returned by N groups of detection subsystems, where i=1, 2, 3 . . . N; the positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through a spatially geometric transformation; a) a standard deviation of each coordinate sequence in the position data is calculated: the standard deviation of each coordinate sequence in the N groups of positioning data returned by the N groups of detection subsystems is calculated as the basis for judging outliers in each coordinate sequence in the N groups of data, where the standard deviation of each coordinate sequence is as follows:
σ.sub.index=√{square root over ((X.sub.index−X.sub.index).sup.2/)}.sub.N  (10) where, if indexε(x,y,z), σ.sub.index presents the standard deviation of each coordinate sequence in the N groups of data, X.sub.index represents the N groups of measured data, each of which contains a coordinate value (x,y,z), and X.sub.index represents the average value of the N groups of data, i.e., a one-dimensional vector formed by the average value of each coordinate sequence; b) outliers in each coordinate sequence are obtained according to the calculated standard deviation, wherein the outliers can be judged by the following formula:
outliters=|X.sub.index−X.sub.index|>c*σ.sub.index  (11) where outliers represent the obtained outliers; once a coordinate value in a group of coordinate data consisting of x,y,z is judged as an outlier in its sequence, this group of coordinate values is judged as an outliner in the N groups of coordinate data; C is a constant coefficient determined according to experimental experiences and requirements; and the constant can be determined by: judging a fluctuation range of test values through lots of tests, selecting a symmetric range by using a mean of the test values as a center with lots of unreasonable points going beyond this range, and using half of the length of this range as C; c) the outliers are removed from the N groups of original measured data to obtain a new positioning data sequence X′ having a dimensionality of N′, and performing equally-weighted average data fusion on X′ to obtain final fused data, as follows: $\begin{array}{cc}{\hat{X}}_{\mathrm{ki}}=\underset{i=1}{\overset{N}{.Math.}}.Math.a_{\mathrm{ki}}.Math.X_{\mathrm{ki}}& \left(9\right)\end{array}$ where {circumflex over (X)}′ is the final positioning data after data fusion; and d) by the same method as in the steps a) to c), a final result of data fusion of the Y-axis coordinate value y and the Z-axis coordinate value z is calculated; and (II) when the data to be integrated is the attitude angle data, a vector q.sub.i(α.sub.i,β.sub.i,γ.sub.i) is used to represent N groups of attitude angle data returned by the N groups of detection subsystems, where i=1, 2, 3 . . . N; and, by the same method as in the step (I), the integrated attitude angle data is calculated.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0068] FIG. 1 is a block diagram of a system for berthing and navigating a vessel according to the present invention;

[0069] FIG. 2 is a diagram showing a mounting position of the equipment;

[0070] FIG. 3 is a flowchart of camera calibration;

[0071] FIG. 4 is a diagram showing an ultraviolet array and a shooting position;

[0072] FIG. 5 is a diagram showing the position of a solar blind ultraviolet lamp array;

[0073] FIG. 6 shows a lattice coordinate system (a) and a camera coordinate system (b);

[0074] FIG. 7 is a flowchart of execution of berthing software;

[0075] FIG. 8 is a schematic diagram of a vessel and a shoreline;

[0076] FIG. 9 is a schematic diagram of positions of measurement modules; and

[0077] FIG. 10 is a flowchart of a normalized correlation algorithm.

DETAILED DESCRIPTION OF THE INVENTION

[0078] According an example of the present invention, a system for improving a vessel's close-distance navigation capability in the foggy weather may be provided. This system is able to display a schematic diagram of a vessel and a shoreline and the position information, so that a pilot is able to berth the vessel at low visibility through an output interface of a display device.

[0079] For this purpose, the present invention will be further described below with reference to the accompanying drawings by embodiments. The following embodiments are merely illustrative, and the present invention is not limited to the solutions in the embodiments. Additionally, all technical solutions obtained by simple transformation by those skilled in the art within the scope of the prior art shall fall into the protection scope of the present invention.

Embodiment 1

[0080] FIG. 1 shows a block diagram of a system for berthing and navigating a vessel. The present invention mainly solves the problem on close-distance berthing of the vessel in the foggy weather. In this embodiment, the vessel navigation system includes a group of solar blind ultraviolet lamps 101, two GPS modules 112 and 113, a solar blind ultraviolet imaging module 103, a data processing module 104 and a display device 105.

[0081] All the GPS modules 112 and 113 and the solar blind ultraviolet imaging module 103 are mounted on the vessel. The differential GPS modules 112 and 113 are preferably mounted at such a position that it is most convenient to determine the course. In this embodiment, the two differential GPSs are mounted on decks on two sides of a cab, and a connection line between the two differential GPS modules may be approximately perpendicular to a connection line between the head portion and tail portion of the vessel. In the two differential GPS modules, the master GPS module (also called a master station) 112 is mounted at a position close to the shoreline, and the slave GPS module (also called a slave station) 113 is mounted at a position away from the shoreline. The solar blind ultraviolet imaging module 103 is mounted on a deck on one side of the vessel. For convenience of subsequent calculation, in this embodiment, positions on the vessel having a marked distance away from the head portion and the tail portion are preferably selected. The distances from the solar blind ultraviolet module to the head portion and the tail portion are L.sub.1 and L.sub.2, respectively, which are known and have been marked on the vessel. The specific mounting positions are roughly shown in FIG. 2. The gray area 100 in FIG. 1 is the shoreline where the vessel is going to berth.

[0082] The solar blind ultraviolet imaging module 103, the signal processor 104 and the display device 105 may be integrated together. The data processing module 104 includes an information acquisition module, a calculation module and a storage module.

[0083] This embodiment includes the following main steps, among which steps 1 to 3 are preparation operations on the shore, and steps 4 to 5 are operations performed on the vessel.

[0084] 1. A camera is calibrated to obtain internal parameters. There are many methods for calibrating the camera and algorithms for obtaining the internal parameters. Here, a conventional calibration technology or Zhenyou Zhang's calibration algorithm is preferably selected. The calibration flow is shown in FIG. 3:

[0085] step 1-1-1: an ultraviolet array is arranged;

[0086] step 1-1-2: geometrical information of the ultraviolet array is measured;

[0087] step 1-2: the ultraviolet array is shot by an ultraviolet receiver, where the ultraviolet array and the shooting position are shown in FIG. 4;

[0088] step 2-1: coordinates are extracted; and

[0089] step 2-2: the internal parameters of the equipment are solved to obtain image plane coordinates of a specified ultraviolet light source, and the internal parameters (f.sub.x, f.sub.y, c.sub.x, c.sub.y, k.sub.x, k.sub.y, etc.) of the camera are obtained by the calibration algorithm.

[0090] 2. Berth information is measured: an included angle θ between each berth shoreline in a port for berthing and a certain direction (for example, the north direction in this example) is measured in advance.

[0091] A yaw angle of the berth shoreline is measured by the differential GPSs (including the master station and the slave station) or other tools for measuring the yaw angle. When the differential GPS devices are used, the master station and the slave station may be placed on the head and tail sides of the berth, with an approximately equal distance to the berth offline.

[0092] 3. The ultraviolet light source lamp array is arranged and the related position information of the lamp array is measured: within a certain period of time (e.g., half an hour) before berthing the vessel, a target lamp array is arranged nearby the berth 100 by using the group of solar blind ultraviolet lamps 101. The target lamp array is in a shape of a square grid. The size of the target lamp array and the number of lamps are not limited. In this embodiment, the arrangement of FIG. 5 is used, where the size of the lamp array is 8 m×8 m, with an equal spacing between the lamps in each row and an equal row pitch.

[0093] In this embodiment, the distance from a reference point of the lamp array to a bollard of the berth is set as L.sub.2 (which may be measured by measurement tools such as a flexible rule, where L.sub.2 is the distance from the solar blind ultraviolet imaging image 103 to the tail portion of the vessel and is known; the setting of L.sub.2 is to enable the solar blind ultraviolet detector to directly face the lamp array during the berthing of the vessel so as to determine the X direction of the vessel relative to the berth. Of course, other distance L.sub.n may also be used as long as the distance between L.sub.2 and L.sub.n is known). During the arrangement of the lamp array, the vertical distance from the first row of the lamp array to an anti-collision fender is L (which may also be measured by simple length measurement tools such as a flexible rule), as shown in FIG. 5.

[0094] 4. The course of the vessel and the attitude and position information of the berth shoreline are calculated. The following steps are included.

[0095] Firstly, a relationship between the course of the vessel and the direction of the berth shoreline (i.e., an included angle between the course of the vessel and the berth shoreline) is calculated. Specifically:

[0096] The slave GPS station 113 transmits latitude-longitude information about its own position to the master GPS station 112; and the master GPS station 112 obtains the distance between them from the latitude-longitude information of the slave GPS 113 station and its own latitude-longitude information, and also obtains an included angle α between a vector r from the slave GPS station 113 to the master GPS station 112 and the north direction and an included angle β between the r and the horizontal direction, where β is the roll angle of the vessel.

[0097] Since the vector r from the slave GPS station 113 to the master GPS station 112 is perpendicular to the course of the vessel, an included angle γ between the course of the vessel and the north direction can be obtained, where γ is the course angle of the vessel.

[0098] When the vessel is berthed to the right, γ=α−90°; and

[0099] when the vessel is berthed to the left, γ=α+90°.

[0100] The included angle θ between each berth shoreline and the north direction is measured in advance, and the included angle between the course of the vessel and the berth shoreline may be obtained by the angle θ and the angle γ and then displayed on the display device 105 in form of an image, where a=γ−θ.

[0101] Secondly, position information of the vessel relative to the shoreline is determined.

[0102] When there is a close distance from the vessel to the shoreline, the solar blind ultraviolet imaging module can clearly identify all solar blind ultraviolet signals. Then, the signal processor 104 performs image processing and coordinate transformation on the image taken by the solar blind ultraviolet imaging module 103, to obtain position information X, Y and Z of the solar blind ultraviolet imaging module 103 in the coordinate system of the lamp array.

[00012]$\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=R^{-1}.Math.\left(-T\right)$

[0103] where R is a rotation matrix, and T is a translation vector.

[0104] The algorithm includes the following specific steps:

[0105] since the internal parameters of the camera, the lattice coordinates and image plane coordinates of the target lattice coordinate system (referring to FIG. 6) are known by the camera calibration, coordinates and rotation direction of the camera in the target lattice coordinate system may be obtained:

[00013]$\begin{array}{cc}\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{ccc}{f}_x& 0& c_x\\ 0& f_y& c_y\\ 0& 0& 1\end{array}\right].Math.\left(R.Math.\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]+T\right)& \left(1\right)\end{array}$

[0106] where (f.sub.x,f.sub.y,c.sub.x,c.sub.y) represents camera intrinsic matrix, R is a rotation matrix, T is a translation vector, (u,v) is image plane coordinates (in pixels), and (X,Y,Z) represents lattice coordinates in the target lattice coordinate system; and, the formula may be simplified as follows:

[00014]$\begin{array}{cc}\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=R.Math.\left[\begin{array}{c}x\\ y\\ z\end{array}\right]+T& \left(2\right)\end{array}$

[0107] where (x,y,z) represents coordinates of a target lattice in the camera coordinate system (referring to FIG. 6), so that R and T may be interpreted as a transformation matrix for transforming the target lattice coordinate system into the camera coordinate system.

[0108] During the calculation of the coordinates of the camera in the target lattice coordinate system, since both the internal parameters (f.sub.x,f.sub.y,c.sub.x,c.sub.y) and the lattice coordinates (X,Y,Z) in the target lattice coordinate system are fixed values and the image plane coordinates (u,v) are acquired from an image in real time, the rotation matrix R.sub.0 and the translation vector T.sub.0 at the same moment (u.sub.0,v.sub.0) may be correspondingly obtained in real time. Then, if it is required to obtain the lattice coordinates of the camera in the target lattice coordinate system, the origin (0,0,0) of the camera coordinate system is substituted into the left side of the formula 2 to solve (X.sub.0,Y.sub.0,Z.sub.0) on the right side, thus:

[00015]$\begin{array}{cc}\left[\begin{array}{c}{X}_0\\ Y_0\\ Z_0\end{array}\right]=R_0^{-1}.Math.\left(-T_0\right)& \left(3\right)\end{array}$

[0109] An inverse matrix R.sub.0.sup.−1 of the rotation matrix represents the rotation of the camera coordinate system relative to the target lattice coordinate system, and may be simplified into a rotation vector through transformation. This vector represents an Euler angle of rotation of the camera relative to the target lattice coordinate system.

[0110] Among the fixed values mentioned during the calculation of the camera coordinates, the target lattice coordinate is a result measured after manual arrangement, and the internal parameters represent intrinsic parameters of the camera, where f.sub.x and f.sub.y are values of focal lengths by using the number of pixels in the horizontal direction and the vertical direction as a unit of measurement, and c.sub.x and c.sub.y are coordinates of pixels formed by the right front of the center of a camera lens (i.e., a point on a theoretically optical axis) on an image plane.

[0111] If the distance from the solar blind ultraviolet imaging module 103 to the shoreline in a vertical direction, i.e., along the vessel's rail, is set as Y.sub.rail, Y.sub.rail=Y−L−Z*tan β, where L is the distance from the first row of the lamp array to the anti-collision fender and β is the roll angle of the vessel.

[0112] If the distance from the head portion of the vessel to the shoreline in the vertical direction is set as Y.sub.head and the distance from the tail portion of the vessel to the shoreline in the vertical direction is set as Y.sub.tail, Y.sub.head=Y.sub.rail−L.sub.1*sin(γ−θ), and Y.sub.tail=Y.sub.rail+L.sub.2*sin(γ−θ), where L.sub.1 and L.sub.2 are the distances from the solar blind ultraviolet imaging module 103 to the head portion of the vessel and the tail portion of the vessel, respectively, and γ and θ are the included angle between the course of the vessel and the north direction and the included angle between the berth shoreline and the north direction, respectively.

[0113] 5. Scene simulation is performed to output a schematic navigation diagram and the position coordinate information into the display device 105. FIG. 7 shows a flowchart of execution of the berthing software. Before the signal processor 104 operates, information about the berth, including the berth number and direction information of the vessel relative to the shoreline during berthing (i.e., berthing to the left or berthing to the right), is input; position information L.sub.1 and L.sub.2 of the solar blind ultraviolet imaging module on the vessel is input, where L.sub.1 and L.sub.2 are the distances from the solar blind ultraviolet imaging module to the head portion of the vessel and to the tail portion of the vessel, respectively; and, the width B of the vessel is input.

[0114] According to the position information X and Y of the vessel in the lamp array coordinate system, the direction information γ−θ of the vessel relative to the shoreline, the position information L.sub.1 and L.sub.2 of the solar blind ultraviolet imaging module relative to the vessel and the width B of the vessel, the schematic diagram and position information Y.sub.head and Y.sub.tail of the vessel and the shoreline may be displayed on the display device 105, as shown in FIG. 8. Thus, the pilot can realize the berthing of the vessel at low visibility through the output interface of the display device.

Embodiment 2

[0115] This embodiment describes how to obtain optimal position information from multiple groups of data by the following algorithm.

[0116] A vector p.sub.i(x.sub.i,y.sub.i,z.sub.i) is used to represent the positioning data, which is subjected to angular and spatial transformation, returned by N systems, where i=1, 2, 3 . . . N. The positioning data, which is subjected to angular and spatial transformation, is obtained by the following method: when the relative positions of all the solar blind ultraviolet imaging module and the GPS signal receiving modules and the attitude angle of the vessel are known, the measured position data of different measurement modules are converted into the measured position data of a same measurement module based on a spatial position relationship and through spatially geometric transformation. The specific transformation method is as follows:

[0117] (1) a reference point is determined, wherein the reference point may be the position of any one of the solar blind ultraviolet receiving module and the GPS signal receiving modules, or may be another point;

[0118] (2) the distance from each other measurement module to the reference point and a direction angle (which is a parameter for a light source reference system and needs to be determined by superposing the attitude angle of the vessel) are measured, so that a corresponding transformation vector is obtained; and

[0119] (3) the transformation vector is added to the relative position coordinate parameters obtained by each measurement module to obtain the transformed positioning data.

[0120] As shown in FIG. 9, measurement coordinates of two measurement modules are p.sub.1(x.sub.1, y.sub.1, z.sub.1) and p.sub.2′ (x.sub.2′, y.sub.2′, z.sub.2′), respectively. If it is assumed that p.sub.1 is used as a reference, the measured distance between the both is L, an included angle between the connection line of the both and the course is θ, and an angle of pitch is φ (an included angle with the XY plane), a method for) calculating a transformation vector {right arrow over (A)}=(a,b,c) is as follows:

[00016]$\begin{array}{cc}\{\begin{array}{c}a=L\times \cos .Math..Math.\varphi \times \cos .Math..Math.\theta \\ b=L\times \cos .Math..Math.\varphi \times \sin .Math..Math.\theta \\ c=L\times \sin .Math..Math.\theta \end{array}& \left(4\right)\end{array}$

[0121] Then, the coordinate of p.sub.2′ after its transformation to the reference position is p.sub.2=p.sub.2′+{right arrow over (A)}.

[0122] The transformed coordinates of other measurement modules may be obtained by the same method.

[0123] In this algorithm, a Normalized Correlation Coefficient (NCC) is used to represent the confidence of the positioning data returned by each system, which is expressed as follows:

[00017]$\begin{array}{cc}\mathrm{NCC}\left(p_i\right)=\underset{j=1.Math..Math.i\ne j}{\overset{N}{.Math.}}.Math..Math.\frac{p_i.Math.p_j}{.Math.p_i.Math..Math..Math.p_j.Math.}=\underset{j=1.Math..Math.i\ne j}{\overset{N}{.Math.}}.Math..Math.\frac{x_i.Math.x_j+y_i.Math.y_j+z_i.Math.z_j}{\sqrt{x_i^2+y_i^2+z_i^2}.Math.\sqrt{x_j^2+y_j^2+z_j^2}}& \left(5\right)\end{array}$

[0124] If a threshold is set to be 80% of the average confidence value of all systems, the threshold G may be expressed as follows:

[00018]$\begin{array}{cc}G=\frac{0.8}{N}.Math.\underset{i=1}{\overset{N}{.Math.}}.Math..Math.\mathrm{NCC}\left(p_i\right)& \left(6\right)\end{array}$

[0125] The positioning data with a lower NCC is filtered according to the threshold G, to obtain a final system confidence weight w, which is expressed as follows:

[00019]$\begin{array}{cc}w\left(p_i\right)=\{\begin{array}{c}\mathrm{NCC}\left(p_i\right),\mathrm{NCC}\left(p_i\right)>G\\ 0,\mathrm{NCC}\left(p_i\right)\le G\end{array}& \left(7\right)\end{array}$

[0126] Thus, the final fitted positioning data is obtained:

[00020]$\begin{array}{cc}{p}_{\mathrm{result}}=\frac{\underset{i=1}{\overset{N}{.Math.}}.Math..Math.w\left(p_i\right)\times p_i}{\underset{i=1}{\overset{N}{.Math.}}.Math..Math.w\left(p_i\right)}& \left(8\right)\end{array}$

[0127] The algorithm flow is shown in FIG. 10.

Embodiment 3

[0128] The specific steps of performing ultraviolet camera calibration and solving internal parameters in a system with an enhanced close-distance vessel navigation capability according to the present invention will be described below by way of examples.

[0129] There are many methods for calibrating the camera and algorithms for solving the internal parameters. Here, a conventional calibration technology or Zhenyou Zhang's calibration algorithm is preferably selected. In the Zhenyou Zhang's calibration algorithm, a checkerboard-shaped calibration template is used, and connection points of black and white checkers on the calibration template are used as feature points of the calibration target. Calibration targets are placed at different positions, and synchronous acquisition is performed on the camera to obtain internal and external parameters of the camera. This method does not require any expensive instruments, and is excellent in robustness, easy to operate and improved in accuracy in comparison with self-calibration. However, all calibration methods and algorithms for solving internal parameters available to this embodiment shall be included in the present invention.

[0130] FIG. 3 shows a calibration flow, where, in a step 1-1-1, an ultraviolet array is arranged; in a step 1-1-2, geometrical information of the ultraviolet array is measured; in a step 1-2, the ultraviolet array is shot by an ultraviolet receiver; and, the software processing includes a step 2-1 of acquiring image plane coordinates of a specified ultraviolet light source and a step 2-2 of solving the internal parameters of the camera by a calibration algorithm. The specific calibration steps are as follows:

[0131] Step 1-1-1: An ultraviolet array is arranged. The ultraviolet array is a planar and rectangular grid-shaped ultraviolet array. FIG. 4 shows the ultraviolet array and the shooting position. Geometrical features, such as shape and size, of the ultraviolet array are not limited, and are determined according to the algorithm for solving the internal parameters. The ultraviolet array may be a planar pattern or a stereoscopic pattern, and may be of a rectangular structure, a circular structure or in other geometrical shapes.

[0132] Step 1-1-2: Geometrical information of the ultraviolet array is measured, and coordinates c.sub.w={X.sub.1,Y.sub.1,Z.sub.1}, {X.sub.2,Y.sub.2,Z.sub.2} . . . {X.sub.30,Y.sub.30,Z.sub.30} of a specific ultraviolet point in a coordinate system o-xyz are measured. The geometrical information of the ultraviolet array refers to the coordinates of the specific ultraviolet point or an angular point in the world coordinate system.

[0133] Step 1-2: The ultraviolet array is shot by a solar blind ultraviolet imaging module 103. The selected shooting position A should fulfill the following conditions: at different shooting positions, different orientations of the OA are not parallel, and n groups of images are shot, where n should be greater than 3 in this embodiment.

[0134] Step 2-2: The signal processor 104 perform software processing on the shot digital images to obtain image plane coordinate groups ci.sub.1, ci.sub.2, ci.sub.3 . . . ci.sub.n of the specific ultraviolet point, where there are total n groups.

[0135] Step 2-2: c.sub.w and ci.sub.1, ci.sub.2, ci.sub.3 . . . ci.sub.n are processed by the Zhenyou Zhang's calibration algorithm to obtain related photoelectric internal parameters (f.sub.x, f.sub.y, c.sub.x, c.sub.y, k.sub.x, k.sub.y, etc.) of the camera, where f.sub.x and f.sub.y are focal lengths in pixels in the x and y directions, c.sub.x and c.sub.y are reference points in the image plane, and k.sub.x and k.sub.y are radial distortion coefficients in the x and y directions.

[0136] The principle of the Zhenyou Zhang's calibration algorithm used herein is as follows.

[0137] 1) Correspondence Between Angular Points of the Calibration Target and Corresponding Image Points.

[0138] If the plane of the calibration target is assumed as Z.sub.w=0, then:

[00021]$\begin{array}{cc}s\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=A\left[r_1.Math..Math.r_2.Math..Math.r_3.Math..Math.T\right]\left[\begin{array}{c}{X}_w\\ Y_w\\ Z_w\\ 1\end{array}\right]=A\left[r_1.Math..Math.r_2.Math..Math.T\right]\left[\begin{array}{c}{X}_w\\ Y_w\\ 1\end{array}\right]=H\left[\begin{array}{c}{X}_w\\ Y_w\\ 1\end{array}\right]& \left(9\right)\end{array}$

[0139] where A is determined by f.sub.x, f.sub.y, v.sub.0, u.sub.0 and s, i.e., the internal parameters of the camera, and is related to only the internal structure of the camera; and, H is an external parameter of the camera, and directly reflects the position of the camera in the space. (u,v) represents the pixel coordinates in the image coordinate system, and the world coordinate system is (X.sub.w, Y.sub.w, Z.sub.w). S is an amplification factor, where s=−f.sub.x cot θ. f.sub.x=f/μ.sub.x and f.sub.y=f/μ.sub.y, where f is the focal length of the lens. [X.sub.w,Y.sub.w,Z.sub.w,1].sup.T represents the world coordinates of any object point in the space, and [u,v,1].sup.T represents the pixel coordinates of this object point in an imaging point of the camera.

[0140] The translation matrix T=[T.sub.x,T.sub.y,T.sub.z].sup.T is a 4×4 matrix, the rotation matrix R is an 3×3 orthogonal identity matrix, and both the translation matrix T and the rotation matrix R(r1 r2 r3) are external parameters.

[0141] If it is assumed that H=[h.sub.1 h.sub.2 h.sub.3], then:

H=[h.sub.1h.sub.2h.sub.3]=λA[r.sub.1r.sub.2T]  (10)

[0142] where λ is an arbitrary scaling factor, r.sub.1 is orthogonal to r.sub.2, and two constraints for A can be obtained:

[00022]$\begin{array}{cc}\{\begin{array}{c}{h}_1^{.Math.T}.Math.A^{-T}.Math.A^{-1}.Math.h_2=0\\ h_1^{.Math.T}.Math.A^{-T}.Math.A^{-1}.Math.h_1=h_2^{.Math.T}.Math.A^{-T}.Math.A^{-1}.Math.h_2\end{array}& \left(11\right)\end{array}$

[0143] 2) Solution of Parameters

[00023]$\begin{array}{cc}B=A^{-T}.Math.A^{-1}=\left[\begin{array}{ccc}{B}_{11}& B_{12}& B_{13}\\ B_{21}& B_{22}& B_{23}\\ B_{31}& B_{32}& B_{33}\end{array}\right]=\hspace{1em}[.Math.\begin{array}{ccc}\frac{1}{f_x^{.Math.2}}& \frac{-s}{f_x^{.Math.2}.Math.f_y}& \frac{v_0.Math.s-u_0.Math.f_y}{f_x^{.Math.2}.Math.f_y}\\ \frac{-s}{f_x^{.Math.2}.Math.f_y}& \frac{s^2}{f_x^{.Math.2}.Math.f_y}+\frac{1}{f_y^{.Math.2}}& -\frac{s\left(v_0.Math.s-u_0.Math.f_y\right)}{f_x^{.Math.2}.Math.f_y}-\frac{v_0}{f_y^2}\\ \frac{v_0.Math.s-u_0.Math.f_y}{f_x^{.Math.2}.Math.f_y}& -\frac{s\left(v_0.Math.s-u_0.Math.f_y\right)}{f_x^{.Math.2}.Math.f_y}-\frac{v_0}{f_y^2}& \frac{{\left(v_0.Math.s-u_0.Math.f_y\right)}^2}{f_x^{.Math.2}.Math.f_y}+\frac{v_0^{.Math.2}}{f_y^2}+1\end{array}]& \left(12\right)\end{array}$

[0144] It can be seen from the formula that B is a positive definite symmetric matrix, which is defined as follows:

b=[B.sub.11B.sub.12B.sub.22B.sub.13B.sub.23B.sub.33].sup.T  (13)

[0145] If it is assumed that the i.sup.th column of H is h.sub.i, then:

h.sub.i.sup.TBh.sub.j=v.sub.ij.sup.Tb  (14)

and:

v.sub.ij=[h.sub.1ih.sub.1jh.sub.1ih.sub.2j+h.sub.2ih.sub.1jh.sub.2ih.sub.2jh.sub.3ih.sub.1j+h.sub.1ih.sub.3jh.sub.3ih.sub.2j+h.sub.2ih.sub.3jh.sub.3ih.sub.3j].sup.T  (15)

hence:

[00024]$\begin{array}{cc}\left[\begin{array}{c}{v}_{12}^{.Math.T}\\ {\left(v_{11}-v_{22}\right)}^T\end{array}\right].Math.b=0& \left(16\right)\end{array}$
That is:

Vb=0  (17)

[0146] where V is a 2 n×6 matrix; and, when n>2, b has a unique solution, that is, at least three pictures needs to be collected. The internal parameters are decomposed by Cholesky decomposition:

[00025]$\begin{array}{cc}\{\begin{array}{c}{v}_0=-\frac{B_{12}.Math.B_{13}-B_{11}.Math.B_{23}}{B_{11}.Math.B_{22}-B_{12}^2}\\ \lambda =B_{33}-\left[B_{13}^{.Math.2}+\frac{v_0\left(B_{12}.Math.B_{13}-B_{11}.Math.B_{23}\right)}{B_{11}}\right]\\ f_x=\sqrt{\frac{\lambda }{B_{11}}}\\ f_y=\sqrt{\frac{\lambda .Math..Math.B_{11}}{B_{11}.Math.B_{22}-B_{12}^{.Math.2}}}\\ s=\frac{-B_{12}.Math.f_x^{.Math.2}.Math.f_y}{\lambda }\\ u_0=\frac{{\mathrm{sv}}_0}{f_y}-\frac{B_{13}.Math.f_x^2}{\lambda }\end{array}& \left(18\right)\end{array}$

[0147] Thus, the external parameters are solved:

[00026]$\begin{array}{cc}\hspace{1em}\{\begin{array}{c}{r}_1=\lambda .Math..Math.A^{-1}.Math.h_1\\ r_2=\lambda .Math..Math.A^{-1}.Math.h_2\\ r_3=r_1\times r_2\\ T=\lambda .Math..Math.A^{-1}.Math.h_3\end{array}& \left(19\right)\end{array}$

[0148] 3) Non-Linear Optimization

[0149] Parameter optimization is performed according to a maximum-likelihood criterion, and the target function is as follows:

[00027]$\begin{array}{cc}\underset{i=1}{\overset{n}{.Math.}}.Math.\underset{j=1}{\overset{m}{.Math.}}.Math.•.Math..Math.m_{\mathrm{ij}}-\stackrel{\_}{m}\left(A,R_i,T_i,M_j\right).Math.{•}^2& \left(20\right)\end{array}$

[0150] where m is a projection of a point M.sub.j, and may be solved by an LM optimization algorithm during the optimization.

[0151] With the method and device of the present invention, position information of a vessel relative to a berth is determined by a solar blind ultraviolet imaging method; meanwhile, an attitude angle of the vessel relative to the berth is determined by a differential GPS method. Thus, the vessel can be berthed safely when getting close to the shore at low visibility.