METHOD FOR TARGET DETECTION IN A DDM RADAR SENSOR

Abstract

A method for target detection in a DDM radar sensor having a number N.sub.TX of transmission antennas Tx and a number N.sub.Slots>N.sub.TX of DDM slots, the assignment of which to transmission antennas is determined by a DDM code. The method includes: calculating a range-Doppler matrix, which is divided in the Doppler dimension into N.sub.Slots ambiguity zones; converting the range-Doppler matrix into a three-dimensional range-Doppler ambiguity matrix by converting the range-Doppler matrix into a three-dimensional range-Doppler ambiguity matrix by non-coherent cyclic discrete convolution with the DDM code; and target detection by cell-by-cell comparison of the range-Doppler ambiguity matrix (34) with a threshold matrix.

Claims

1. A method for target detection in a DDM radar sensor having a number N.sub.TX of transmission antennas Tx and a number N.sub.Slots>N.sub.TX of DDM slots, an assignment of which to the transmission antennas is determined by a DDM code, the method comprising the following steps: calculating a range-Doppler matrix, which is divided in a Doppler dimension into N.sub.Slots ambiguity zones; converting the range-Doppler matrix into a three-dimensional range-Doppler ambiguity matrix by non-coherent cyclic discrete convolution with the DDM code; and performing target detection by cell-by-cell comparison of the range-Doppler ambiguity matrix with a threshold matrix.

2. The method according to claim 1, in which N.sub.Slots2 N.sub.Tx.

3. The method according to claim 1, wherein the threshold matrix is derived from a noise floor matrix, which is calculated based on the range-Doppler matrix by averaging, in each range-Doppler bin, N.sub.Tx smallest entries of the matrix cells across the ambiguity zones.

4. The method according to claim 1, wherein, for determination of a convolution kernel, the DDM code is selected, which dampens expression of sidelobes in an autocorrelation function.

5. The method according to claim 1, wherein those ambiguity bins in which a certain minimum signal strength is achieved in at least one range-Doppler cell, and in which the target detection is carried out on a RoI matrix that contains only selected ambiguity bins, are selected from the range-Doppler ambiguity matrix.

6. The method according to claim 1, wherein only those matrix cells which are local maxima in a distance dimension and the Doppler dimension are taken into account during target detection.

7. The method according to claim 1, for the target detection in distance and velocity, an angle estimation is performed based on data received in a plurality of reception channels.

8. The method according to claim 1, in which a multi-target estimation algorithm is used to more sharply separate targets with a same ambiguous velocity from one another.

9. The method according to claim 8, wherein the multi-target estimation algorithm is used to determine a plurality of target angles of targets with the same unambiguous velocity.

10. A radar system for target detection, the radar system comprising a DDM radar sensor having a number N.sub.Tx of transmission antennas Tx and a number N.sub.Slots>N.sub.Tx of DDM slots, an assignment of which to the transmission antennas is determined by a DDM code, the radar system being configured to: calculate a range-Doppler matrix, which is divided in a Doppler dimension into N.sub.Slots ambiguity zones; convert the range-Doppler matrix into a three-dimensional range-Doppler ambiguity matrix by non-coherent cyclic discrete convolution with the DDM code; and perform target detection by cell-by-cell comparison of the range-Doppler ambiguity matrix with a threshold matrix.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG. 1 is a block diagram of a transmission part of a radar sensor with which the method according to an example embodiment of the present invention can be carried out.

[0025] FIG. 2 is a frequency/time diagram for a transmission signal.

[0026] FIG. 3 is a diagrammatic representation of phase progressions in a DDM radar.

[0027] FIG. 4 is a block diagram of an evaluation device configured for the method according to an example embodiment the present invention.

[0028] FIG. 5 shows an example of a DDM code.

[0029] FIG. 6 shows the autocorrelation function of the DDM code according to FIG. 5.

[0030] FIG. 7 is a diagram for illustrating the operation of a VAR solver shown in FIG. 4, which converts a range-Doppler matrix into a range-Doppler ambiguity matrix (RDVAR).

[0031] FIG. 8 is a diagram for illustrating a reduction of the RDVAR matrix to a region of interest.

[0032] FIG. 9 is a diagram for illustrating a function of the VAR solver, which converts the range-Doppler matrix into a noise floor matrix.

[0033] FIG. 10 is a flow chart that shows the steps of the method according to an example embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0034] FIG. 1 shows schematically the transmission part of a radar sensor that comprises four transmission antennas Txi (0i3). Although not shown here, the radar sensor will usually be configured as a MIMO radar, which can be used to make angle estimations according to the MIMO (Multiple-Input Multiple-Output) principle. A signal generator 10 generates a transmission signal that has the frequency profile shown in FIG. 2. This signal consists of sequences, repeatedly transmitted at regular time intervals, of so-called chirps 12, in which the frequency f increases linearly as a function of time t. This transmission signal is supplied to the transmission antennas Txi via a phase shifter 14, which rotates the phase of the signal by a certain angle for each chirp. If p (0p3) is an index that counts the chirps 12 in the sequence, and i is the index that counts the transmission antennas, then the complex amplitude of the transmission signal in the chirp p in the antenna i can, for example, have the following form:

[00003]$A\left(t\right)=a\exp \left(j\left(t\right)t+{}_{p,i}\right)$

[0035] Here, a is a real constant, j is the imaginary unit, (t) is the (circular) frequency dependent on time t, and .sub.p,i is a phase angle. The phase shifters 14 can be controlled so that they set the phase angle .sub.p,i, for example according to the following formula:

[00004]${}_{p,i}=\left(2/N_{\mathrm{Tx}}\right)*p*i$

[0036] In FIG. 3, the phase angles .sub.p,i of the signals for a conventional DDM radar with four antennas are shown. For antenna i=0, .sub.p,0 is equal to zero for all four chirps. For antenna i=1, the phase angle is zero for the first chirp and is then increased by 90 for each subsequent chirp. For antenna i=2, the phase angle is increased by 180 from chirp to chirp, starting with the phase angle .sub.0,2=0. For antenna i=3, the phase angle is increased by 270 from chirp to chirp, likewise starting with .sub.0,3=.sup.0 for the first chirp.

[0037] FIG. 4 is a block diagram of components of an evaluation system of the radar sensor. In a first evaluation stage 16, a range-Doppler matrix is calculated in each measurement cycle and in each reception channel by carrying out a two-dimensional Fourier transformation on a baseband signal obtained by down-mixing the reception signal. Each cell in the range-Doppler matrix is part of a distance bin (row of the matrix) and a Doppler bin (column of the matrix), and the signal value of the cell indicates the strength of the signal obtained from a radar target whose distance and radial relative velocity lie in the distance bin and the Doppler bin. A so-called VAR solver 18 converts the range-Doppler matrix into a range-Doppler VAR matrix, which is temporarily stored in a memory 20. The abbreviation VAR stands for Velocity Ambiguity Range and refers to an ambiguity range composed of a plurality of adjacent columns in the range-Doppler matrix, as will be explained in more detail later. The German term for range-Doppler VAR matrix is therefore Range-Doppler-Mehrdeutigkeits-Matrix [range-Doppler ambiguity matrix in English].

[0038] Furthermore, the VAR solver 18 converts the range-Doppler matrix into a so-called noise floor matrix, which is stored in a further memory 22. This matrix indicates, in a sense, the noise background from which the signals stored in the range-Doppler VAR matrix are to stand out.

[0039] In a detection stage 24, radar targets are detected by comparing the cell contents of the range-Doppler VAR matrix with a threshold matrix, which can be the noise floor matrix itself or a threshold matrix that has been derived from the noise floor matrix using conventional methods, for example the CFAR (Constant False Alarm Rate) method. Alternatively, the threshold matrix could also be ascertained directly from the range-Doppler matrix using conventional methods. Prior to the actual detection, a coherent summation (e.g., with the aid of an angle FFT) or non-coherent summation of the range-Doppler spectra of the individual reception channels is performed prior to detection.

[0040] The result of the detection step is a detection list that is stored in a further memory 26 and contains the location signals of all localized radar targets.

[0041] The detection list is then supplied to an angle estimation stage 28, where an angle estimation is performed using conventional methods in order to obtain a point cloud that characterizes the current environment of the radar sensor.

[0042] Each of the phase shifters 14 shown in FIG. 1 defines a DDM slot that is characterized by the phase progression in the transmission channel in question. The smallest non-zero phase progression determines the number N.sub.Slot of the total available slots according to the formula

[00005]$N_{\mathrm{Slot}}=\frac{360}{{}_{1,1}}.$

In the example shown in FIG. 3, .sub.1,1=90, and accordingly, N.sub.Slot=4. In this example, N.sub.Slot thus corresponds to the number N.sub.TX of simultaneously active transmission antennas.

[0043] However, in order to make the resolution of velocity ambiguities possible, phase progressions are used in practice where N.sub.Slot is greater than N.sub.TX, preferably at least twice as large. In this case, some slots remain unoccupied because there are not enough transmission antennas. The slots occupied by transmission antennas are specified by the so-called DDM code. This is a binary vector with the components d.sub.s, (s=0, . . . , (N.sub.Slot1)). In FIG. 5, the DDM code

[00006]$d=\left(1,1,0,1,0,0,0,1,1,0,0,0,0,1,0,0\right)$

is graphically shown as an example. In this example, N.sub.Slots=16 and N.sub.TX=6, so that only six slots are occupied by antennas.

[0044] FIG. 6 shows the corresponding autocorrelation function (m). The variable m is an index that counts ambiguity zones (also called spectrum slots) in the range-Doppler matrix. If the DDM code, whose components are designated here by the index i, is considered as a function d(s), then f(m) is the result of a convolution of the function d(s) with a kernel which, in turn, is defined by the DDM code d, but is periodically continued using a modulo function. The index of the second d in the formula in FIG. 6 has the meaning: (s+m)modulo N.sub.Slots.

[0045] At m0, the correlation function (m) has a maximum by definition. The sum in the specified formula is then simply equal to the number N.sub.TX of the non-zero components of the DDM code, so that normalization yields the value 1 or 0 dB. However, the correlation function exhibits more or less pronounced sidelobes at non-zero values of m. For example, for m=6:

[00007]$\begin{array}{c}{\mathrm{NT}}_{x}f\left(6\right)=d_0+d_6+d_1+d_7+d_3+d_9+d_7+d_{13}+d_8+d_{14}+d_{13}+d_3\\ =0+1+0+1+0+1\\ =4\end{array}$

[0046] The component d.sub.3 in the last summand results from the modulo function.

[0047] An example of a range-Doppler matrix is shown graphically in FIG. 7. In this simplified example, the matrix has eight cells or bins in the range dimension r, and in the Doppler dimension v the number of bins is N.sub.large. If the transmission signals were not phase-modulated, the range-Doppler matrix in the Doppler dimension would cover the entire range of velocities that could theoretically occur (for example, in a radar for cars, 150 km/h to +150 km/h), and the sampling frequency would be so high that the distance measurement within this range would be unambiguous. However, the phase modulation induces N.sub.Slots different synthetic Doppler shifts, with the result that even at a relative velocity v=0, the Doppler spectrum would not just obtain a single peak, but rather one peak for each DDM slot. Therefore, the range-Doppler matrix is divided in the Doppler dimension into a number N.sub.Slots of ambiguity zones 32, which are also referred to as spectrum slots and are counted here by the index m, which also appears in FIG. 6. The number of Doppler cells or v bins within each ambiguity zone is therefore N.sub.Small=N.sub.Large/N.sub.Slots. Each matrix cell contains an entry x.sub.RD that indicates the signal strength of a radar target, whose distance and velocity are determined by the position of the cell within the matrix.

[0048] One function of the VAR solver 18 is to convert, according to the formula given in FIG. 7, the two-dimensional range-Doppler matrix 30 into a three-dimensional range-Doppler VAR matrix 34, which is graphically shown on the right-hand side in FIG. 7. The third dimension here is the dimension in which the ambiguity index m varies. In the velocity dimension v.sub.amb, this matrix has only N.sub.Small cells. The index amb in V.sub.amb indicates that the velocity in this dimension is ambiguous, since the true velocity of the target could lie in any of the N.sub.Slots bins in the ambiguity dimension m. The relationship between the velocity v in the matrix 30 and the velocity v.sub.amb in the matrix 34 is given by:

[00008]$v=v_{\mathrm{amb}}+m{v}_{\mathrm{small}}$

where v.sub.small is the width of a single ambiguity zone 32.

[0049] The entry x.sub.RDVAR in each matrix cell of the three-dimensional matrix 34 is a function of the distance r, the velocity v.sub.amb and the ambiguity index m. The entries x.sub.RD in the two-dimensional matrix 30 can also be regarded as functions of these three variables, but in the formula in FIG. 7 the index m is replaced by the summation index s.

[0050] According to the formula indicated in FIG. 7, the VAR solver 18 carries out a non-coherent discrete convolution on the functions x.sub.RD (r, v.sub.amb, s) with the kernel d (mod(s+m, M.sub.Slots)) from FIG. 6.

[0051] Through this convolution, the different ambiguity zones 32 are correlated with one another, so that in the three-dimensional matrix 34 a maximum is obtained where the correlation is greatest. As a result, this not only resolves the velocity ambiguities, but also results in an integration gain due to the summation over the index s.

[0052] In the three-dimensional range-Doppler VAR matrix 34 in FIG. 7, the number of VAR bins in the VAR dimension m is N.sub.Slots. In practice, however, it will often be the case that none of these VAR bins contains a significant signal at any point, i.e., for any combination of r and v.sub.amb. In order to reduce the storage requirements and the computational effort, it is therefore useful to thin out the matrix by limiting oneself to those bins in which a significant signal is actually present. This is symbolized in FIG. 8 by replacing the matrix 34 with a three-dimensional RoI matrix 36 (range of interest). The number of VAR bins in this matrix is reduced to N.sub.VAR,valid.

[0053] A further function of the VAR correlator 18 is to calculate, based on the two-dimensional range-Doppler matrix 30, a likewise two-dimensional noise floor matrix 38, which, in the velocity dimension v.sub.amb, has only N.sub.small v bins, just like the range-Doppler VAR matrix 34, as shown in FIG. 9. The entries m in the noise floor matrix 38 indicate a noise background for each cell and are calculated according to the formula specified in FIG. 9. The function mink is used to identify the cells in the range-Doppler matrix 30 that contain the N.sub.Tx smallest entries, and in the ambiguity dimension s these entries are averaged. Due to the averaging, the integration gain mentioned above is again achieved here.

[0054] For target detection, either the noise floor matrix 38 can be used directly as the threshold matrix or a suitable threshold matrix can be calculated from this matrix using conventional methods, for example the CFAR method.

[0055] Alternatively, the threshold matrix could also be ascertained directly from the range-Doppler matrix using conventional methods.

[0056] In FIG. 10, the steps of the method are summarized in a flow chart. In step S1, a range-Doppler matrix is calculated for each reception channel. In step S2, a range-Doppler VAR matrix (range-Doppler ambiguity matrix) is calculated with the aid of the VAR solver 18. In an optional step S3, the matrix calculated in step S2 is reduced to a RoI matrix 36. In step S4, the noise floor matrix 38 is then calculated with the aid of the VAR solver 18, from which a threshold matrix is then formed in step S5, for example by simply adopting the noise floor matrix as the threshold matrix. In step S6, the target detection is then carried out with the aid of the detection stage 24 in FIG. 4. For this purpose, the entries in the RoI matrix are compared with the entries in the threshold matrix. According to a variant of the method, only those cells are taken into account that represent local maxima in relation to the dimensions of distance and unambiguous velocity. A significant advantage of the method proposed here is that in this way, targets having the same ambiguous velocity v.sub.amb can also be detected in the detection step.

[0057] In step S7, an angle estimation is then carried out using conventional methods. The location signals are processed so that they are comparable with one another with regard to angle estimation.

[0058] In an optional step S8, conventional iterative CLEAN algorithms can then be used to further process the data that were subjected to a convolution operation in step S2, so that targets with the same ambiguous velocity can be better distinguished and characterized. The CLEAN algorithm can be used not only in the ambiguity dimension, but also in the angle dimension in order to determine a plurality of target angles of targets with the same unambiguous velocity.

[0059] The sequence of the proposed iterative range-Doppler CLEAN method can be outlined as follows: After successful estimation of a first ambiguity region and the corresponding target angle(s), the complex location signal (i.e., the complex amplitudes of the transmission and reception channels) of these radar targets is calculated. The location signal is then coherently received by those cells of the N.sub.Slots large input vector of the range-Doppler matrix that are occupied by the radar targets according to their ambiguity range, before a further iteration can begin. In the next iteration, in turn a determination of the ambiguity region is carried out with the aid of the DDM code and the formula from FIG. 7. By previously subtracting the signal components of the already detected targets, a weaker target can now also be detected. The detection process is again carried out according to FIGS. 4, 7, 8 and 9.

[0060] As soon as a termination criterion is fulfilled, e.g., an insufficient amplitude of the remaining location signal, the CLEAN algorithm ends. In the case of high-quality angle estimation, dynamics of over 30 dB or even over 40 dB can be achieved.

[0061] Alternatively, another multi-target estimation algorithm such as RELAX can be used instead of the CLEAN algorithm.