METHOD FOR ESTIMATING NOISE IN A RADAR SENSOR

Abstract

A method for estimating noise in a radar sensor, which generates a digital spectrum which indicates a received signal strength as a function of at least one discrete locating parameter, and on this spectrum a CFAR detection is carried out to decide whether an examined cell in the locating space contains a genuine radar target or just noise and a determination of a noise level is also carried out on the basis of the signal strengths in a selection of neighboring cells in the vicinity of the examined cell. The CFAR detection precedes the determination of the noise level and cells identified in the CFAR detection as target cells are excluded from the selection of the neighboring cells.

Claims

1. A method for estimating noise in a radar sensor, which generates a digital spectrum which indicates a received signal strength as a function of at least one discrete locating parameter, the method comprising: carrying out, on the spectrum, a detection to decide whether an examined cell in a locating space contains a genuine radar target or just noise; and determining a noise level based on the signal strengths in a selection of neighboring cells in the vicinity of the examined cell; wherein the detection precedes the determination of the noise level, and cells identified in the detection as target cells are excluded from the selection of the neighboring cells.

2. The method as recited in claim 1, wherein the detection is a CFAR detection.

3. The method as recited in claim 1, wherein, in the determination of the noise level, the signal strengths of the excluded neighboring cells are each replaced with an existing estimated noise value for a cell in a vicinity.

4. The method as recited in claim 1, wherein, in the determination of the noise level, a magnitude of a number of cells in the selection of the neighboring cells is reduced in accordance with a number of excluded neighboring cells.

5. The method as recited in claim 1, wherein, in the determination of the noise level, the signal strengths of the excluded neighboring cells are each replaced with signal values for nearby cells from an enlarged neighborhood.

6. The method as recited in claim 1, wherein the locating space is at least two-dimensional.

7. The method as recited in claim 2, wherein the CFAR detection is carried out in accordance with an OS-CFAR algorithm.

8. The method as recited in claim 7, in which the CFAR detection is carried out in accordance with a rank-only OS-CFAR algorithm.

9. The method as recited in claim 1, wherein, for the determination of the noise level, a window that has a size of N cells of the locating space is shifted over a matrix of the cells of the locating space and, at each position of the window, a cell contained in the window is the examined cell and remaining cells are the neighboring cells.

10. The method as recited in claim 9, wherein the window is one-dimensional.

11. The method as recited in claim 9, wherein the window is one-dimensional, the examined cell is located at one end of the window and, the window is moved over the cell matrix such that the end at which the examined cell is located traverses each cell of the cell matrix before any other part of the window.

12. The method as recited in claim 9, wherein the window is a multi-dimensional window, the examined call is located in a corner of the window, and the window is moved over the cell matrix such that the corner at which the examined cell is located traverses each cell of the cell matrix before any other part of the window.

13. The method as recited in claim 9, wherein estimated values for the noise level of successive cells are calculated iteratively.

14. The method as recited in claim 9, wherein N is a power of two.

15. The method as recited in claim 9, wherein the window size N varies depending on a current position of the window.

16. The method as recited in claim 9, wherein individual cells are hidden within the window such that they do not contribute to the noise estimation.

17. A radar system, comprising: a radar sensor; and an electronic evaluation system; wherein the radar sensor is configured to generate a digital spectrum which indicates a received signal strength as a function of at least one discrete locating parameter; and wherein the electronic evaluation system is configured to: carry out, on the spectrum, a detection to decide whether an examined cell in a locating space contains a genuine radar target or just noise; and determine a noise level based on the signal strengths in a selection of neighboring cells in the vicinity of the examined cell; wherein the detection precedes the determination of the noise level, and cells identified in the detection as target cells are excluded from the selection of the neighboring cells.

18. The radar system as recited in claim 17, wherein the evaluation system includes a noise estimator, which includes a FIFO memory having N memory cells and a bit shifter, where N is a power of two.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] FIG. 1 is a block diagram of a radar system in which a method according to an example embodiment of the present invention is implemented.

[0029] FIG. 2 is a circuit diagram of an implementation of a rank-only OS-CFAR detector, according to an example embodiment of the present invention.

[0030] FIG. 3 shows two different states of a sliding window, according to an example embodiment of the present invention.

[0031] FIG. 4 is a circuit diagram of an implementation of a noise estimator, according to an example embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0032] FIG. 1 shows a block diagram of a radar system for a motor vehicle, which radar system comprises a radar sensor 10 and an electronic evaluation system 12. The radar sensor 10, for example a frequency-modulated continuous-wave (FMCW) radar, converts the received analog radar signals into digital signals and forms from these, using a fast Fourier transform, a discrete two-dimensional spectrum 14 in which one dimension represents the distance d from a located object and the other dimension represents the radial relative velocity v of the object. If an object having the distance d and the relative velocity v is located, this is shown in the spectrum 14 as a local maximum of the signal strength at the point (d, v) in the spectrum. The locating space covered by the distance and velocity dimensions is divided into a number of cells 16, which each correspond to a specific distance range and a specific velocity range and together form an n×m matrix. Each cell 16 is assigned a spectral value a, which indicates the signal strength in the relevant cell. For example, the spectral value a is a complex amplitude which contains both amplitude and phase information.

[0033] The evaluation system 12 also comprises a CFAR and noise detection unit 18, which is shown in the form of a separate block in FIG. 1 and has to fulfill two interrelated objectives. A first objective is to make a decision, for each of the cells 16 in the spectrum, as to whether the cell contains a radar target or whether the signal received for this cell merely constitutes noise. In the former case the cell is referred to as a target cell 20, and in the latter case it is referred to as a noise cell 22. The second objective is to estimate a local noise level P.sub.R for each cell 16.

[0034] The decision as to whether or not a given cell is a target cell provides a binary detection result D, i.e., a variable which has the value 1 when the cell is a target cell and has the value 0 when it is a noise cell. In principle, the detection result D is obtained by calculating the squared amplitude |a|.sup.2 in a quadratic module 24 from the complex amplitude a in the cell to be examined and then comparing this squared amplitude with a suitable threshold value. This means that a cell is only classified as a target cell 20 if the squared amplitude is above a threshold value selected in light of the local noise level P.sub.R such that the threshold value is only exceeded if the signal strength is markedly above the noise level. Since the local noise level can be the reason for fluctuations in time and space, the estimated values for the noise level and the threshold values derived therefrom need to be dynamically adjusted during operation of the radar system.

[0035] In the method according to the present invention, however, the squared amplitude is first supplied to a CFAR detector 26, which provides the detection result D for each cell. This detection result D is transmitted to downstream instances of the evaluation system 12, but also to a noise estimator 28, which uses this detection result to estimate the noise level P.sub.R on the basis of the squared amplitude. The noise level thus obtained is then transmitted to downstream instances of the evaluation system 12 and can, for example, be used to assess the quality of the locating result of the radar sensor and/or to update, in a subsequent measurement cycle, the threshold values used in the CFAR detector 26. The complex amplitudes a from the spectrum 14 are also transmitted in parallel therewith directly to the downstream instances of the evaluation system 12, where they can be used together with corresponding amplitudes for other receiving channels for angle estimation of the located targets.

[0036] FIG. 2 shows a possible implementation of the CFAR detector 26 as a rank-only OS-CFAR. The input data are the squared amplitudes of the spectral values from the spectrum 14, of which a section of a row of the cell matrix is shown in FIG. 2. In the example shown, a one-dimensional window 30, which surrounds a specific number of neighboring cells, is shifted over the cell matrix of the spectrum 14 such that each cell 16 of the spectrum in succession is granted the status of an “examined cell” 16a located in the center of the window 30. The examined cell 16a is flanked by window cells 16b of which the spectral values feed into the decision as to whether the examined cell 16a is a target cell or a noise cell. In the example shown, the window additionally still has a number of protective cells 16c which are symmetrical to the examined cell 16a and of which the spectral values are not evaluated. In the case of expansive objects extending over a plurality of cells, this is intended to prevent the cells that neighbor the examined cell 16a and likewise have a high signal strength if the examined cell is a target cell from being mistakenly interpreted as noise background and distorting the detection result. In each position of the window 30 on the cell matrix, the spectral value of the examined cell 16a is then multiplied by a suitable scaling factor using a multiplier 32, and the spectral value scaled in this way is compared with the (unscaled) spectral values of the window cells 16b in comparators 34. In a summer 36, the binary comparison results over all the window cells are added together. In a further comparator 38, the resulting sum is compared with a so-called rank k, which, in practice, can have a predetermined value, for example k=3N/4, where N is the number of window cells. When the sum is greater than k, this means that the spectral value of the examined cell 16a is greater than the spectral value of most of the window cells, and therefore that the signal strength in the examined cell 16a markedly contrasts with the noise background provided by the signal strengths of the window cells. In this case, it is therefore decided that the examined cell 16a is a target cell, and the detection result D is given the value 1. Otherwise, the detection result D is given the value 0, which means that the examined cell 16a is classified as a noise cell.

[0037] The level of the constant false alarm rate can be parameterized according to the desired application by way of the scaling factor for the multiplier 32, as well as the rank k and the window size N.

[0038] FIG. 3 shows an example of a window 40 that is used in the noise estimator 28 for the noise estimation and does not need to be identical to the window 30 from FIG. 2. In the example shown, the window 40 is also a one-dimensional window, although the examined cell 16a is not in the center but instead is located at an end of the window. An index i indicates the row index of the cell matrix of the spectrum 14. The window 40 contains N cells having the indices i−N+1, i−N+2, . . . , i−1, i. When the index i is increased stepwise by the increment 1, this means that the window 40 is shifted over the cell matrix in the row direction, and specifically in the direction of the increasing row indices, meaning that the examined cell 16a forms the leading end of the window.

[0039] In general, the spectral values of the cells in the window 40 form the basis for the estimation of the local noise level. In the example shown, however, the window 40 not only contains noise cells, but also target cells 16d, which are hatched here. In conventional methods, at the time of the noise estimation it is not yet known whether or not the window 40 contains target cells, and therefore all the cells have to be considered to be noise cells. In the method according to the present invention, however, the detection result D is already available for the cells currently located in the window 40, and therefore the target cells 16d can be identified on the basis of this detection result. In FIG. 3, for example, the cell in the position i−2 is a target cell, while the cell in the position i−3 is a noise cell. The examined cell 16a is the cell for which the noise estimation is currently being performed. Since the window 40 moves from right to left over the cell matrix as the index i increases, the noise estimation has already taken place for the cells in the positions i−2, i−3, etc. So that the high signal levels in the target cells 16d do not feed into the noise estimation, the spectral value (the square of the absolute value) of the target cells is replaced with the estimated noise value for the closest noise cell in each case. In FIG. 3, this replacement is indicated by the different hatching in the cells in the lower window, which represents the state after the replacement.

[0040] The actual noise estimation can then be performed by forming the average of the spectral values over all the cells of the window once the above-described replacement has taken place. When P.sub.R(i) is the estimated value to be determined for the cell 16a currently being examined, F(j) is the (optionally replaced) spectral value of the cell having the index j, and N is the number of cells of the window, the following applies:

[00001]$\begin{array}{cc}{P}_R\left(i\right)=\frac{1}{N}{.Math.}_{j=i-N+1}^{i}F\left(j\right)& \left(1\right)\end{array}$

[0041] The above-described replacement of the spectral values and the averaging can, however, be performed efficiently in a considerably lower number of computing operations when the calculation is performed iteratively:

[0042] when D(i)=0:

P.sub.R(i)=P.sub.R(i−1)+(1/N)(P(i)−F(i−N)  (2)

when D(i)=1:

P.sub.R(i)=P.sub.R(i−1)+(1/N)(P.sub.R(i—l)−F(i−N)  (3)

[0043] where P(i) is the spectral value in the cell having the index i.

[0044] A possible hardware implementation of this iterative estimation process is shown in the form of a block diagram in FIG. 4. The noise estimator 28 shown in this figure comprises a shift register 42 having N memory locations, where N is a power of two: N=2.sup.P. The spectral values P(i) (squared amplitudes) of the cells 16 are supplied in succession to a multiplexer 44 at the input of the shift register 42, together with the detection result D (0 or 1) for the relevant cell. Depending on this detection result, a decision is made as to whether formula (2) or formula (3) should be applied.

[0045] An adder 46 and a subtractor 48 form the difference between the first and the last memory location in the shift register 42, and delayers 50 control the transition from the index i to the previous index i−1. The division by N in accordance with formula (2) or formula (3) is performed in a very efficient manner with the aid of a simple bit shifter 52, which shifts the relevant binary value by p (base-2 logarithm of N). In this way, the noise estimator 28 delivers the associated estimated value P.sub.R(i) for each of the successive values of the index i.