METHOD FOR SEPARATING RADAR TARGETS OF A RADAR SENSOR

Abstract

A method for the model-based, high-resolution separation of radar targets for a radar sensor, in which the radar sensor initially generates radar data by capturing radar targets by sampling a field of view of the radar sensor. Various model orders are calculated for the number of radar targets with the aid of a grid-based method and are interleaved in one another. A highest model order is specified, and only the highest model order is calculated, so that the lower model calculations are implicitly produced from the calculation of the highest model order.

Claims

1. A method for separation of radar targets for a radar sensor comprising: generating radar data by capturing radar targets by sampling a field of view of the radar sensor; calculating various model orders for a number of radar targets with a grid-based method, wherein the model orders are interleaved in one another; specifying a highest model order; and calculating the highest model order only, wherein results for the lower model orders are produced as partial results of the calculation for the highest model order.

2. The method according to claim 1, further comprising: providing a grid for the grid-based method, wherein grid points on the grid are regularly distributed; and pre-calculating elements of w as a vector of length.

3. The method according to claim 2, further comprising using one of a discrete Fourier transform matrix or a fast Fourier transform for the pre-calculating.

4. The method according to claim 2, further comprising utilizing the model order to indicate the number of radar targets.

5. The method according to claim 2, further comprising utilizing the calculation of a model order as a cost function, so that a cost function exists for each model order.

6. The method according to claim 5, further comprising establishing a maximum of the grid points with the aid of the cost function.

7. The method according to claim 6, further comprising carrying out a post-processing of the maximum by interpolation.

8. The method according to claim 7, further comprising selecting the model order with the interpolated maxima.

9. The method according to claim 1, further comprising storing pre-calculated values in a look-up table.

10. The method according to claim 1, further comprising storing a simplified function for cyclical vectors in a look-up table.

11. A radar sensor for detecting objects for a motor vehicle, which can capture objects in its field of view with the aid of radar targets, wherein the captured radar targets are separated by: calculating various model orders for a number of the radar targets with a grid-based method, wherein the model orders are interleaved in one another; specifying a highest model order; and calculating the highest model order only, wherein results for the lower model orders are produced as partial results of the calculation for the highest model order.

12. The radar sensor according to claim 11, wherein grid points of a grid for the grid-based method are regularly distributed, and wherein elements of w are pre-calculated as a vector of length.

13. The radar sensor according to claim 12, wherein the pre-calculation uses a discrete Fourier transform matrix or a fast Fourier transform.

14. The radar sensor according to claim 12, wherein the model order is utilized to indicate the number of radar targets.

15. The radar sensor according to claim 12, wherein the calculation of a model order is utilized as a cost function, so that a cost function exists for each model order.

16. The radar sensor according to claim 15, wherein a maximum of the grid points is established with the cost function.

17. The radar sensor according to claim 16, wherein a post-processing of the maximum is carried out by interpolation.

18. The radar sensor according to claim 17, wherein the model order is selected with the aid of the interpolated maxima.

19. The radar sensor according to claim 11, wherein pre-calculated values are stored in a look-up table.

20. The radar sensor according to claim 11, wherein a simplified function for cyclical vectors is stored in a look-up table.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present disclosure will become more fully understood from the detailed description and the accompanying drawings, wherein:

[0017] FIG. 1 shows an exemplary configuration of an algorithm for a method for separation of radar targets for a radar sensor.

DETAILED DESCRIPTION

[0018] The invention is described in more detail below with the aid of expedient embodiments. In the case of the method, the extreme point searches for subordinate model orders can be produced as auxiliary calculations of the calculation for the highest model order with the aid of a grid-based approach. This can save computing time to an extent. The calculation of the optimal solution of the parameter {circumflex over ()}, based on a grid method or grid-based method, can be simplified for multiple interleaved model orders with the algorithm described, so that fewer calculations are required. In this case, only the highest model order has to be calculated, wherein lower model orders can be produced or can be calculated by means of interim results. To this end, equation (3) can be reformulated, so that

[00005]$\begin{array}{cc}\begin{array}{c}\stackrel{}{}=\min m\left(\right)=\max \left[z^H{A\left(A^{H}A\right)}^{-1}{A}^{H}z\right]\\ =\max \left[w^H{D}^{-1}w\right]=\max \left[{.Math.U^{-H}w.Math.}^2\right]\end{array}& \mathrm{Equation}\left(5\right)\end{array}$$\mathrm{wherein}$$\begin{array}{cc}{U}^{H}U=D\mathrm{and}{A}^{H}z=w& \mathrm{Equation}\left(6\right)\end{array}$

and U denotes the upper triangular matrix of the Cholesky decomposition of A.sup.HA. It should be noted that U.sup.H denotes a lower triangular matrix.

[0019] On the understanding that the solutions for the parameter {circumflex over ()} are established by means of a uniform grid-based method and the models are interleaved in one another, i.e., a lower model order can be represented by a higher one (see Equation (1), wherein z where N=1 is equivalent to z where N=2, if .sub.1=0), only the highest model order has to be calculated with the methodlower model orders are produced, e.g., by means of interim results, i.e., implicitly.

[0020] If the grid points are regularly distributed, that is to say the same sampling points k.sub.0, . . . , k.sub.L1 are used for each element of , the elements of w can be pre-calculated as a correlation function as a vector of the length L:

[00006]$\begin{array}{cc}{w}_L={\left[w_0,w_1,.Math.,w_{L-1}\right]}^T={\left[a\left(k_0\right),.Math.,a\left(k_{L-1}\right)\right]}^{H}z& \mathrm{Equation}\left(7\right)\end{array}$

[0021] In order to calculate m() for a specific grid point , U.sup.Hw has to be calculated, wherein w can be constructed directly from the corresponding elements of w.sub.i. The lower triangular matrix U.sup.H may be structured such that the submatrix U.sub.nn.sup.H comprises the first n lines and columns of U.sup.H and is sufficient to calculate the nth model, that is to say N=n. For example, this produces:

[00007]$\begin{array}{cc}{U}_{33}^{-H}=\left[\begin{array}{ccc}{u}_{11}& 0& 0\\ u_{21}& u_{22}& 0\\ u_{31}& u_{32}& u_{33}\end{array}\right]=\left[\begin{array}{ccc}& & 0\\ & U_{22}^{-H}& 0\\ u_{31}& u_{32}& u_{33}\end{array}\right]& \mathrm{Equation}\left(8\right)\end{array}$

so that the following cost functions m() are produced for the tuple =[k.sub.l, k.sub.m].sup.T where N=2 and the group of three =[k.sub.l, k.sub.m, k.sub.n]T where N=3:

[00008]$\begin{array}{cc}{.Math.{U_{22}^{-H}\left[w_l,w_m\right]}^T.Math.}^2={.Math.{\left[u_{11}{w}_l,u_{21}{w}_l+u_{22}{w}_m\right]}^T.Math.}^2\mathrm{for}N=2& \mathrm{Equation}\left(9\right)\end{array}$

[00009]$\begin{array}{cc}{.Math.{U_{33}^{-H}\left[w_1,w_m,w_n\right]}^T.Math.}^2={.Math.\left[\begin{array}{c}{U_{22}^{-H}\left[w_l,w_m\right]}^T\\ u_{31}{w}_l,u_{32}{w}_m+u_{33}{w}_n\end{array}\right].Math.}^2\mathrm{for}N=3& \mathrm{Equation}\left(10\right)\end{array}$

[0022] A configuration of the algorithm or of the course of the method is depicted in FIG. 1 with a calculation example, wherein a calculation of the correlation function is initially carried out for the incoming radar data or sample data for the sampling points k.sub.i, i=0, . . . , L1 in a first step. Thereafter, the algorithm described above for calculating the cost function is performed, e.g., for N=1, N=2, N=3 and the like. The maximum can subsequently be sought via the cost functions. Furthermore, a post-processing of the maximum can subsequently be carried out, e.g., by means of interpolation, in an optional step, and a decision can be made for the parameter {circumflex over ()} and N, for example via known methods such as, e.g., information criteria.

[0023] Furthermore, the method can be enlisted in the case of all high-resolution model-based methods in the field of radio waves, wherein this concerns radar methods and the localization of objects in particular. The method is particularly computationally efficient if the corresponding values for [a(k.sub.0), . . . , a(k.sub.L1)].sup.H [a(k.sub.0), . . . , a(k.sub.L1)].sup.H and for U.sup.h are pre-calculated externally and saved, e.g., as a look-up table. This can in turn save memory, as the elements of U.sup.H have repetitions. Moreover, in the event that a(k) is cyclically periodic, a(k.sub.i).sup.Ha(k.sub.j)=(k.sub.jk.sub.i) can be depicted by a simplified function, which is only defined by the interval k.sub.jk.sub.i, which makes it easier to calculate the coefficients U.sup.H analytically. Furthermore, this configuration can make it possible to save memory for the look-up table. If a higher resolution of the solution than the given grid points is to be achieved, the cost function of the optimization or of the mean square error m({circumflex over ()}) can also be interpolated via a parabolic approach, which can then be performed again individually for each model order.