Bi-CGSTAB and SL0 algorithm-based MIMO radar target parameter estimation method

A technology for parameter estimation and radar target, which is applied in the field of MIMO radar target parameter estimation to avoid failure, save solution time, and speed up reconstruction.

Active Publication Date: 2017-05-10
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0010] Purpose of the invention: the present invention aims at the problem that the perceptual ill-conditioned matrix of MIMO radar is a perceptual ill-conditioned matr

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  • Bi-CGSTAB and SL0 algorithm-based MIMO radar target parameter estimation method
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  • Bi-CGSTAB and SL0 algorithm-based MIMO radar target parameter estimation method

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Embodiment Construction

[0042] Below in conjunction with embodiment the present invention will be further described.

[0043] A method for estimating MIMO radar target parameters based on Bi-CGSTAB and SL0 algorithm, the method is obtained by off-line calculation matrix in SL0 algorithm Instead of the pseudo-inverse A of the perceptually ill-conditioned matrix A * (AA * ) -1 ,in(·) * Represents the conjugate transpose operation of the matrix, and then uses the improved SL0 algorithm to process the received signal y of the MIMO radar. The specific operation steps include:

[0044] Step 1, initialize data:

[0045] (1a) Load the pseudo-inverse substitution matrix Will As the initial value of the algorithm operation, set the number of loops L and the step size μ in the initial value, where L, μ>0;

[0046] (1b) Set the attenuation factor ρ, 0 and the final value of the shape parameter σ J , where D is the total number of range-angle-Doppler units divided in the target detection scene;

[004...

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Abstract

The invention discloses a Bi-CGSTAB and SL0 algorithm-based MIMO radar target parameter estimation method and belongs to the MIMO radar target estimation technical field. According to the method, a pseudo-inverse substitution matrix which is represented by a character described in the descriptions of the invention, which is obtained by off-line calculation in an SL0 algorithm, is adopted to replace the pseudo-inverse A<*>(AA<*>)<-1> of a sensing ill-conditioned matrix A, wherein (.)<*> indicates the conjugate transpose operation of the matrix, an improved SL0 algorithm is adapted to process the received signals y of an MIMO radar. Thus, the failure of the SL0 algorithm caused by the ill condition of the sensing ill-conditioned matrix of that the MIMO radar can be avoided, and the robustness of the SL0 algorithm can be improved, so that the SL0 algorithm can have high reconstruction precision; the pseudo-inverse substitution matrix of the sensing ill-conditioned matrix of the MIMO radar is calculated offline and is stored; and when the SL0 algorithm is utilized to estimate the target parameters of the MIMO radar, the value of the pseudo-inverse matrix can be directly called, and therefore, the solution time of an ill-conditioned linear equation set is saved, the reconstruction speed of sparse target signals is accelerated, and the real-time performance of the target parameter estimation of the MIMO radar is improved.

Description

technical field [0001] The invention belongs to the technical field of MIMO radar target parameter estimation, in particular to a MIMO radar target parameter estimation method based on Bi-CGSTAB and SL0 algorithms. Background technique [0002] Multiple-input multiple-output MIMO radar is a new radar system developed on the basis of phased array radar. The radar uses the antenna array with the same structure as the phased array radar, but uses waveform diversity technology to expand its virtual array aperture, thereby improving the resolution of target parameters and the maximum number of resolvable targets, and can obtain better target detection Performance and parameter estimation performance. [0003] In recent years, compressive sensing CS is a research hotspot in the field of signal processing and optimization. It solves the problem based on l q The optimization problem of (0≤q≤1) norm minimization can recover sparse signals with high probability from a small number o...

Claims

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Application Information

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IPC IPC(8): G01S7/41
CPCG01S7/411
Inventor 陈金立李家强朱艳萍
Owner NANJING UNIV OF INFORMATION SCI & TECH
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