Flexible MIMO (Multi-Input and Multi-Output) radar hybrid target DOA (Direction of Arrival) estimation method based on compressed sensing (CS)

A hybrid target, flexible technology, applied in radio wave measurement systems, instruments, etc., can solve the problems of high computational complexity and sparse array MIMO radar structure design, etc.

Active Publication Date: 2018-11-16
AIR FORCE UNIV PLA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The main disadvantages of the existing technology are: 1) The structural design of sparse array MIMO radar based on the joint optimization of degrees of freedom and mutual coupling has not been carried out; 2) The traditional CS algorithm has a high amount of computation when performing sparse array MIMO radar mixed target DOA estimation

Method used

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  • Flexible MIMO (Multi-Input and Multi-Output) radar hybrid target DOA (Direction of Arrival) estimation method based on compressed sensing (CS)
  • Flexible MIMO (Multi-Input and Multi-Output) radar hybrid target DOA (Direction of Arrival) estimation method based on compressed sensing (CS)
  • Flexible MIMO (Multi-Input and Multi-Output) radar hybrid target DOA (Direction of Arrival) estimation method based on compressed sensing (CS)

Examples

Experimental program
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Effect test

Embodiment 1

[0215] Example 1: Establishing a Flexible MIMO Radar Echo Signal Model

[0216] refer to image 3 , is a schematic diagram of the distribution of related MIMO radar virtual array elements of a flexible MIMO radar hybrid target DOA estimation method based on compressed sensing provided by the embodiment of the present invention, assuming that the number of SA-FIS transmitting arrays and receiving arrays is M=4,N =3. Then according to Theorem 1, 1≤α≤5, 1≤β≤7. Combined with the related sparse array MIMO radar structure in Table 2, image 3 The virtual element distribution of each array structure is given, among which, the flexible mutual prime MIMO radar satisfies p=2, SA-FIS satisfies α=5, β=3. from image 3 It can be seen that the nested sub-array MIMO radar transmit or receive array is a dense array, and other array structures are composed of sparse arrays, so the nested sub-array MIMO radar has the highest mutual coupling rate. Specifically, SA-FIS can obtain 35 virtua...

Embodiment 2

[0217] Embodiment 2, improving the operation time of the CS algorithm

[0218] refer to Figure 4 , is a schematic diagram of the operation time of traditional CS and two-step CS algorithm of a flexible MIMO radar mixed target DOA estimation method based on compressed sensing provided by an embodiment of the present invention, wherein the SNR is 10dB, the number of snapshots is 200, and the search range is [ 0°,40°], the search step is 1°, N=3, and the range of M is [2,8]. Suppose α=5, β=3, and the three target directions are θ 1 =10°, θ 2 =20°, θ 3 =30°, where the latter two targets are coherent, and the corresponding coherence coefficients are 0.9exp(j1.1π) and 0.8exp(j0.75π). So, by Figure 4 It can be seen that the two-step CS algorithm has a lower computational load than the traditional CS algorithm.

Embodiment 3

[0219] Embodiment 3, improved CS algorithm mean square error

[0220] refer to Figure 5 with Image 6 , firstly, compare the traditional CS algorithm, l 1 - Estimated performance of SVD and two-step CS algorithms, and CRB provides estimated performance lower bounds. Assume that the target position and coherence coefficient are the same as those in Embodiment 2, M=2, N=3, α=5, β=3, and the search step size is 0.05°. Figure 5 The relationship between RMSE and SNR is given, and the number of snapshots is 200. Image 6 The relationship between RMSE and the number of snapshots is given, and the SNR is 0dB. from Figure 5-6 It can be seen that the improved CS algorithm, l 1 - Both SVD and traditional CS algorithms increase with the increase of SNR and the number of snapshots. Among them, the second step of the improved CS algorithm can improve the estimation performance by modifying the target covariance matrix, so the estimation accuracy is better than the traditional CS a...

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Abstract

The invention discloses a flexible MIMO (Multi-Input and Multi-Output) radar hybrid target DOA (Direction of Arrival) estimation method based on compressed sensing (CS), and relates to the technical field of array signal processing technology. The present invention focuses on the following two issues for the sparse array MIMO radar structure design and hybrid target DOA estimation: (1) designing aflexible MIMO radar structure and defining it as a sparse array with a sparse array with flexible inter-element spacing (SA-FIS); (2) proposing a two-step CS algorithm with reduced complexity to makefull use of the total virtual array elements. By modifying and removing non-diagonal elements in the target covariance matrix, the improved CS algorithm can identify only the diagonal elements therein. Since the conventional CS algorithm needs to estimate all non-zero elements, the present invention can improve estimation performance and reduce complexity by estimating a smaller number of elements.

Description

technical field [0001] The invention relates to the technical field of array signal processing, in particular to a flexible MIMO radar mixed target DOA estimation method based on compressed sensing. Background technique [0002] In order to increase the upper limit of degrees of freedom under the condition of a given number of physical array elements, the research on the expansion of sparse array MIMO radar virtual array elements from the perspective of "joint array" has gradually attracted the attention of the academic community and made great progress. Nested MIMO radar can use O(M) array elements to obtain O(M 2 ) or O(M 3 ) degrees of freedom, but the densely distributed sub-arrays make the mutual coupling ratio relatively large. The larger array element spacing of the coprime MIMO radar further reduces the mutual coupling rate, and O(M+N) array elements can be used to obtain O(MN) degrees of freedom. However, no systematic research on the structural design of sparse ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 师俊朋胡国平周豪张秦冯子昂刘梦波
Owner AIR FORCE UNIV PLA
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