基于压缩感知理论的宽带信号波达方向估计

doi:10.13229/j.cnki.jdxbgxb20200694

Abstract

Abstract:

In order to solve the problem that the Direction of Arrival (DOA) estimation of one-dimensional wideband signal needs angle estimation, a DOA estimation algorithm without angle estimation is designed on the basis of bilateral correlation transform algorithm. Firstly, the wideband signal is decomposed into several narrow-band data models at different frequency points by using Discrete Fourier Transform (DFT). Secondly, the narrow-band data at different frequency points are focused on the same reference frequency point by the improved focusing matrix, thus, the narrow-band signal model at a single frequency point is obtained. Finally, the improved array covariance matrix sparse iterative estimation algorithm is used to solve the problem. Theoretical research shows that the proposed method has higher estimation accuracy and resolution than the traditional algorithm under the condition of low SNR and multiple snapshots, and the computational complexity of the algorithm is effectively reduced by combining with compressed sensing theory. The simulation results verify the reliability of the above conclusions.

Key words: signal and information processing, direction of arrival（DOA） estimation, compressive sensing, focusing transformatio

CLC Number:

TN911.7

Hui-jing DOU,Gang DING,Jia GAO,Xiao LIANG. Wideband signal direction of arrival estimation based on compressed sensing theory[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2237-2245.

Figures/Tables 9

Table 1

Contrast table of focus transformationcriteria and focus matrix"

TCT算法	聚焦变换准则	$T (f j) ? A (f j, θ) S (f j) = A (f 0, θ) S (f 0)$
TCT算法	聚焦矩阵	$T (f j) = Q (f 0) Q H (f j)$
RSS算法	聚焦变换准则	$T (f j) ? A (f j, θ) = A (f 0, θ)$
RSS算法	聚焦矩阵	$T (f j) = V (f j, θ) U H (f j, θ)$
MTLS-CSSM 算法	聚焦变换准则	$T (f j) ? A (f j, θ) = A (f 0, θ)$
MTLS-CSSM 算法	聚焦矩阵	$T (f j) = A 0 (A 0 H A 0) - 1 / 2 (A j H A j) - 1 / 2 A j H$

Table 1

Fig.1

Fig.2

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Fig.7

Table 2

Running time comparison of different algorithms"

算法	复杂度	运行时间/s
TCT	$O (M 3 + 2 N 3 M 3 + N 2 M 3)$	2.735 147
l₁-SRACV	$O (M 3 N 3)$	2.290 214
本文	$O (k N 3 M 3 + M 2)$	2.435 238

Table 2

References 16

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