Multi-target DOA estimation method based on improved SP algorithm

被引：0

作者：

Cao R. ^{[1
,2
]}

Zhao Y. ^{[1
,2
]}

Qiu Y. ^{[1
,2
]}

机构：

[1] National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an

[2] School of Electronic Engineering, Xidian University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 07期

关键词：

direction of arrival (DOA) estimation; relaxation algorithm; sparse reconstruction; subspace pursuit;

D O I：

10.12305/j.issn.1001-506X.2024.07.13

中图分类号：

学科分类号：

摘要：

The application of sparse reconstruction algorithms in radar target parameter estimation has been a hot topic in recent years. However, due to the limitations of sparse reconstruction algorithms, they are affected by the mutual influence between atoms when estimating the direction of arrival (DOA) of target waves, resulting in a decrease in the accuracy of multi-target angle measurement. To address this issue, a relaxation subspace tracking algorithm based on the idea of signal separation iteration is proposed. Firstly, the multiple atoms with the strongest correlation between the echo signal and the normalized dictionary matrix are calculated as the initial estimated values. Then, the initial estimated angles is used to construct the cost function, and estimate repeatedly until the cost function converges. The simulation results show that the proposed algorithm reduces the influence of the number of targets and phase difference, improves the angle measurement accuracy of multi-target DOA estimation, and reduces the computational complexity compared to traditional relaxation algorithms. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：2294 / 2300

页数：6

共 33 条

[1]

CHENG Z Y, LIAO B., QoS-aware hybrid beamforming and DOA estimation in multi-carrier dual-function radar-communication systems, IEEE Journal on Selected Areas in Communications, 40, 6, pp. 1890-1905, (2022)

[2]

CHEN P, YANG Z H, CHEN Z M, Et al., Reconfigurable intelligent surface aided sparse DOA estimation method with non-ULA, IEEE Signal Processing Letters, 28, pp. 2023-2027, (2021)

[3]

ABRAMOVICH Y, SPENCER N K, GOROKHOV A Y., GL-RT-based threshold detection-estimation performance improvement and application to uniform circular antenna arrays, IEEE Trans, on Signal Processing, 55, 1, pp. 20-31, (2006)

[4]

ZHANG Y L., Research on greedy reconstruction algorithms for compressed sensing, (2016)

[5]

VIBERG M, OTTERSTEN B., Sensor array processing based on subspace fitting, IEEE Trans, on Signal Processing, 39, 5, pp. 1110-1121, (1991)

[6]

DONG J Y, LYU W T, ZHOU D, Et al., Variational Bayeslan and generalized approximate message passing-based sparse Bayesian learning model for image reconstruction, IEEE Signal Processing Letters, 29, pp. 2328-2332, (2022)

[7]

ZHOU J H, ZHANG B, ZENG S N., Consensus sparsity: multi-context sparse image representation via Loo-induced matrix vari-ate[j], IEEE Trans, on Image Processing, 32, pp. 603-616, (2022)

[8]

PANHUBER R., Fast, efficient, and viable compressed sensing, low-rank, and robust principle component analysis algorithms for radar signal processing, Remote Sensing, 15, 8, (2023)

[9]

LIU S Y, WANG S, SHI T X, Et al., Photonics-assisted compressed sensing radar receiver for frequency domain non-sparse signal sampling based on dictionary learning, Optics Letters, 48, 3, pp. 767-770, (2023)

[10]

YONG J W, LI K X, FENG Z J, Et al., Research on photon-integrated interferometric remote sensing image reconstruction based on compressed sensing, Remote Sensing, 15, 9, (2023)

← 1 2 3 4 →