Paper Info
Title
Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
Abstract
The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time-division multiplexing (TDM) multiple-input, multiple-output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high-computational complexity when dealing with massive TDM-MIMO observations, due to high-dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction-of-arrival (DOA) estimation of the TDM-MIMO radar based on the receiving features of the sub-aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM-MIMO reception time to improve the real-time performance. Additionally, it allows for much less iterations, avoiding high-dimensional matrix operations, allowing the computational complexity to be reduced from O (K-3) to O(K-2). Simulated and real-data results demonstrate the superiority and effectiveness of the proposed method.
Year
DOI
Venue
2022
10.3390/rs14092133
REMOTE SENSING
Keywords
DocType
Volume
LASSO, TDM-MIMO, DOA, online
Journal
14
Issue
ISSN
Citations 
9
2072-4292
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Jiawei Luo100.34
Yongwei Zhang200.34
Jianyu Yang3817.44
Donghui Zhang400.34
Yongchao Zhang500.68
Yin Zhang63910.94
Yulin Huang700.68
Andreas Jakobsson840943.32