| 1. |
- Luo, Jiawei, et al.
(författare)
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Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:9
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Tidskriftsartikel (refereegranskat)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( K3) to O( K2). Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.
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| 2. |
- Zhang, Yongchao, et al.
(författare)
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Online Sparse Reconstruction for Scanning Radar Using Beam-Updating q-SPICE
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1545-598X. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The generalized sparse iterative covariance-based estimation ( $q$ -SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating $q$ -SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online $q$ -SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online $q$ -SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating $q$ -SPICE method in the task of sparse reconstruction for scanning radar.
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