Neural network-aided sparse convex optimization algorithm for fast DOA estimation. (May 2022)

• Record Type: Journal Article
• Authors: Cong, Jingyu; Wang, Xianpeng; Wan, Liangtian; Huang, Mengxing
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 44:Number 8(2022)
• Page Start: 1649
• Page End: 1655
• Publication Date: 2022-05
• Subjects: Fast DOA estimation -- neural network -- sparse convex optimization -- computational complexity -- off-grid; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312211049067
• Languages: English
• ISSNs: 0142-3312

Abstract

In this paper, a fast sparse convex optimization algorithm based on a neural network is proposed to improve the direction of arrival estimation. First, a fastL 1 -sparse representation of the array covariance vector model based on the Hermitian Toeplitz structure of array covariance is established to reduce computational complexity in data dimension and variable number. Then, the estimation error upper bound problem is investigated, and a neural network-aided coefficient selection method is developed. The direction of arrival estimation problem is solved through spectral peak search. Finally, the algorithm is extended to the case of off-grid error. The algorithm's advantages in accuracy, calculation speed and robustness is verified by the simulations.