A novel wideband DOA estimation method based on a fast sparse frame

Abstract

In this study, a novel fast wideband direction of arrival (DOA) estimation algorithm is proposed to reduce the computational complexity. First, a multiple measurement vector (MMV)-based compact structure for a wideband signal is established. Combined with the focus operation, the array manifolds of different frequency bins are transformed into the dictionary of the reference frequency. Then, two efficient novel methods named adaptive step-size-based null space tuning with hard thresholding and feedback (ASNHF) and MMV-ASNHF are proposed to process single measurement vector and MMV problem, respectively. Finally, wideband DOA estimation can be achieved by MMV-ASNHF algorithm. Compared with other algorithms, the proposed algorithm has higher accuracy at a low signal-to-noise ratio and lower computational complexity. Simulation results show that the proposed estimator is effective and feasible.

Off‐grid direction‐of‐arrival estimation for wideband noncircular sources

“…In a variety of practical applications, wideband signals are nearly ubiquitous. Previous studies about wideband DOA estimation methods can be categorized into three types: (a) maximum likelihood (ML) methods [11][12][13], (b) subspace-based algorithms [14][15][16][17][18][19][20][21][22][23], and (c) SR-based algorithms [24][25][26][27][28][29][30]. Multi-dimensional search is required by ML approaches, which are burdened by enormous computational complexities.…”

“…Although the dictionary contains array manifold matrices at all frequency bins, the dictionary's large size may result in enormous computational complexity. The third idea is to transform the signal model into the multiple measurement vectors (MMV) problem [28][29][30]. Different manifold matrices can be concentrated into a single dictionary.…”