Li, N.; Zhang, X.; Zong, B.; Lv, F.; Xu, J.; Wang, Z. Wideband DOA Estimation Utilizing Hierarchical Prior Based on Variational Bayesian Inference. Preprints2023, 2023051622. https://doi.org/10.20944/preprints202305.1622.v1
APA Style
Li, N., Zhang, X., Zong, B., Lv, F., Xu, J., & Wang, Z. (2023). Wideband DOA Estimation Utilizing Hierarchical Prior Based on Variational Bayesian Inference. Preprints. https://doi.org/10.20944/preprints202305.1622.v1
Chicago/Turabian Style
Li, N., Jiahua Xu and Zhaolong Wang. 2023 "Wideband DOA Estimation Utilizing Hierarchical Prior Based on Variational Bayesian Inference" Preprints. https://doi.org/10.20944/preprints202305.1622.v1
Abstract
Sparse direction-of-arrival (DOA) estimation of wideband signal has attracted widespread researches for its unique high-resolution performance. Numerous existing methods based on sparse Bayesian learning (SBL) don’t possess the ability to enhance sparsity even if they have already enjoyed high favor among sparse recovery approaches. In view of this, we propose a novel hierarchical Bayesian prior framework that enhance sparsity evidently and derive its corresponding iterative algorithm. On analysis, the computational complexity of the iterative are less than most existing other state-of-the-art algorithms. Not only that, proposed method possesses high angular estimation precision and sparsity performance by utilizing joint sparsity of multiple measure vector (MMV) models. Last but not least, the method obtains the ability to stabilize the estimated values between different frequencies or snapshots such that flat spatial spectrum. Extensive simulation results are presented to prove the superior performance of our methods
Copyright:
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