Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing
Abstract
This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to overcome the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar paramter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided for high signal-to-noise ratio (SNR) and low SNR scenarios. Finally, numerical examples are generated to compare the performances of the proposed approaches with those of the existing ones.
- Publication:
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IEEE Transactions on Signal Processing
- Pub Date:
- February 2013
- DOI:
- arXiv:
- arXiv:1802.03070
- Bibcode:
- 2013ITSP...61..933A
- Keywords:
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- Array processing;
- asymptotically minimum variance estimator;
- direction-of-arrival (DOA) estimation;
- sparse AMV estimation;
- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- Abeida Habti, Qilin Zhang, Jian Li, and Nadjim Merabtine. "Iterative sparse asymptotic minimum variance based approaches for array processing." IEEE Transactions on Signal Processing 61, no. 4 (2013): 933-944