A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA EstimationReport as inadecuate


A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation


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1

Jiangsu Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

2

Key Laboratory of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

3

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China





*

Author to whom correspondence should be addressed.



Academic Editor: Xue Wang

Abstract Direction of arrival DOA estimation using a uniform linear array ULA is a classical problem in array signal processing. In this paper, we focus on DOA estimation based on the maximum likelihood ML criterion, transform the estimation problem into a novel formulation, named as sum-of-squares SOS, and then solve it using semidefinite programming SDP. We first derive the SOS and SDP method for DOA estimation in the scenario of a single source and then extend it under the framework of alternating projection for multiple DOA estimation. The simulations demonstrate that the SOS- and SDP-based algorithms can provide stable and accurate DOA estimation when the number of snapshots is small and the signal-to-noise ratio SNR is low. Moveover, it has a higher spatial resolution compared to existing methods based on the ML criterion. View Full-Text

Keywords: DOA estimation; maximum likelihood; uniform linear array; sum-of-squares; semidefinite programming; alternating projection DOA estimation; maximum likelihood; uniform linear array; sum-of-squares; semidefinite programming; alternating projection





Author: Shu Cai 1,* , Quan Zhou 2,3 and Hongbo Zhu 1

Source: http://mdpi.com/



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