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Sensors 2016, 16(12), 2191; doi:10.3390/s16122191

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

1,* , 2,3
and
1
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
Received: 2 September 2016 / Revised: 5 December 2016 / Accepted: 14 December 2016 / Published: 20 December 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [814 KB, uploaded 20 December 2016]   |  

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cai, S.; Zhou, Q.; Zhu, H. A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation. Sensors 2016, 16, 2191.

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