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A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation.
Shu Cai1, Quan Zhou2,3, Hongbo Zhu4
1Jiangsu Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. caishu@njupt.edu.cn.
Sensors (Basel, Switzerland)
|December 22, 2016
Summary
This study introduces a novel sum-of-squares (SOS) and semidefinite programming (SDP) method for direction of arrival (DOA) estimation. The new approach offers stable, accurate DOA estimation, even with limited data and low signal-to-noise ratio (SNR).
Area of Science:
- Array Signal Processing
- Optimization Techniques
Background:
- Direction of Arrival (DOA) estimation is a fundamental challenge in array signal processing.
- Existing methods often struggle with limited snapshots and low signal-to-noise ratios (SNR).
Purpose of the Study:
- To develop a novel and robust method for DOA estimation.
- To improve accuracy and spatial resolution compared to existing Maximum Likelihood (ML) criteria.
Main Methods:
- Formulating the DOA estimation problem using a sum-of-squares (SOS) approach.
- Solving the SOS formulation via semidefinite programming (SDP).
- Extending the method for multiple DOA estimation using alternating projection.
Main Results:
- The SOS- and SDP-based algorithms demonstrate stable and accurate DOA estimation.
- Effective performance is achieved even with a small number of snapshots and low SNR.
- The proposed method exhibits higher spatial resolution than traditional ML-based techniques.
Conclusions:
- The SOS and SDP framework provides a powerful new tool for DOA estimation.
- This approach enhances performance in challenging signal conditions.
- It offers a significant improvement in spatial resolution for array signal processing.
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