Target localization in a multi-static passive radar system through convex optimization

• Published in: Signal processing Vol. 102; pp. 207 - 215
• Main Authors: Chalise, Batu K., Zhang, Yimin D., Amin, Moeness G., Himed, Braham
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2014; Elsevier
• Subjects: Applied sciences; Convex optimization; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; Miscellaneous; Passive radar; Radar signal processing; Semi-definite relaxation; Signal and communications theory; Signal processing; Signal, noise; Target localization; Telecommunications and information theory; Semi-definite relaxation; Passive radar; Radar signal processing; Convex optimization; Target localization; Performance evaluation; Non convex programming; Random error; Multistatic radar; Passive system; Algorithm; Convex programming; Optimization; Mean square error; Arrival time; Relaxation; Simulation; Signal processing; Maximum likelihood; Target detection; Cramer Rao inequality; Software radio
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2014.02.023

We propose efficient target localization methods for a passive radar system using time-of-arrival (TOA) information of the signals received from multiple illuminators, where the position of the receiver is subject to random errors. Since the maximum likelihood (ML) formulation of this target localization problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in general do not provide satisfactory performance. As a result, approximated ML optimization problems are proposed and solved with SDR plus bisection methods. For the case without position error, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position error involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide root mean-square error performance very close to the Cramer–Rao lower bound. •Efficient target localization methods exploiting time-of-arrival information are developed based on semi-definite relaxation (SDR).•The proposed methods provide root mean-square error (RMSE) performance that is close to the Cramer–Rao lower bound (CRLB) and outperform existing SDR-based target localization techniques.•The application to bistatic passive radar systems with multiple illuminators is new.•The computation complexity of the proposed methods is shown to be advantageous as compared to existing SDR-based methods.