This paper proposes efficient target localization methods for a passive radar system using bistatic time-of-arrival (TOA) information measured at multiple synthetic array locations, where the position of these synthetic array locations is subject to random errors. Since 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 errors, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position errors involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide mean square position error performance very close to the Cramer-Rao lower bound even for larger values of noise and position estimation errors.

Date Published: 28 May 2013
PDF: 10 pages
Proc. SPIE 8753, Wireless Sensing, Localization, and Processing VIII, 87530I (28 May 2013); doi: 10.1117/12.2018148

Published in SPIE Proceedings Vol. 8753:
Wireless Sensing, Localization, and Processing VIII
Sohail A. Dianat; Michael David Zoltowski, Editor(s)