An Efficient Semidefinite Relaxation Algorithm for Moving Source Localization Using TDOA and FDOA Measurements

Abstract

In this letter, we address the moving source localization problem by using time-difference-of-arrival and frequency-difference-of-arrival measurements. The localization problem is first reformulated based on the robust least squares criterion and then perform semidefinite relaxation (SDR) to obtain a convex semidefinite programming problem, which can be solved efficiently via optimization toolbox. Unlike several existing SDR localization methods requiring the initial estimate, the proposed method does not require this priori knowledge. The simulation results also show the superior positioning performance of the proposed method at high noise level than other existing methods.