Convex Solution for Target Localization in Passive MIMO Radar Using Delay,Doppler and Angle Measurements
A convex-optimum localization algorithm based on semidefinite relaxation is proposed for moving tar-get localization from time delay,Doppler shift and angle of arrival measurements in distributed multiple-input multiple-output radar. This algorithm alleviates the threshold effect that the positioning error deviates from the Cramer-Rao lower bound (CRLB) when the measurement error is large. First,the localization problem is formulated as a maximum likeli-hood estimation problem,which is reformulated as a weighted least squares problem with constraints by introducing auxiliary variables and then a convex semidefinite programming (SDP) problem by performing semidefinite relaxation. The SDP problem is solved efficiently by using the interior-point method to obtain the target position and velocity esti-mates. Since the local optimal solution of the convex optimization problem is the global optimal solution,the proposed algorithm has good global convergence. Simulation results demonstrate that the proposed algorithm approaches the CRLB,and achieves higher localization accuracy and robustness than existing algorithms at relatively large measure-ment noise levels.
distributed MIMO radarangle of arrivaltime delayDoppler shiftsemidefinite relaxation
万方数据
维普
