A Novel Subspace Approach for Bearing-Only Target Localization

Abstract

A new subspace method is proposed to solve the bearing-only target localization (BOTL) problem. Instead of linearizing the nonlinear bearing equations to form least-squares optimization, we construct a scalar product matrix by making full use of all bearing and intersensor bearing geometric information. After exploiting the dimension and eigenstructure of the scalar product matrix, we devise a subspace algorithm for BOTL with its weights computed from a prior location estimate. Simulation results show that the proposed algorithm achieves a mean square error performance close to the Cramer–Rao lower bound, even at high noise levels. Field experiments demonstrate the superiority of the proposed algorithm.