Recursive and Nonrecursive Passive Target Localization Algorithms with Bearing-Only Measurements

The paper investigates the problem of passive target localization from a moving observer using bearing-only measurements. The following three estimation algorithms are considered: a recursive iterative extended Kalman smoother; a nonrecursive iterative batch linearized filter, and a recursive iterative batch linearized smoother. The accuracy and computational complexity of the recursive and nonrecursive algorithms are compared using the Monte Carlo simulation. The Cramer-Rao lower bound for the problem under consideration is calculated. The advantages of the recursive iterative batch linearized smoother over the nonrecursive iterative batch linearized filter and the recursive iterative extended Kalman smoother are shown.