Switched and Iterated Square-Root Gauss–Hermite Filter for Passive Target Tracking

Abstract

In this paper, a novel algorithm referred to as the switched and iterated square-root Gauss–Hermite filter is developed for the nonlinear state estimation. The algorithm is derived by embedding the matrix triangularization, iteration update, and switching control into the Gauss–Hermite filter architecture. This new filtering approach is used and analyzed in passive localization and tracking system, which is troublesome to other filtering algorithms because of its intrinsic disadvantages such as large initial error and weak observability. Comparative simulation results are demonstrated that a more accurate state estimation can be obtained rapidly by the proposed algorithm with lower computational burden and stronger robustness.