Modified polar coordinate sampling particle filter for passive localization of moving emitter

摘要

Passive localization by a single observer, is a typical nonlinear and non-Gaussian filtering problem,and usually suffers large initial estimation error and low observability. Particle filter provides a means to achieve the state estimation in a nonlinear and nonGaussian system, however it may be very inefficient when directly applied to single observe passive localization and tracking (SOPLAT) application.Considering that the measurements' likelihood distribution is more concentrated, and filtering in modified polar coordinate is usually more stable, a novel modified polar coordinate sampling particle filter(MPCPF) is presented, in which the particles are generated from a proposal distribution approximated by linear Kalman filtering with the measurements.Simulation results demonstrate that the estimation error can approximate the Cramer-Rao lower bound.