Hybrid Particle Filtering Algorithm for Passive Location by a Single Observer Based on Bearing Constrained Sampling
基于角度约束采样的单站无源定位混合粒子滤波算法

To achieve fast location of moving emitter by a single stationary observer, an algorithm of hybrid particle filter based on bearing constrained sampling is presented. The algorithm gets proposal importance density from Extended Kalman Filter(EKF), and generates particles through the constraint between bearing measurements and the state variables, thus the number of particles and computation cost decrease when tackling high-dimensional filtering, and the filtering performance gets improved. Applying the algorithm to the location method of using Doppler changing rate and bearing measurements, simulation results of comparing the proposed algorithm with EKF, Unscented Kalman Filter(UKF) and the general hybrid particle filter, show that the proposed algorithm is superior in convergence speed, tracking precision and filtering stability to others, and the estimation error is more closer the Cramer-Rao lower bound.