基于UT的混合粒子滤波单站无源定位算法

Hybrid particle filtering algorithm based on UT for passive location by a single observer

为利用无源固定单站对运动辐射源快速定位,将粒子滤波和UT(unscented transformation)应用于单站无源定位,给出了一种基于UT的角度约束采样混合粒子滤波无源定位算法,该算法从UKF滤波得到建议分布,从该建议分布采样时引入角度测量对状态变量的约束,可以减少粒子滤波用于高维情况时所需的粒子数目,改善滤波性能。与EKF、UKF (unscented kalman filter)以及基于EKF的混合粒子滤波算法的仿真比较表明,本文算法在滤波收敛速度、跟踪精度以及稳定性方面优于其它算法,估计误差可以接近Cramer-Rao下界。

To achieve fast location of moving emitter by a single passive stationary observer, applying particle filter and unscented transformation （UT） to passive location, an algorithm of bearing constrained sampling hybrid particle filter based on UT is presented. The algorithm gets proposal importance density from unscented kalman filter, and generates particles through the constraint between bearing measurements and the state variables, thus the number of particles decrease when tackling high-dimensional filtering, and the filtering performance gets improved. Simulation results of comparing the proposed algorithm with extend kalman filter（ EKF）, unscented kalman filter（UKF） and the EKF based hybrid particle filter, shows that the proposed algorithm is superior in convergence speed,tracking precision and filtering stability to others,and the estimation error can approximate the Cramer-Rao lower bound.