基于改进粒子滤波的双站无源定位跟踪算法研究

在非线性非高斯状态空间下,粒子滤波器是一种有效的非线性滤波算法,它的关键问题包括粒子权重的计算、粒子重采样和状态估计等.根据粒子滤波算法思想和双站无源定位跟踪的非线性,将粒子滤波算法用于双站无源定位跟踪问题,给出了一种改进的粒子滤波算法,并对其关键问题根据双站无源定位跟踪的特殊性进行了改进.利用matlab进行了仿真实验,与最小二乘算法、扩展卡尔曼滤波算法进行了比较,结果表明所提算法定位跟踪精度优于其他方法.

Studies on Passive Location Algorithm Based on Improvement Particle Filter

Particle filtering algorithm is an effective non-linear filter in the non-linear and non-gaussian state. Its key issues are weights computing, resampling and state estimation. According to the particle filter and the nonlinear of passive location, a new passive location algorithm based on an improvement particle filter was presented that was used in passive location tracking. And its key issues were improved on the particularity of passive location tracking. It was compared with linear minimum mean-square error filtering and extended kalman filtering in passive location. Experiments were made in matlab. It is proved that the location error by an improvement particle filtering is less than by other algorithms.