区间量测下基于无迹变换的伯努利粒子滤波算法

Bernoulli particle filter algorithm based on unscented transformation in interval measurement

针对区间量测下目标的实时检测与跟踪问题,提出基于无迹变换的伯努利粒子滤波算法(BernoulliUpf).该算法在伯努利粒子滤波算法(Bernoulli-pf)的基础上融合无迹卡尔曼滤波(UKF),融合后的算法在预测步骤产生持续存活粒子时,充分考虑到当前时刻的量测,从而引导粒子向高似然区域移动,使得粒子分布更加接近真实状态的后验分布.仿真实验表明,Bernoulli-Upf算法的估计精度优于Bernoulli-pf算法.

An improved Bernoulli particle filter algorithm based on unscented transformation is proposed for target detection and tracking in the interval measurement. Under the theory framework of the particle filter, an algorithm which combines the particle filter with the unscented Kalman filter（UKF） is presented. When persistent particles are calculated during the predicted measure by using the algorithm, the persistent particles are most likely to be in the region of high likelihood based on the current measurement, which makes the particles distribution more approach to the true posterior distribution of the state. Simulation results show that the tracking error of the improved Bernoulli particle filter is less than the original algorithm.