EKF、PF在目标跟踪中的研究

介绍两种目标跟踪算法—扩展卡尔曼滤波器（Extended Kalman Filter,EKF）、粒子滤波器（Particle filter,PF）。EKF利用泰勒级数方法,将非线性问题转化到线性空间,再利用卡尔曼滤波器进行滤波,并达到一阶估计精度。PF是一种采用蒙特卡罗采样的贝叶斯滤波方法,它将复杂的目标状态分布表示为一组加权值,通过寻找在粒子滤波分布中最大权值的粒子来确定目标最可能所处的状态分布,已成为复杂环境下进行目标跟踪的最好的方法。文中通过仿真实验,对二者的性能进行了仿真比较,结果证明在复杂的非高斯非线性环境中,PF的性能明显优于EKF,但计算复杂,耗时长。

Research of EKF、PF in the target tracking

This paper presents two target tracking algorithm,EKF and PF.By using Taylor series,EKF transforms nonlinear problem into linear space,then using Kalman filter to estimate the results to achieve the first order accuracy.PF is a Bayesian filtering adopted by Monte Carlo sampling method.The complex target state distribution is expressed as a set of weights in this filter.By finding the largest weight particles in the particle filter to determine the most likely target has been proved as the best way to track target in a complex environment.By simulation experiments,their performances are compared.The results prove the tracking performance of PF is much better than EKF in complex environment,but computation of PF is much larger than EKF.