针对扩展卡尔曼粒子滤波算法滤波精度较低和粒子退化的问题,将马尔可夫链蒙特卡罗(MCMC)方法与扩展卡尔曼粒子滤波相结合,应用于目标跟踪.该算法利用扩展卡尔曼滤波来构造粒子滤波的建议分布函数,使建议分布函数能够融入最新的观测信息,以便得到更符合真实状态的后验概率分布;同时引入MCMC方法对所选的建议分布进行优化处理,使抽样粒子更加多样性.仿真结果表明,该算法能有效地解决粒子贫化问题并提高滤波精度.%Considering the problem of poor tracking accuracy and particle degradation in the traditional particle filter algorithm, discussed a new improved particle filter algorithm with the Markov chain Monte Carlo (MCMC) and extended particle filter.The algorithm used extend Kalman filter to generate a proposal distribution, which could integrate latest observation information to get the posterior probability distribution that was more in line with the true state.Meanwhile, optimized the algorithm by MCMC sampling method, which made the particles more diverse.The simulation results show that the improved extend Kalman particle filter solves particle degradation effectively and improves tracking accuracy.
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