一种补偿的扩展KALMAN粒子滤波

Compensated Extended Kalman Particle filter

设计合适的重要性概率密度函数是粒子滤波中的一个重要问题。首先分析了扩展Kalman滤波器的线性化误差,然后加入调节因子,采用补偿的方法减小线性化误差,并用此方法获取粒子滤波中的重要性概率密度函数,同时该概率密度函数参考了最新的观测量,因此提议分布产生的粒子更能反映系统状态的后验概率分布。实验结果表明新算法的估计性能优于标准粒子滤波和Kalman粒子滤波,与UnscentedParticleFilter相比,新算法降低了计算复杂度。

Designing an appropriate importance density function is a critical issue in particle filter. The linearization error of the extended Kalman filter （EKF） was analyzed firstly. Then a compensation method with adjustable factor was introduced to decrease the linearization error and to generate the proposal distribution in particle filtering. Meanwhile, the new proposal distribution integrated the latest observation. Particles sampled from this distribution are closer to the true distribution than the ones sampled from EKF. Experimental results show that the proposed particle filter performed better than the standard particle filter （PF） and the Kalman particle filter. At the same time, the complexity of the proposed algorithm is lower than the unscented particle filter （UPF）.