基于KLD采样的自适应UPF非线性状态估计方法

Adaptive Unscented Particle Filter with KLD-Sampling for Nonlinear State Estimation

针对标准UPF算法存在的计算量大、实时性差的问题,设计了一种利用KLD采样在线实时改变粒子个数的自适应UPF算法。该算法的核心思想是利用KLD采样原理,根据预测粒子在状态空间中的分布情况来在线实时的确定下一次滤波迭代所需的粒子个数,减少对滤波算法没有帮助的粒子,仅保留保证滤波估计精度所需的最少粒子个数,从而有效减小算法的运算量,提高算法的实时处理能力。最后,将自适应UPF算法与粒子滤波、标准UPF算法进行了仿真比较,仿真结果表明在保持高精度估计能力的同时,自适应UPF算法比标准UPF算法具有更好的实时性,是解决非线性非高斯系统状态估计问题的一种有效方法。

The Unscented Particle Filter （UPF） was considered as one of the most effective state estimation method for nonlinear and non-Gaussian system. However, UPF had the inherent drawback of costly calculation. An Adaptive UPF by online choosing the number of particles was proposed to overcome the drawback of computational burden in the traditional UPF. The KLD-Sampling was used to determine the number of particles of adaptive UPE The new algorithm chose a small number of particles if the density was focused on a small subspace of the state space, and it chose a large number of samples if the state uncertainty was high. The computer simulations were performed to compare the Adaptive UPF algorithm and the traditional UPF in performance. The simulation results demonstrate that the Adaptive UPF is very efficient and smaller time consumption compared to traditional UPF. Therefore the Adaptive UPF is more suitable to the nonlinear and non-Gaussian state estimation.