State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China.
Big Data. 2019 Jun;7(2):114-120. doi: 10.1089/big.2018.0130. Epub 2019 Mar 20.
Particle filtering (PF) algorithm has found an increasingly wide utilization in many fields at present, especially in nonlinear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. This article proposed an improved PF algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in Matlab, it discovers that the proposed algorithm provides better accuracy than sequential importance resampling, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filters in Gaussian mixture noise.
粒子滤波（PF）算法目前在许多领域得到了越来越广泛的应用，特别是在非线性和非高斯情况下。由于粒子退化的限制，已经研究了各种重采样方法。本文提出了一种改进的 PF 算法，该算法结合了不同的秩相关系数来克服退化的缺点。通过在 Matlab 中模拟迭代操作，发现该算法在高斯混合噪声下比序贯重要性重采样、高斯和粒子滤波器和高斯混合 Sigma 点粒子滤波器具有更高的精度。
