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.

中文翻译：

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一种改进的使用不同相关系数的粒子滤波算法，用于非线性系统状态估计。

目前，粒子滤波（PF）算法已在许多领域得到越来越广泛的应用，尤其是在非线性和非高斯情况下。由于粒子简并性的限制，已经研究了各种重采样方法。提出了一种改进的PF算法，结合了不同的秩相关系数，克服了简并的缺点。通过在Matlab中模拟迭代操作，发现该算法在高斯混合噪声中比顺序重要性重采样，高斯总和粒子滤波器和高斯混合sigma-point粒子滤波器提供更高的精度。