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首页> 外文期刊>Journal of Intelligent Learning Systems and Applications >Particle Filtering Optimized by Swarm Intelligence Algorithm
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Particle Filtering Optimized by Swarm Intelligence Algorithm

机译：群体智能算法优化粒子滤波

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A new filtering algorithm — PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which means that these particles will move to the region with higher weights. This process can be regarded as one-step predefined PSO process, so the proposed algo-rithm is named PSO-UPF. Although the PSO process increases the computing load of PSO-UPF, but the refined weights may make the proposed distribution more closed to the poster distribution. The proposed PSO-UPF algorithm was compared with other several filtering algorithms and the simulating results show that means and variances of PSO-UPF are lower than other filtering algorithms.
机译：针对非线性动态系统，提出了一种新的滤波算法PSO-UPF。根据重采样的概念，应将较大重量的粒子重新采样更多的时间，并且在PSO-UPF中，在计算了粒子的重量之后，一些粒子将加入精炼过程，这意味着这些粒子将移动到权重较高的区域。该过程可以看作是一步式预定义的PSO过程，因此提出的算法称为PSO-UPF。尽管PSO过程增加了PSO-UPF的计算量，但是经过改进的权重可能使建议的分布更接近发布者分布。将提出的PSO-UPF算法与其他几种滤波算法进行了比较，仿真结果表明，PSO-UPF的均值和方差均低于其他滤波算法。

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