Particle filters have, in recent years, been found to perform well in highly nonlinear problems as well as in estimation of parameters. However, there is still the problem of particle degeneracy in particle filters which has led to the invention of, among others, feedback particle filters. In this paper, we introduce a stochastically perturbed feedback particle filter and show that it is exact. The novelty is in the fact that the innovation process is stochastically perturbed. Resampled sinkhorn particle filter is also introduced. We then compare their performance with that of other filters in simultaneous state and parameter estimation.
翻译：近年来,粒子过滤器发现在高度非线性问题和参数估计方面表现良好,然而,粒子过滤器中的粒子退化问题依然存在,这导致产生了反馈粒子过滤器。在本文件中,我们引入了一种随机扰动的反馈粒子过滤器,并表明其准确性。新颖之处在于,创新过程是随机的,还引入了重新采样的汇角粒子过滤器。然后,我们用同步状态和参数估计来比较它们与其他过滤器的性能。
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