Among other methods, state estimation using filters is currently used to increase the accuracy of systems. These filters are categorized into the following three branches: linear, nonlinear with Gaussian noise, and nonlinear with non-Gaussian. In this paper, the performance of particle filters is investigated via nonlinear, non-Gaussian methods. The main aim of this paper was to improve the performance of particle filters by eliminating particle impoverishment. A resampling step was proposed to overcome this limitation. However, resampling usually leads to a dearth of samples. Therefore, to virtually increase the number of samples, the available samples were broken into smaller parts based on their respective weights via a clustering approach. The experimental results indicate that the proposed procedure improves the accuracy of state estimations without increasing computational burden

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