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This study explores the application of annealed particle filtering as a robust method for state estimation in dynamic systems. By incorporating annealing techniques, we enhance convergence and mitigate the effects of sampling degeneracy in particle filters. Various simulation results illustrate the improved accuracy and efficiency of this approach compared to traditional filtering methods. The findings indicate that annealed particle filtering significantly outperforms standard techniques in challenging scenarios, making it a valuable tool for researchers and practitioners in control systems and signal processing.
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