Particle Filter Enhanced with Error Correction Algorithm

In this research, we implemented a particle filter enhanced with an error correction algorithm, specifically designed to resolve the problem of filter divergence during local filtering operations. The error correction algorithm employs a systematic approach to adjust particle weights through appropriate weight normalization and correction techniques, thereby significantly improving the filter's accuracy and stability. Our implementation includes a weight adjustment mechanism that recalculates particle importance based on residual errors, preventing weight degeneracy and enhancing state estimation performance. Furthermore, we optimized the algorithm by incorporating efficient resampling strategies and parallel computation techniques to boost both computational efficiency and estimation precision. The optimized algorithm maintains O(N) complexity while improving numerical stability through systematic error propagation management. Overall, this study provides an effective solution for particle filter applications and offers valuable insights for research in related fields, particularly in scenarios requiring robust state estimation under uncertain conditions.