Harris Hawk Optimized Interactive Multi-model Target Tracking Method Using Particle Filtering

This paper proposes a Harris hawk-optimized particle filtering algorithm integrated with interactive multiple models for dynamic target tracking. The algorithm simulates the hunting behavior of individual Harris hawks to address particle impoverishment in traditional resampling processes. Additionally, it improves the hunting mechanism using strategies from the wolf pack algorithm, particularly enhancing global search. Furthermore, an interactive multiple model algorithm based on three motion models is designed and integrated. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of accuracy and stability under varying noise intensities.