A Mobile Target Tracking Method Based on Enhanced Harris Hawk-Optimized Particle Filtering
In order to address the issue of particle depletion and reduced diversity that may arise from resampling in traditional particle filtering,this paper proposes a Harris hawk optimization particle filtering algorithm based on interactive multimodels.Firstly,the Harris hawk algorithm is introduced into particle filtering,simulating the hunting process by representing particles as individual Harris hawks.Secondly,the hunting process of the Harris hawk is improved by utilizing the hunting mechanism of the wolf pack algo-rithm,particularly in the global search stage strategy,concentrating particles mainly in the high likelihood regions of the target while selectively retaining a portion of particles in low likelihood regions.Lastly,an interactive multimodel algorithm based on three motion models is designed and integrated with the Harris hawk optimization particle filtering.Sinulation verification of maneuvering target tracking under different noise intensities,the results demonstrate that the improved algorithm effectively addresses particle deple-tion issues and exhibits higher accuracy and stability in maneuvering target tracking,outperforming other algorithms.
万方数据
维普
