An Improved Particle Filter Algorithm Based on Neural Network for Target Tracking
Wen Qin, Peng Qi-cong · 2007
To the shortcoming of general particle filter, an improved algorithm based on neural network is proposed and is shown to be more efficient than the general algorithm in the same sample size. The improved algorithm has mainly optimized the choice of importance density. After receiving the samples drawn from prior density, and then adjust the samples with general regression neural network (GRNN), make them approximate the importance density. Apply the new method to target tracking problem, has made the result more precise than the general particle filter.