Adaptive object tracking based on pruning quadrature Kalman particle filter
基于修正积分卡尔曼粒子滤波的自适应目标跟踪算法

This paper proposed a new adaptive particle filter for the particle degeneration phenomenon and the contradiction between accuracy and consumption. Considering two parallel improving filtering methods, such as optimum proposed distribution function and re-sampling. First, used pruning quadrature Kalman filter produce optimization proposal distribution function, on the basis of quadrature Kalman filter, introduced pruning factors to improve filtering precision and reduce the running time. In resampling stage, introduced system estimation and prediction for the new spreads value online adaptive adjustments sampling particle counts, kept better sampling efficiency and real-time. Theoretical analysis and experiments show that the proposed new particle filter algorithm has higher accuracy and lower computation time than other improved particle filters, which is a new kind of particle filter algorithm for high precision.