基于Box粒子滤波的参数自适应机动目标跟踪

针对机动目标跟踪加速度的不确定性,引入一种新的参数自适应算法,采用粒子滤波及高斯核密度估计技术,估计目标机动参数,实现对任意机动目标的跟踪。在此基础上,考虑到粒子滤波计算代价较高的问题,进一步引入区间分析技术,采用Box粒子代替传统的粒子,以提高算法的计算效率。实验结果表明,提出的算法能够有效地跟踪任意机动目标,且运算时间明显低于传统的参数自适应算法。

Adaptive parameter tracking algorithm based on box particle filter

A new kind of adaptive parameter estimation algorithm is introduced for the acceleration uncertainty of maneuvering target tracking.The particle filter and Gaussian kernel density estimation are used to estimate target maneuvering parameter accurately.In order to decrease the computational complexity,the interval analysis technology is further introduced.Box particles are used to replace traditional particles,which can improve the computational efficiency.The simulation results show that the proposed algorithm can effectively track arbitrary maneuvering target,and the computation time is significantly less than that of the traditional adaptive parameter estimation algorithm.