基于改进粒子滤波算法的水下目标跟踪

Underwater target tracking based on improved particle filter

摘要: 针对常规粒子滤波算法粒子数目保持不变的问题，提出了一种可以自适应调整粒子数目的改进算法。该算法将KL距离（Kullback-Leibler Divergence，KLD）引入粒子滤波重采样过程，保证在一定的滤波精度下，可以有效地调整滤波过程中使用的粒子数目，从而实现了滤波过程中粒子数目的自适应。将该算法应用于纯方位水下目标跟踪，仿真结果表明，该方法有效地改善了滤波效果，计算量低，适合于实际应用。

Abstract: An improved adaptive particle number method is presented to solve the problem of huge computation complexity.The algorithm is proposed via introducing the KLD (Kullback-Leibler Divergence) into the process of resampling so as to control the effective particle number.Numerical simulations demonstrate that the modified method can improve tracking performance and suit for practical applications.