纯方位目标跟踪的ST-MEF-PDAF方法研究

Study of ST-MEF-PDAF for Bearings-only Target Tracking

摘要:

传统的最大熵模糊概率数据关联滤波器(MEF-PDAF)算法用于水下杂波环境下单站纯方位目标跟踪存在对系统模型变化鲁棒性差、跟踪机动目标能力低的问题；为了解决这些问题,对MEF-PDAF算法进行了改进,提出了强跟踪MEF-PDAF(ST-MEF-PDAF)算法；与强跟踪滤波器(STF)算法类似,ST-MEF-PDAF算法通过引入渐消因子来实时调节增益矩阵,提高了算法的鲁棒性；进行了水下杂波环境下单观测站纯方位目标跟踪的仿真实验,ST-MEF-PDAF能够在500秒以内跟踪机动目标,而传统的MEF-PDAF算法不能,即ST-MEF-PDAF算法跟踪机动目标的能力高于传统的MEF-PDAF算法。

Abstract:

Traditional maximum entropy fuzzy probabilistic data association filter (MEF-PDAF) algorithm has the problems of weak robustness to model variation and low capacity to track maneuvering target in single observer bearings-only target tracking in cluttered underwater environment. In order to resolve these problems, MEF-PDAF is improved, and a novel algorithm named strong tracking MEF-PDAF (ST-MEF-PDAF) is proposed. Similar to strong tracking filter (STF) algorithm, the fading factor is introduced in ST-MEF-PDAF to adjust the gain matrix, and its robustness is strengthened. The simulation of single observer bearings-only target tracking in cluttered underwater environment is conducted, ST-MEF-PDAF algorithm can track maneuvering target in 500 seconds, but the traditional MEF-PDAF can’t, so the ability to track maneuvering target of ST-MEF-PDAF algorithm is higher than traditional MEF-PDAF algorithm.