一种新的避免航迹合并的比例联合概率关联算法

摘要

Through analysis of coalescence neighboring tracks in the application of joint probability data association, this paper proposes a novel Scaled Joint Probabilistic Data Association (SJPDA) algorithm based on the maximum interact probability, which finds the nearest measurement to the interact probability 'center' of the neighboring targets, and then updates the association probability between the expected measurement and targets to avoiding track coalescence. The simulations prove the effectiveness of the novel algorithm at the same time scale.%通过对传统的联合概率数据关联算法(JPDA)在平行临近及小角度交叉目标的关联跟踪中引起的航迹合并问题的分析,提出了一种基于最大交互概率的改进比例联合概率数据关联算法.该算法找出距离两逼近目标的交互概率中心最近的有效观测,引入一比例因子对该观测与所有目标的关联概率进行修正,将逼近目标“拉开”,从而抑制了航迹的合并.仿真结果表明,改进算法在同等时间复杂度下很好地抑制了传统的JPDA算法引起的航迹合并.