面向目标的概率多假设跟踪算法

 概率多假设跟踪(PMHT)算法由于其计算量与目标和量测的个数成线性关系而成为一种重要的数据关联方法,但该算法采用的是一种面向量测的参数模型,容易受到杂波的干扰。针对这个问题,提出一种面向目标的PMHT（TO/PMHT）算法,将量测与目标的距离作为权重,使计算出的后验关联概率能够真实地反映量测和目标的关联可能性。通过多种典型环境的仿真计算表明：TO/PMHT算法和面向量测的PMHT算法相比,跟踪性能有了明显的提高;与多假设跟踪(MHT)算法相比,在保持跟踪性能的同时,极大地提高了计算效率。

Target Oriented Probabilistic Multi-hypothesis Tracker Algorithm

Probabilistic multi-hypothesis tracker (PMHT) algorithm is an important data association algorithm owing to the fact that its computational cost is linear with respect to the number of targets and observations. However, the present PMHT is a measurement oriented algorithm which is hospitable to clutters. Therefore, this article proposes a target oriented probabilistic multi-hypothesis tracker (TO/PMHT), which takes into account the distances between measures and targets, and calculate the true association probabilities. Thus, the weakness of PMHT of hospitality to clutters is greatly reduced. A comparison of the proposed TO/PMHT with measurement oriented PMHT and traditional data association algorithms, such as multi-hypothesis tracker (MHT) by computer simulation shows that TO/PMHT increases tracking performance and computation efficiency greatly.