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摘要: 传统的多假设跟踪(Multiple Hypothesis Tracking, MHT)算法通常假设一个目标独立地产生一个量测。但在实际观测场景中，当多个目标之间足够接近时，分辨率有限的传感器只能识别出一个未分辨的量测。这种现象使得数据关联问题更加复杂，跟踪算法性能明显下降。针对这一问题，本文提出了一种可适应未分辨量测的改进随机化贪心-自适应搜索结构MHT(Greedy Randomized Adaptive Search Procedure MHT, GRASP-MHT)算法，推导了关联未分辨量测的航迹假设得分，将未分辨量测的数据关联问题建模成最大权重独立集问题(Maximum Weight Independent Set Problem, MWISP)，以适应可能存在未分辨量测的场景。仿真结果表明，改进GRASP-MHT能够处理未分辨量测的数据关联问题，并且保留了GRASP-MHT的大部分优点。Abstract: In conventional multiple hypothesis tracking (MHT) algorithm, a target is assumed to generate one measurement independently. In practical scenario, however, closely spaced multi-target may be identified as one unresolved measurement due to limited resolution. This phenomenon complicates the data association problem and badly degrades the tracking performances. In order to solve this problem, an improved greedy randomized adaptive search procedure MHT (GRASP-MHT) algorithm is proposed. To adapt to scenarios may contain unresolved measurements, the new algorithm derived the score of track hypothesis associated with unresolved measurements and modeled the complex data association problem as a maximum weight independent set problem (MWISP). Simulation results demonstrate that the improved GRASP-MHT can solve the data association problem with unresolved measurements and retains most of the advantages of GRASP-MHT.
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