自适应门限GM-CPHD多目标跟踪算法

带有势估计的高斯混合概率假设密度滤波（GM-CPHD）作为一种杂 波环境下目标数可变的检测前跟踪方法，将复杂的多目标状态空间的运算转换为单目标状态 空间内的运算，有效避免了多目标跟踪中复杂的数据关联问题，但该方法的计算复杂度与观 测数的3次方成正比，在密集杂波情况下计算量十分巨大。针对该方法计算复杂度高的问题 ，提出利用一种最大似然自适应门限的快速算法，该算法首先利用自适应门限对观测进 行处理，然后仅利用处于门限内的有效观测进行GM-CPHD算法的更新步计算，大大降低了算 法的计算复杂度。实验结果证明，本文方法在有效降低计算复杂度的同时，在多目标跟踪效 果 方面与GM CPHD相当，优于GM-PHD滤波算法。

Adaptive Gating GM-CPHD Filter for Multitarget Tracking

The Gaussian mixture cardinalized probability hypothesis density fi lter (GM-CPHD) is a recursive Bayesian filter for track-before-detect multita rge t tracking algorithm in clutter, which propagates the first moment of the multi target posterior density, incorporating track initiation and termination without consideration of measurement-to-track association. Due to the fact that GM-C PHD filer has a great computational complexity: (nm3), where n is the nu mber of targets and m is the cardinality of measurement set, an adaptive gating alg orit hm is proposed . The algorithm reduces the measurement set by using a maximum likelihood adaptive gate, and only the measurements falling into the gate are us ed to update the PHD estimation. Simulation results show that the proposed algor ithm reduces the computational complexity obviously, and obtains a similar perf ormance.