自适应关联波门机动群目标跟踪算法

为解决中心群跟踪（CGT）算法中由于群机动造成的量测丢失、估计误差增大的问题，提出了一种基于自适应关联波门的机动群目标跟踪算法。首先，将CGT算法与交互式多模型（IMM）算法结合，并利用最新量测信息对IMM算法中的转移概率矩阵进行实时修正。其次，设计了一种用于整体机动和分离机动的自适应关联波门，根据机动时刻模型的新息协方差对其进行自适应调整，确保量测点迹进入波门。仿真结果表明，所提算法一方面减小了传统固定转移概率矩阵带来的估计误差，将优势模型的平均概率由0.58增加到了0.7；另一方面，设计的自适应关联波门有效解决了目标机动带来的有效量测减少的问题，相比于传统波门，目标失跟率减少了30%，具备工程实用性。 

Maneuvering group target tracking algorithm with adaptive correlation gate

A new maneuvering group target tracking algorithm based on adaptive correlation gate for solving measurement loss and increasing estimation error of centroid group tracking (CGT) algorithm when tracking maneuvering group target in clutter is proposed in this paper. First, CGT algorithm is combined with interacting multiple model (IMM) algorithm and the latest measurement information is used to modify the transition probability matrix. Second, a new adaptive correlation gate is designed when tracking overall and split maneuvering by the covariance of model innovation to guarantee valid measurements existing in the gate. The simulation results show that the proposed algorithm decreases the estimated error of traditional IMM algorithm with fixed transition probability matrix and increases the probability of dominant model from 0.58 to 0.7 on the one hand. On the other hand, the loss-target rate of adaptive gate designed in this paper is reduced by 30% compared to traditional gate on account of decreasing valid measurement during target maneuvering. The proposed algorithm has a certain practical value in engineering.