一种状态扩维标签匹配的分布式融合算法

在分布式多传感器网络中，针对标签多伯努利（Labeled Multi-Bernoulli，LMB）后验分布进行广义协方差交集（Generalized Covariance Intersection，GCI）融合时，存在标签不一致、计算复杂度高、以及目标漏跟使得GCI融合后势低估问题，提出一种状态扩维标签匹配的分布式传感器融合算法。首先，针对标签不一致问题，对目标状态进行扩维，改进分布式融合中的目标标签的匹配过程，使融合过程更加高效，同时也克服标签空间不一致的问题；针对计算复杂度高的问题，只传输“疑似目标”后验分布，减少通信数据量，采用“分而治之”的策略对已匹配的存活目标、新生目标、漏跟目标等分别进行融合，结合前述改进目标标签匹配过程有效降低了计算复杂度；针对目标漏跟使得GCI融合后势低估问题，建立漏跟与虚警表记录相应目标，对漏跟目标分布采用反馈补偿策略，有效降低单一传感器目标漏跟对传感器网络GCI融合后跟踪精度的影响。实验结果证明了提出融合方法的有效性和鲁棒性。 

A Distributed Fusion Algorithm Based on State-extended Label Matching

? ?For the problems of label inconsistencies among different sensors and high computational complexity with cardinality underestimation by using the Generalized Covariance Intersection （GCI） method for the distributed multi-sensor multi-target tracking based on the labeled multi-Bernoulli （LMB）. We present a distributed fusion algorithm of state-extended label matching under the labeled multi-Bernoulli filter framework. First， in order to overcome label inconsistencies and reduce label matching computation， a variable is extended in state vector for recording the matching history when the first label is matched. Then only the target-like LMB components are transmitted for fusion among the sensors and the “divide and conquer” strategy is introduced for fusing surviving targets， newborn targets， and misdetection targets. Moreover， the misdetection targets and false alarm targets are recorded on a table and the misdetection target posteriors fed back for compensation， which can effectively alleviate the degradation of GCI fusion accuracy caused by target misdetection. Finally， the experimental results prove the effectiveness and robustness of the proposed method.