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.