Joint Matching and Fusion With Sensor Bias and Clutter for Decentralized Multitarget Tracking

In decentralized tracking systems, matching and fusion of target tracks in local sensors are keys to improve estimation performance. However, sensor measurements usually contain bias and clutter due to sensor performance or environmental influence, which makes it difficult to match local tracks correctly and obtain accurate tracking results. Moreover, the mutual influence of local track matching and sensor bias estimation further aggravates the difficulty. This article proposed a joint matching and fusion optimization framework to address the decentralized fusion problem for multitarget tracking systems with sensor bias and clutter. To deal with the impact of clutter, a direct relationship between local estimates and bias is obtained based on the joint probabilistic data association (JPDA) filter. A hypothesis test is applied in the matching detection of local tracks; the target matching results at relatively sparse locations are obtained with less computational cost. Then, soft matching and fusion estimation are iteratively implemented to gradually adjust the matching results and sensor bias estimates, in which the Kullback-Leibler (KL) distance is introduced to better measure the similarity between two local tracks. Finally, simulation results of multitarget tracking are provided to verify that the proposed method can obtain more accurate fusion and bias estimates and has lower computational complexity compared to other methods.