Multitarget Federated Fusion Tracking Using Heterogeneous Sonar Measurements
Xue Yu, Xi'an Feng · IEEE Sensors Journal · 2024
To achieve optimal multitarget fusion tracking using an active sonar’s kinematic, spread angle measurements, and a passive sonar’s bearing-only measurements, a federated fusion algorithm of Gaussian mixture probability hypothesis density (GM-PHD) filters is proposed. Our algorithm features a hierarchical structure consisting of a fusion node and two sonar nodes. Based on a newly defined target state, the observation model of spread angle measurements is established as the premise of fusing this data. Two local filters of sonar nodes transform heterogeneous measurements into isomorphic state estimates, erasing data heterogeneity. A global filter of the fusion node can supplement missed detections. Multitarget density fusion is decomposed into multiple single-target estimate fusion by estimate association. We derive the optimal single-target estimate fusion methods in the absence and presence of missed detections. The covariance upper bounding technique is introduced to eliminate inevitable correlations. Simulations demonstrate that our algorithm outperforms the existing centralized and distributed heterogeneous fusion tracking algorithms, and the relative weights of the global and local filters can be flexibly adjusted.