Multisource Decision Fusion for Multiview Extended Target Tracking With Self-Occlusion

To address the problem of multi-view extended target tracking with self-occlusion, a Dempster–Shafer evidence theory with generalized covariance intersection (DS-GCI) multi-source decision fusion is proposed. The proposed DS-GCI algorithm develops a multi-view fusion framework for extended target tracking under conditions where sensor observations are asynchronous and local estimates are inconsistent due to target self-occlusion. First, a confidence evaluation strategy based on the observation angle interval and Gaussian kernel similarity is employed to quantify the reliability and consistency of local estimates. The resulting quantified measures are then fused to construct the initial basic probability assignment (BPA) within the Dempster–Shafer evidence theory framework. Second, the belief Jensen–Shannon divergence and Deng entropy are jointly used to characterize conflicts and information among local estimates, and are further employed to adjust the initial BPA. If the difference between the adjusted and initial BPAs exceeds a certain threshold, the adjusted BPA is used as the dynamic fusion weights. Otherwise, the weights are derived using Dempster’s combination rule. Finally, the global estimate is computed using the generalized covariance intersection with these dynamic weights. Simulation and experiment demonstrate that the proposed algorithm effectively improves the accuracy of multi-view extended target tracking, when local estimates are inconsistent due to sensor viewpoint differences and target self-occlusion.