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

To address the problem of multiview extended target tracking (ETT) with self-occlusion, a Dempster–Shafer evidence theory with generalized covariance intersection (DS-GCI) multisource decision fusion is proposed. The proposed DS-GCI algorithm develops a multiview fusion framework for ETT 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 (DS) evidence theory framework. Second, the belief Jensen–Shannon (JS) 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 GCI with these dynamic weights. The simulation and experiment demonstrate that the proposed algorithm effectively improves the accuracy of multiview ETT, when local estimates are inconsistent due to sensor viewpoint differences and target self-occlusion.