针对基于传统融合机制的联合跟踪器在复杂环境下鲁棒性不足的缺陷, 提出一种在交互式多模型粒子滤波框架下传递概率矩阵可在线更新的自适应融合跟踪器。首先, 在贝叶斯理论框架下, 基于最小二乘误差估计法得到传递概率矩阵迭代更新方程; 然后, 利用数值积分法获得迭代更新方程的数值解; 最后, 结合重采样技术实现不同子跟踪器之间先验状态分布的自适应交互, 以确保传递权值较大粒子对应的目标状态。在复杂环境下进行了的跟踪实验, 结果验证了本文提出的自适应交互式融合机制增加了对粒子先验状态的校正功能, 有效避免了因误差积累导致的“跟踪漂移”问题, 使联合跟踪器的鲁棒性明显优于单一跟踪器或基于其它融合机制的联合跟踪器。

As collaborative trackers based on traditional fusion strategy has poor robustness in complex environments, a novel adaptive interactive fusion tracking strategy based on the online updated transition probability matrix in a multiple model particle filter framework was proposed. Firstly, an iterative updating equation was obtained based on minimum mean square error estimation method based on the Bayes theory. Then, the numerical solution of the iterative equation was obtained by numerical integration algorithm. Finally, with the updated TPM and re-sampling technology, the adaptive interaction of prior state distributions for different trackers was achieved to guarantee the target state of transmitted particles with larger weights. Tracking experiments were performed in complex environments. The results demonstrate that the proposed adaptive interactive fusion strategy improves the correction function for Particle prior state and effectively avoids the ‘tracking drifting’ problem from error accumulation. So, the robustness of proposed collaborative tracker is more better than those single trackers or collaborative trackers based other fusion strategy.