基于改进自适应IMM-UKF算法的水下目标跟踪

针对现有自适应交互式多模型算法(AIMM)在水下目标跟踪过程中模型切换和跟踪精度上的不足，该文结合无迹卡尔曼滤波(UKF)算法，提出一种改进的AIMM-UKF算法。该算法在自适应修正马尔可夫转移概率矩阵的基础上，利用判定窗对其进行二次修正，实现匹配模型概率的快速增大和对非匹配模型的抑制。仿真结果表明，改进算法相比原有自适应算法，能更加充分地利用后验信息，拥有更好的模型切换速度，跟踪精度提升约24%。

Underwater Target Tracking Algorithm Based on Improved Adaptive IMM-UKF

To solve the lack of model switching and tracking accuracy of the existing Adaptive Interacting Multiple Model (AIMM) in the underwater target tracking, combined with the Unscented Kalman Filter, an improved AIMM-UKF algorithm is proposed. On the basis of adaptively modifying the Markov probability transition matrix, this algorithm uses the decision window to modify it twice to increase the probability of the matching model observably and reduce the effects of the mismatch model. Simulation results show that compared with the original adaptive algorithm, the improved algorithm can make fuller use of posterior information, has a better model switching speed, and improves tracking accuracy by about 24%.