【摘要】 针对高炮火控系统在小微型无人机(unmanned aerial vehicle, UAV)复杂机动跟踪中误差较大的问题，提出了基于自适应转移概率矩阵(adaptive transition probability matrix, ATPM)的交互式多模型改进强跟踪无迹卡尔曼滤波(interacting multiple model-improved strong tracking unscented Kalman filter, IMM-ISTUKF)算法。该算法结合多重渐消因子增强STUKF的滤波性能，并利用后验概率与似然函数信息自适应调整马尔可夫转移概率矩阵的每个元素。设计了基于模型极化校正与模型转移加速的双阶段ATPM更新机制，在模型稳定阶段抑制非匹配模型干扰，在模型切换阶段加快模型转换，降低了状态估计误差。仿真结果表明：所提算法比传统IMM算法及改进型IMM算法的跟踪精度更高，为高炮反无人机作战提供技术支撑。更多还原

【Abstract】 In response to the large tracking error of anti-aircraft fire control systems when tracking complex maneuver of small and micro unmanned aerial vehicle(UAV), an interacting multiple model-improved strong tracking unscented Kalman filter(IMM-ISTUKF) algorithm based on adaptive transition probability matrix(ATPM) was proposed. The proposed algorithm combined a multiple fading factor to enhance the filtering performance of STUKF, and adaptively adjusted each element of the Markov transfer probability matrix using posterior probability and likelihood function information. A two-stage ATPM update mechanism based on model polarization correction and model transfer acceleration was designed to suppress interference from mismatched models during the stable phase, and accelerate model switching during the transition phase, thereby reducing state estimation error. Simulation results demonstrate that the proposed algorithm achieves higher tracking accuracy than traditional IMM and improved IMM algorithms, providing technical support for anti-UAV operations of anti-aircraft gun system.更多还原