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针对于机动目标的跟踪问题,提出了一种基于交互式多模型的自适应去偏转换卡尔曼滤波器。该算法利用交互多模型算法来完成不同跟踪模型的相互切换;根据自适应去偏转换测量卡尔曼滤波算法来推导跟踪目标状态,同时自适应因子可以确保不正常测量时的鲁棒性。与传统的去偏转换卡尔曼滤波算法对比,该算法可以很好地改善所获量测信息在雷达被干扰时的目标跟踪精度。仿真结果表明了算法的有效性和可行性,且跟踪精度相对传统的去偏转换卡尔曼滤波算法减少9.38%的位置误差。
Aimed at tracking maneuvering targets, an adaptive de-skew-switched Kalman filter based on interactive multi-model is proposed. The algorithm uses interactive multi-model algorithm to complete the mutual switching between different tracking models. The adaptive de-skew measurement Kalman filter algorithm is used to derive the tracking target state. Meanwhile, the adaptive factor can ensure the robustness in abnormal measurement. Compared with the traditional de-shifted Kalman filter algorithm, this algorithm can well improve the target tracking accuracy of the acquired measurement information when the radar is jammed. The simulation results show the effectiveness and feasibility of the proposed algorithm, and the tracking accuracy is reduced by 9.38% compared with the traditional de-skew-switched Kalman filtering algorithm.