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Interacting multiple model tracking algorithm fusing input estimation and best linear unbiased estimation filter

机译:融合输入估计和最​​佳线性无偏估计滤波器的交互多模型跟踪算法

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摘要

In manoeuvring target tracking, a primary tradeoff is the robust tracking of manoeuvres against the accurate tracking of constant velocity (CV) motion. To achieve this goal, an interacting multiple model (IMM) algorithm fusing input estimation (IE) and best linear unbiased estimation (BLUE) filter is presented. First, the constant input assumption of IE is modified to track possible manoeuvre fluctuation. Then, an innovation sequence modification technique is proposed so manoeuvre can be detected and estimated sequentially. With improvements above, a modified input estimator (MIE) is designed to track varying manoeuvres. In view of the optimal tracking of BLUE filter for CV motion, MIE and BLUE filter are fused within IMM framework to generate an overall adaptive tracking algorithm. Simulation results reveal the proposed approach yields better accuracy for CV tracking and robustness for manoeuvre tracking.
机译:在机动目标跟踪中,主要的权衡是对机动的鲁棒性跟踪与对等速(CV)运动的精确跟踪。为了实现这一目标,提出了一种融合了输入估计(IE)和最佳线性无偏估计(BLUE)滤波器的交互多模型(IMM)算法。首先,修改IE的恒定输入假设以跟踪可能的操纵波动。然后,提出了一种创新序列修改技术,以便可以依次检测和估计机动。通过以上改进,设计了一种改进的输入估计器(MIE)来跟踪变化的动作。考虑到针对CV运动的BLUE滤波器的最佳跟踪,将MIE和BLUE滤波器融合在IMM框架中以生成总体自适应跟踪算法。仿真结果表明,该方法在CV跟踪中具有更好的准确性,在机动跟踪中具有更好的鲁棒性。

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