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AFAKF for manoeuvring target tracking based on current statistical model

机译：AFAKF用于基于当前统计模型的机动目标跟踪

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摘要

The fixed maximum acceleration of current statistical model (CSM) will lead to the deterioration of Kalman filter. To improve the performance of CSM in target tracking, a new modified CSM (MCSM) and a new Kalman filter (KF) are proposed. The new model, which employs innovation dominated subjection function to adaptively adjust maximum acceleration, has a better performance in target tracking, but it is very sensitive to innovation and will lead to a fluctuant phenomenon when target manoeuvres occur. The new adaptive fading Kalman filter which is formed by amendatory KF (AKF) and adaptive fading KF can weaken the fluctuant phenomenon caused by MCSM. The principle and deducing of AKF are specifically elaborated based on probability theory. Three simulations results indicate the high performance and robustness of MCSM and MCSM-adaptive fading amendatory Kalman filter in target tracking.
机译：当前统计模型（CSM）的固定最大加速度将导致卡尔曼滤波器的退化。为了提高CSM在目标跟踪中的性能，提出了一种新的改进的CSM（MCSM）和新的卡尔曼滤波器（KF）。新模型采用创新主导的主观功能来自适应地调整最大加速度，在目标跟踪方面具有更好的性能，但对创新非常敏感，并且在发生目标机动时会导致波动现象。由修正KF（AKF）和自适应衰落KF构成的新的自适应衰落卡尔曼滤波器可以减弱由MCSM引起的波动现象。基于概率论对AKF的原理和推论进行了详细阐述。三个仿真结果表明，MCSM和MCSM自适应衰落修正卡尔曼滤波器在目标跟踪中具有很高的性能和鲁棒性。

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