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本文提出机动目标“当前”统计模型的概念并建议用修正的瑞利-马尔科夫过程描述目标随机加速机动的统计特性。文中指出了在机动目标运动模型中状态(机动加速度)估值与状态噪声之间的内在联系。在此基础上提出了具有机动加速度均值及方差自适应的卡尔曼滤波算法。对一维和三维的情形进行了计算机模拟。计算结果表明,在仅对目标位置进行观测的情况下,这类自适应估值算法无论对高度机动或无机动的目标均可绘出较好的位置、速度及加速度估值。
This paper proposes the concept of “current” statistical models of maneuver targets and proposes to describe the statistical properties of the target stochastic accelerating maneuvers using a modified Rayleigh-Markov process. The paper points out the intrinsic relationship between state (maneuvering acceleration) estimation and state noise in maneuvering target motion model. Based on this, a Kalman filter algorithm with maneuvering acceleration mean and variance adaptive is proposed. One-dimensional and three-dimensional computer simulations were performed. The calculation results show that, in the case of observing only the target position, this kind of adaptive estimation algorithm can draw a better estimation of position, velocity and acceleration for highly maneuvering or maneuvering targets.