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本文应用控制理论分析了R.A.Singer零均值模型在跟踪机动目标方面的局限性;提出了一种目标加速度的截断正态概率密度模型,并将目标加速度模拟为非零均值、时间相关的随机过程;借助于切比雪夫(CHEBYSHEV) 不等式来确定目标加速度的均值和方差之间的关系,并引用卡尔曼滤波器所包含的目标机动的统计信息来实现方差自适应算法。 理论分析表明,本文的模型和算法跟踪恒加速目标时稳态偏差为零,从而消除了Singer方法在跟踪恒加速目标时所存在的稳态偏差。 计算机仿真结果证实了理论分析的结论,表明本文提出的模型和采用的算法能够较好地跟踪高度机动目标。
In this paper, the limitations of RASinger zero-mean model in tracking maneuvering targets are analyzed based on control theory. A truncated normal probability density model of target acceleration is proposed and the target acceleration is simulated as a non-zero mean and time-dependent stochastic process. The relationship between the mean and the variance of the target acceleration is determined by means of CHEBYSHEV inequality and the variance adaptive algorithm is implemented by referencing the target maneuver statistical information contained in the Kalman filter. The theoretical analysis shows that the steady-state deviation of the Singer method in tracking a constant acceleration target is eliminated by tracking the constant acceleration target with the model and algorithm in this paper. Computer simulation results confirm the theoretical analysis of the conclusions show that the proposed model and the algorithm used to better track highly maneuvering targets.