在机动目标跟踪中，“当前”统计模型(“Current”Statistical model, CS)需要预先依据经验设定机动频率和加速度极限值，当预先设定的值与目标的实际运动状态不一致时，将造成较大的跟踪误差。为克服上述问题，该文首先从“当前”统计模型的离散状态方程中，导出了一种机动频率自适应算法，然后对张安清及巴宏欣等人提出的加速度方差自适应算法进行了改进。仿真实验表明，在综合运用上述机动频率自适应和加速度方差自适应算法的基础上，对CS模型修改后，得到的机动目标跟踪自适应滤波算法(Mending CS based Adaptive Filtering algorithm, MAF)，能够有效增强基于CS模型的机动目标跟踪自适应滤波算法(CS based Adaptive Filtering algorithm, AF)对目标运动状态变化的自适应能力，并且在低噪声环境下，跟踪精度比 AF 算法有所提高，算法收敛速度可达到AF算法的2倍，在强噪声环境下，目标机动阶段的跟踪精度提高近2倍，匀速阶段的精度与AF算法相当，算法的收敛速度可达到AF算法的4～10倍，因此，MAF算法具有较强的抗干扰能力。% The approach of tracking maneuvering targets based on the“Current”Statistical (CS) model is widely used. The method needs to preset the maneuvering frequency and the maximum acceleration based on experience. In practice, the preset values are often not consistent with the actual moving state of targets and result in larger tracking errors. To tackle the problem, we initially deduce a self-adapting maneuvering frequency algorithm from the discrete-state equation of the CS model. Then, an improved self-adapting acceleration covariance algorithm is presented. Simulation results show that, by using the self-adapting maneuvering frequency algorithm and the improved self-adapting acceleration covariance algorithm to track targets simultaneously, we can improve the ability to self-adapt to the fluctuation of the moving state. The tracking accuracy is also improved, and the convergence speed of the algorithm is relatively quick.
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