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弱小目标检测获得的量测数据由于各种不可预知因素的影响,包含了大量的误差和野值。同时由于系统和算法的敏感,微小的数据跳变就可以对目标信息的预测结果造成严重影响,引起跟踪偏差。针对这个问题,提出了一种利用样条逼近的分段多项式拟合思想和目标运动连续性的递推差分更新预测方法。首先根据样条函数和目标运动模型对已有数据进行平滑和预测,然后利用预测结果处理后续数据。这种方法能够实时地对目标信息数据的变化趋势进行更新,削弱误差和野值的影响,并根据此趋势预测后续数据。实验结果表明:此方法对存在突变的目标坐标数据具有很好的修正预测效果。
Weak target detection obtained measurement data due to a variety of unpredictable factors, contains a large number of errors and outliers. At the same time, due to the sensitivity of the system and the algorithm, slight data transition can seriously affect the prediction result of the target information and cause tracking deviation. To solve this problem, a piecewise polynomial fitting idea using spline approximation and a recursive differential update prediction method of target motion continuity are proposed. Firstly, the existing data is smoothed and predicted based on the spline function and the target motion model, and then the prediction result is used to process the subsequent data. This method can update the trend of target information data in real time, weaken the influence of error and outliers, and predict the follow-up data according to the trend. The experimental results show that this method has a good predictive effect on the target coordinate data with mutation.