通过分析传统卡尔曼滤波方法在复杂数据环境应用中遇到的问题,提出了基于单雷达加权直线航迹线参数估计模型的目标运动状态分量合成估计方法。该方法基于复杂数据环境,无须获取系统噪声和观测噪声等先验知识,充分利用目标处于匀速直线运动状态这一特点,分别对当前有限测量点的X、Y 分量进行相对于的测量时刻的垂直距离加权迭代估计,确定目标状态估计参数。通过对比试验,验证了文中所提的方法比传统卡尔曼方法具有更优的目标状态估计效果、测量误差平滑和野值抑制能力,能有效提高观测样本较少时目标状态参数估计的准确性。

By analyzing the problems in the application of traditional Kalman filter in complex data environment, a synthesis estimation of target state component based on weighted linear track parameter estimation model of single radar is presented in this paper. The method is based on complex data environment and has no need to acquire prior knowledge of system noise and detected noise. To make full use of the feature that the target is in a uniform linear motion state, the vertical distance of X and Y components of current limited measurement points is estimated iteratively, and the filtering method of target state estimation parameters is determined. Compared with the traditional Kalman filter, the proposed method has better performance in target state estimation, measurement error smoothing and outlier suppression, and effectively improves the accuracy of target state parameter estimation with fewer observation samples.

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