杂波环境下, 现有的最近邻数据互联算法在高采样率雷达跟踪慢速目标时, 由于量测误差远远大于目标实际运动的距离, 量测值往往难以落入预测波门之内, 从而造成数据互联失败。针对这一问题, 提出了一种自适应距离最近邻数据互联算法。该算法用状态估计向量中的位置信息计算与下一时刻量测值的实际距离来代替预测中心与量测值的统计距离, 降低了预测中心不确定性造成的漏互联概率, 从而提高了跟踪精度。实际距离波门的大小与雷达和目标距离、雷达测距精度、雷达测角精度有关, 并且随着时间实时变化。通过在地心坐标系下的跟踪滤波仿真证明, 该算法能够有效地实现高数据率雷达对慢速目标的跟踪, 且跟踪精度较高, 具有一定的工程实用价值。

The radar with high sampling rate always fails to track a slow moving target when using nearest neighbor data association under the clutter background, since its measuring error is much greater than the real moving distance of the target.To improve tracking precision to the slow moving target, an adaptive range nearest neighbor data association algorithm is proposed.This method uses the location information of the state vector to calculate the real range instead of calculating the statistical range of prediction center and measurements, the tracking precision is improved by reducing the missing association probability.The real range gate size relates to measuring error and the distance of radar and target.Simulation results show that the algorithm can enable the radar with high sampling rate track the low-moving target effectively with relatively high precision.