基于强跟踪滤波器估计的最优融合方法

在分布式雷达数据处理模式中 ,数据融合是获得较精确的目标轨迹的主要环节。为克服卡尔曼滤波器对初始值敏感、鲁棒性差和对机动目标跟踪性能差的缺陷 ,通过利用各传感器的观测数据 ,采用强跟踪滤波器对目标进行跟踪 ,以改善目标状态估计的精度。对判定源于同一目标的状态估计值 ,给出了一种估计状态线进行性组合的最优融合准则。得出了实际数据的实验结果。

Optimum Fusion Method Based on Strong Tracking Filter Estimations

In the pattern of distributive radar data processing, the data fusion is an important tache for getting more accurate tracks. In order to overcome the shortcomings of Kalman or extended Kalman filter such as the estimate being more sensitive to initialization, not being robust and performance of tracking maneuver target not being well. The strong tracking filter is used to tracking a target, and then various filtered states from the same target have been fused in order to obtain a more accurate state and flight track. A linear combination's optimum fusion rule is presented in the paper. Finally the testing results based on the practical data are achieved.