异质传感器弱小群目标关联是传感器协同探测首先要解决的问题。即使在同视场下, 由红外光电系统和雷达组成的异质传感器探测目标也不完全一致, 特别是远距离探测时, 雷达探测目标多而密集,红外光电系统探测目标相对较少, 此时目标航迹关联结果具有很大不确定性。针对这一难题, 采用基于多源数据多特征融合的弱小目标关联方法, 首先基于多模型估计方法筛选同类型目标作为潜在关联目标, 再基于航迹关联算法对同类型目标粗关联, 最后基于多特征最大联合概率分布对目标精细关联。经红外光电系统/雷达同站址探测仿真试验验证, 相比于仅利用航迹进行目标关联, 该方法有效提高了弱小目标关联的准确性。

The dim and small target association of heterogeneous sensors is the first problem to be solved by cooperative detection of sensors. Even in the same field of view, the detection targets of heterogeneous sensors composed of infrared photoelectric systems and radar are not exactly the same, especially in the long-distance detection, the radar detection targets are many and dense, while the detection targets of infrared photoelectric systems are relatively few, so the target track association result has a great uncertainty. Aiming at this problem, the dim and small target association method based on multi-source data and multi-feature fusion was firstly proposed based on the multi-model estimation method, selecting the same type of targets as the potential association target, then making the rough association of the same type of targets based on the track association algorithm, and finally making fine association of targets based on the multi-feature maximum joint probability distribution. The simulation tests of infrared photoelectric systems/radar are verified by the same station site detection that this method effectively improves the accuracy of the association of dim and small targets compared with only using track for target association.