介绍了一种新型大视场、高灵敏度的复眼位标器，用于探测低空飞行目标并快速获取目标物体的运动参数。叙述了复眼模型的结构和制作方法，并利用Zemax对子眼成像通道光线追迹考察其成像特性。介绍了复眼标定与探测方法。采用基于LM算法的神经网络训练，建立各个通道精确的物像对应关系。为了检验神经网络标定算法的效果，采用传统的基于二次多项式拟合的算法进行校正对比。仿真结果表明，神经网络算法可以提供更好的精度，从像点可以准确地预测主光线的方向角(10-3~10-4 rad)，而且具有易于集成，方便快捷的特点。此外，进行了目标定位检测的仿真实验，计算了若干目标点的坐标，结果表明各个坐标相对误差均在3%以内，对于牺牲空间横向分辨率来提升视场角的复眼光学系统，该结果符合目标探测的要求。

A new compound eye model with wider fields of view and higher agility was introduced to track the low-flying targets in a complex background. The struction and preparation of the compound eye model were described and its imaging channels were traced by Zamax to evaluated imaging characteristics.The calibration and detection of the compound eye were introduced,then LM neural network calibration algorithm was trained to build the relationship between object points and corresponding image points. This calibration algorithm provides an accurate direction angle prediction from their corresponding image points, and it is easy to integrate into the system. Preliminary experimental results for neural network calibration were presented and evaluated, which shows that the residual errors between actual and measured direction angles are around 10-3~10-4 rad. In detection simulation experiments, several points were calculated and results show that the errors between actual and calculated coordinates of position are within 3%. This is a good result for the compound eye sensor that sacrifices the spatial resolution to improve the angle resolution.