在传统激光主动成像系统的基础上, 结合目标识别技术搭建了一个激光主动成像识别系统实验平台, 用于研究激光主动成像后的目标识别。介绍了实验平台的工作原理, 基于Hu矩特征的双隐含层BP神经网络算法以及实验处理流程和实验结果。特征量由7个不变Hu矩构成, 通过240张原始目标样本库对由136个权值系数构成的双隐含层BP神经网络算法进行了训练。利用训练好的双隐含层BP算法对黑夜条件下远处的运动目标--43式冲锋模具枪进行了实验研究, 成功获得了清晰的红外激光主动成像效果。实验显示对450 m处2 740帧和550 m处2 420帧激光主动成像图像的统计识别率达到了68.87%和72.11%, 其中旋转变换下的统计识别率可达80.05%和84%, 好于仿射变换的识别效果。

An experiment platform for laser active imaging and recognition was established based on the traditional laser active imaging system to investigate the target recognition after laser active imaging. The working mechanism of the platform was introduced and the Hu moment feature based BP neural network algorithm with double hidden layers and an experimental process were given. The target feature vector was consisted of seven invariant Hu moments. The BP neural network algorithm with double hidden layers including 136 weight coefficients was trained by 240 original sample libraries. The trained BP neural network algorithm was used to research a distance moving target in the dark condition, a model of 43 submachine gun, and a clear infrared laser active image was obtained. Experiment results show that statistical recognition probability is 68.87% for 2 740 frames of images at 450 m and 72.11% for 2 420 frames of images at 550 m. The corresponding recognition probabilities from rotation transformation are 80.05% and 84%, respectively, which is better than the results by affine transformation.