Image-Free Target Classification With Semiactive Laser Detection System

Abstract

Semiactive laser (SAL) guidance is a point-source detection technology without target feature extraction ability. In this article, the quadrant detector (QD)-based seeker and a spatially modulated designator are used in cooperation to achieve image-free target classification via a hybrid convolutional neural network (CNN) architecture. The convolutional layer is implemented optically by illuminating the target with training-obtained modulated patterns, and the detected intensity signals are regarded as compressed target features. The convolutional layers and classification net are trained together on an electronic platform. Layer normalization (LN) is adopted to improve network generalization ability and accelerate the training process. Network hyperparameters such as the channel numbers and kernel sizes of convolutional layers are analyzed and selected. The feasibility and robustness of the network are verified by both numerical simulation and experiments, whose classification accuracies are 88% and 66%, respectively. The proposed SAL detection and classification system is calculation-efficient and convenient to be adopted in existing guidance platforms or other real-time applications with limited computing resources.