Polarized laser target detection system for smoky environment based on full-waveform decomposition and multiscale convolutional neural networks with attention

Abstract

To overcome the problem that the smoky environment affects the laser detection performance and generates false alarm, this paper proposes a polarization pulse laser target detection method based on the difference of the polarization information between the target and the smoke, combining full waveform decomposition and Multiple Scale-Attention convolutional neural network with Focal loss function (MSAFCNN). Optimal parameters of the algorithm are obtained by studying the recognition accuracy of the algorithm with different sampling rates and the number of convolution kernels. The polarization pulse laser detection platform is built and the target detection experiments under five different scenarios using infrared lasers are conducted. The polarization echo signals of the smoke and the targets are collected and the recognition accuracy of the proposed algorithm is calculated. The ablation experiments are carried out and MSAFCNN is compared with several advanced algorithms. The experimental results show that the proposed algorithm has the highest recognition accuracy (99.085% ± 0.249%) compared with other algorithms. According to the ablation experiment, the recognition accuracy can be improved by 1.381% using MSAFCNN. The recognition experiments with different sampling rates and numbers of convolutional kernels are performed, and the accuracy of the proposed algorithm can reach 99.348% ± 0.178% at 1 GHz sampling rate and 98.910% ± 0.297% at 500 MHz sampling rate. The optimal number of convolutional kernels is 16, and the number of the algorithm parameters is only 125.273 KB, which provides the feasibility for the subsequent implementation of the algorithm in hardware.