Few Photon Detection Signal Processing Based on Neural Network
Geiger-mode avalanche photon diode(GM-APD)array improving the signal-to-noise ratio(SNR)has been widely concerned in laser communication and photonic radar.However,due to low transmitting power and strong background noise,the SNR is also low in low-photon detection signal processing.In order to solve the problem,we establish a mathematical model of signal processing based on time-domain and spatial-domain convolutional neural network.The model superimposes echo signals of adjacent four frames in the time domain.In the spatial domain,matrix dimension expansion algorithm is used to expand the convolution kernel dimension,and then echo photon signals are extracted through convolutional neural network.The results show that method can effectively extract the echo photon signal from the noise signal and improve the SNR by 4.5 times.This article can provide references for the hundred-kilometer low-photon detection signal processing.
few photon detectionGM-APDmatrix dimension expansionconvolutional neural network
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