APD optimal bias voltage compensation method based on machine learning

Abstract The signal-to-noise ratio (SNR) of avalanche photodiode (APD) in optical detection system is greatly influenced by background radiation and operating temperature. So an APD optimal bias voltage compensation method based on machine learning is designed to accurately judge the current laser emission state and APD working state so that dichotomy compensation can be carried out to make APD work in optimal state. By means of cross-verification, the accuracy of judging laser emission state and APD working state is as high as 100% and 99.3% separately. Then the number of input variables in the model is reduced appropriately by experiment and the prediction speed of the algorithm is further improved. Finally, road detection application is taken as the experimental background and comparison between the proposed method and the most widely used signal amplitude feedback compensation method is carried out. The results of this study suggest that the proposed APD optimal bias voltage compensation method based on machine learning offers a new and promising approach.