Feature extraction and neural network-based multi-peak analysis on time-correlated LiDAR histograms

Abstract

Abstract Time correlated single photon counting is a statistical method to generate time-correlated histograms (TC-Hists), which are based on the time-of-flight information measured by photon detectors such as single-photon avalanche diodes. With restricted measurements per histogram and the presence of high background light, it is challenging to obtain the target distance in a TC-Hist. In order to improve the data processing robustness under these conditions, the concept of machine learning is applied to the TC-Hist. Using the neural network-based multi-peak analysis (NNMPA), introduced by us, including a physics-guided feature extraction and a distance prediction process, the analysis is focused on a small number of critical features in the TC-Hist. Based on these features, possible target distances with correlated certainty values are inferred. Furthermore, two optimization approaches regarding learning ability and real-time performance are discussed. In particular, variants of the NNMPA are evaluated on both synthetic and real datasets. The proposed method not only has higher robustness in allocating the coarse position ( ± 5 % ) of the target distance in harsh conditions, but also is faster than the classical digital processing with an average-filter and noise suppression. Thus, it can be applied to improve the system robustness, especially in the case of high background light and middle-range detections.