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

Time correlated single photon counting (TCSPC) is a statistical method to generate time-correlated histograms (TC-Hists), which are based on the time-of-flight (TOF) 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, a neural network multi-classifier, and a distance recovery process, the analysis is focused on a small amount 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 the 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. Thus, it can be applied to improve the system robustness, especially in the case of high background light and middle-range detections.