Full-waveform LiDAR echo decomposition based on dense and residual neural networks

For full-waveform (FW) LiDAR signals, conventional echo decomposition methods use complicated filtering or de-noising algorithms for signal pre-processing. However, the speed and accuracy of these algorithms are limited. In this paper, we study a highly efficient and accurate decomposition method based on the FW dense connection network (FDCN) or FW deep residual network (FDRN). FDCN is a lightweight and efficient network for SNR higher than 24 dB, while FDRN is a deeper neural network with multiple residual blocks and works well for low SNR such as 12 dB. We compare FDCN and FDRN with other conventional methods. With FDCN and FDRN, the mean error for estimating an echo peak location is under 0.2 ns, while the amplitude error is under 5 mV when the dynamic range is 0 ∼ 100 mV. Both errors are much lower than the values using conventional methods.