A High-Resolution and Efficient Waveform Decomposition Method for Small-Footprint LiDAR

Small-footprint waveform light detection and ranging (LiDAR) necessitates waveform decomposition for accurate target structure characterization. However, the limited range resolution and heavy computations lead to this being hindered. Addressing these challenges, we propose a high-resolution and efficient waveform decomposition method on the fundamentals of the LiDAR physics model. To enhance LiDAR ranging resolution, we introduce a novel technique that separates the transmitted pulses and received multitarget waveforms to simulate narrow transmitted pulse conditions. Then, the separated pulses and waveforms are input into a deconvolution algorithm, which incorporates an automatic stopping criterion for iteration to ensure accurate results. For efficient processing of waveforms, we design a lightweight classification method that categorizes waveforms into single-target and multitarget waveforms before waveform decomposition, with only the latter undergoing complex downstream processing. Indoor and airborne experiments are conducted on datasets collected using small-footprint full-waveform LiDAR. The indoor results demonstrate that the average target distance error is reduced to 0.064 m with a significant improvement in efficiency, surpassing mainstream methods. The airborne results reveal that our method is able to decompose faster to get more points for better structural characterization.