Sampling of Time-Resolved Full-Waveform LIDAR Signals at Sub-Nyquist Rates

Third-generation full-waveform (FW) light detection and ranging (LIDAR) systems collect time-resolved 1-D signals generated by laser pulses reflected off of intercepted objects. From these signals, scene depth profiles along each pulse path can be readily constructed. By emitting a series of pulses toward a scene using a predefined scanning pattern and with the appropriate sampling and processing, an image-like depth map can be generated. Unfortunately, massive amounts of data are typically acquired to achieve acceptable depth and spatial resolutions. The sampling systems acquiring this data, however, seldom take into account the underlying low-dimensional structure generally present in FW signals and, consequently, they sample very inefficiently. Our main goal and focus here is to develop efficient sampling models and processes to collect individual time-resolved FW LIDAR signals. Specifically, we study sub-Nyquist sampling of the continuous-time LIDAR FW reflected pulses, considering two different sampling mechanisms: 1) modeling FW signals as short-duration pulses with multiple band-limited echoes; and 2) modeling them as signals with finite rates of innovation.