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


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

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