Image-free target identification using a single-point single-photon LiDAR.

Abstract

Single-photon light detection and ranging (LiDAR) - offering single-photon sensitivity and picosecond temporal resolution - has become one of the most promising technologies for 3D imaging and target detection. Generally, target detection and identification requires the construction of an image, performed by a raster-scanned or an array-based LiDAR system. In contrast, we demonstrate an image-free target identification approach based on a single-point single-photon LiDAR. The idea is to identify the object from the temporal data equipped with an efficient neural network. Specifically, the target is flood-illuminated by a pulsed laser and a single-point single-photon detector is used to record the time-of-flight (ToF) of back-scattering photons. A deep-learning method is then employed to analyze the ToF data and perform the identification task. Simulations with indoor and outdoor experiments show that our approach can identify the class and pose of the target with high accuracy. Importantly, we construct a compact single-point single-photon LiDAR system and demonstrate the practical capability to identify the types and poses of drones in outdoor environments over hundreds of meters. We believe our approach will be useful in applications for sensing dynamic targets with low-power optical detection.