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Lidar has emerged as an important technology for the high-precision three-dimensional remote sensing of the ocean. While oceanic lidar has been widely deployed on various platforms, its underwater deployment is relatively limited, despite its significance in deep-sea exploration and obstacle avoidance for underwater platforms. Underwater lidar systems must meet stringent requirements for high performance, miniaturization, and high integration. Single-photon lidar, by elevating the detection sensitivity to the single-photon level, enables high-performance detection under the condition of a low-pulse-energy laser and a small-aperture telescope, making it a stronger candidate for underwater lidar applications. However, this imposes demanding requirements for the data acquisition system utilized in single-photon lidar systems. In this work, a self-developed multi-channel acquisition system (MCAS) with a high-resolution and real-time histogram statistics capability was developed. By utilizing field-programmable gate array (FPGA) technology, a method that combines coarse counters with multi-phase clock interpolation achieved an impressive resolution of 0.5 ns and enabled a time of flight duration of 1.5 μs. To address counting instability, a dual-counter structure was adopted in the coarse counter, and real-time histogram statistics were achieved in the data acquisition system through a state machine. Furthermore, the non-uniform phase shift of the clock was analyzed, and a correction algorithm based on code density statistics was proposed to mitigate the periodic modulation of the backscattered signal, with the effectiveness of the algorithm demonstrated through experimental results. The robustness and stability of the MCAS were validated through an underwater experiment. Ultimately, the development of this compact acquisition system enables the implementation of underwater single-photon lidar systems, which will play a crucial role in underwater target imaging, obstacle avoidance in underwater platforms, and deep-sea marine environment monitoring.
Lin et al. (Thu,) studied this question.