PMT gain self-adjustment system for high-accuracy echo signal detection

ABSTRACT

The intensity difference between the echo signals from the water surface and bottom during bathymetry LiDAR operation requires a photomultiplier tube (PMT) gain self-adjustment. Otherwise, noise echo signals are collected, while weak and useful signals are not. For this reason, this paper proposes a PMT gain self-adjustment system for the high-accuracy detection of LiDAR echo signals. The developed system uses a field programmable gate array (FPGA) collector as a feedback signal generator, an STM32 controller as the PMT gain and voltage control signal generator and a DA module as the PMT gain voltage conversion circuit. The system controls the PMT gain voltage by judging the feedback signal to achieve PMT gain self-adjustment. The system was verified in an indoor tank, building roof, and outdoor pond experiments. By comparing the experiments, the developed system is shown to detect laser energy intensity with a sensitivity of at least 2.26 times stronger than the traditional system, and can measure water depth at least 2.5 times deeper than the traditional system. Therefore, it can be concluded that the proposed PMT gain self-adjusting system can effectively adapt to the changes in laser energy, improve the measurement of water depth, control the amplitude of the echo signals, increase the accuracy of water depth detection, reduce the saturation of the PMT detector, and protect PMT from damages.

KEYWORDS:

• LiDAR
• FPGA
• STM32
• PMT gain self-adjustment

Acknowledgments

This paper is financially supported by the Guangxi Innovative Development Grant Program (the grant #: Guide AD19254002); Guangxi Natural Science Foundation for Innovation Research Team (the grant #: 2019GXNSFGA245001); the BaGuiScholars programme of Guangxi, and the Open Fund of Guangxi Key Laboratory of Spatial Information and Mapping (the grant #: 19-050-11-13); the National Natural Science of China (the grants #: 41961065), Guilin Research and Development Plan Program (the grant #: 20190210-2).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the the Guangxi Innovative Development Grant Program [Guike AD19254002]; the National Natural Science of China [41961065]; the BaGuiScholars programme of Guangxi, and the Open Fund of Guangxi Key Laboratory of Spatial Information and Mapping [19-050-11-13]; Guilin Research and Development Plan Program [20190210-2]; Guangxi Natural Science Foundation for Innovation Research Team [2019GXNSFGA245001].