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Adaptive single photon detection under fluctuating background noise.

Zhen Chen, Bo Liu, Guangmeng Guo

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    This study introduces an adaptive single photon detection method for lidar systems facing changing background noise. The technique achieves high sensitivity and low false alarm rates, improving remote sensing performance.

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    Area of Science:

    • Photonics
    • Remote Sensing
    • Signal Processing

    Background:

    • Single photon lidar systems are crucial for remote sensing.
    • Fluctuating background noise degrades lidar performance, impacting sensitivity and accuracy.
    • Existing methods struggle to maintain optimal performance under dynamic noise conditions.

    Purpose of the Study:

    • To develop an adaptive thresholding method for single photon detection.
    • To improve the performance of single photon lidar in environments with fluctuating background noise.
    • To enable simultaneous high sensitivity and low false alarm rates.

    Main Methods:

    • An adaptive threshold is determined in real-time based on background noise levels.
    • The method adjusts detection parameters to maintain a desired false alarm probability.
    • Experimental validation was conducted to assess the proposed technique's efficacy.

    Main Results:

    • The adaptive method successfully enhances single photon lidar performance under fluctuating noise.
    • Experimental results closely align with theoretical predictions.
    • The system achieved simultaneous high sensitivity and a low false alarm rate.
    • Mean echo photon counts were accurately estimated with an error of less than one photon.

    Conclusions:

    • The proposed adaptive single photon detection method is effective for lidar in noisy environments.
    • This approach significantly improves the reliability and accuracy of remote sensing applications.
    • The ability to estimate echo photon counts adds valuable quantitative data.