Abstract When imaging through fog using Geiger mode avalanche photon diode (Gm-APD) ladar, strong backscattering photons make it difficult to effectively extract an accurate target signal via the traditional peak-select algorithm. Single-parameter estimation (SPEA) is proposed in this paper to solve this problem. First, the physical properties of the gamma distribution model’s characteristics are analyzed, and the model is improved based on Gm-APD ladar’s counting characteristics. Next, a Monte Carlo simulation for photons propagating in fog is conducted. The SPEA’s backscattering distribution has the highest correlation with the original backscattering photon distribution. Realistic imaging data are processed through fog, and the SPEA has the best recovering effect, achieving 71% recovery using 20,000 frames. This study demonstrates that the SPEA enables imaging through fog with Gm-APD ladar.

中文翻译：

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Gm-APD激光透雾成像单参数估计构建算法

摘要 使用盖革模式雪崩光子二极管（Gm-APD）激光雷达在雾中成像时，强反向散射光子使得传统的峰值选择算法难以有效提取准确的目标信号。为了解决这个问题，本文提出了单参数估计（SPEA）。首先，分析了伽马分布模型特征的物理性质，并根据Gm-APD激光雷达的计数特征对模型进行了改进。接下来，进行了光子在雾中传播的蒙特卡罗模拟。SPEA 的背向散射分布与原始背向散射光子分布的相关性最高。真实影像数据经过雾化处理，SPEA恢复效果最好，20000帧恢复71%。