Single-parameter estimation construction algorithm for Gm-APD ladar imaging through fog

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.

Automated summary

This paper proposes Single-parameter Estimation (SPEA) to effectively extract accurate target signals when imaging through fog using Geiger mode avalanche photon diode (Gm-APD) ladar. By analyzing the physical properties of the gamma distribution model, improving the model based on Gm-APD ladar's counting characteristics, and conducting Monte Carlo simulation for photons propagating in fog, SPEA demonstrates the best recovering effect with 71% recovery using 20,000 frames. This study shows that SPEA enables imaging through fog with Gm-APD ladar.