2D Signal Estimation for Sparse Distributed Target Photon Counting Data

TL;DR

This paper introduces a maximum likelihood estimation method using Poisson Total Variation processing for high-resolution 2D photon counting data in lidar systems, improving accuracy over traditional histogram methods.

Contribution

It adapts Poisson Total Variation techniques for sparse photon counting data, enabling high-resolution signal recovery with superior accuracy in distributed target lidar applications.

Findings

Achieves high temporal (50 Hz) and range (75 cm) resolution in photon counting data.

Demonstrates superior accuracy over conventional histogram-based methods.

Validated on both simulated and real-world atmospheric data.

Abstract

In this study, we explore the utilization of maximum likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.