Dead Time Compensation for High-Flux Ranging

TL;DR

This paper introduces a statistical model and optimization-based method to compensate for dead time effects in high-flux lidar ranging, enabling accurate depth estimation with shorter data acquisition times.

Contribution

It develops a Markov chain-based statistical model for detection times under dead time, and formulates a nonlinear inverse problem for improved high-flux ranging accuracy.

Findings

Empirical detection time distribution converges to a stationary Markov chain distribution.

The proposed method reduces ranging error in high-flux regimes.

Simulation results demonstrate improved depth estimation performance.

Abstract

Dead time effects have been considered a major limitation for fast data acquisition in various time-correlated single photon counting applications, since a commonly adopted approach for dead time mitigation is to operate in the low-flux regime where dead time effects can be ignored. Through the application of lidar ranging, this work explores the empirical distribution of detection times in the presence of dead time and demonstrates that an accurate statistical model can result in reduced ranging error with shorter data acquisition time when operating in the high-flux regime. Specifically, we show that the empirical distribution of detection times converges to the stationary distribution of a Markov chain. Depth estimation can then be performed by passing the empirical distribution through a filter matched to the stationary distribution. Moreover, based on the Markov chain model, we formulate the recovery of arrival distribution from detection distribution as a nonlinear inverse problem and solve it via provably convergent mathematical optimization. By comparing per-detection Fisher information for depth estimation from high- and low-flux detection time distributions, we provide an analytical basis for possible improvement of ranging performance resulting from the presence of dead time. Finally, we demonstrate the effectiveness of our formulation and algorithm via simulations of lidar ranging.