Robust framework for modelling long range dToF SPAD Lidar performance.

Area of Science:

• Photonics and Optical Engineering
• Advanced Sensing Technologies
• Computational Imaging

Background:

• Single-photon avalanche diode (SPAD) detector arrays are key for long-range 3D Lidar imaging.
• Realistic SPAD Lidar data may lack unbiased estimators, hindering performance evaluation.
• Existing frameworks have limitations in evaluating SPAD Lidar performance across wide parameter ranges.

Purpose of the Study:

• To introduce a novel metric, the Binomial Separation Criterion, for SPAD Lidar systems.
• To quantify the ability of depth estimation algorithms to reach the Cramér-Rao bound (CRB).
• To extend the performance evaluation of SPAD Lidar systems to a broader range of operating conditions.

Main Methods:

• Extension of an existing SPAD Lidar modeling framework.
• Inclusion of the Binomial Separation Criterion to assess depth estimation performance.
• Validation using experimental data from 532 nm and 1550 nm Lidar systems targeting objects at 1.4 km.

Main Results:

• The Binomial Separation Criterion effectively quantifies depth estimation performance.
• The framework enables evaluation across a significantly larger state space, including variations in photon counts and signal-to-noise ratio.
• Strong agreement was observed between the framework's predictions and experimental results.

Conclusions:

• The developed framework and Binomial Separation Criterion provide a versatile tool for SPAD Lidar research.
• This approach enhances the evaluation of Lidar systems in challenging environments and across diverse parameters.
• The findings are applicable to various SPAD Lidar configurations, wavelengths, and target types.