Abstract-Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyze the response from a histogram of either photon counts or integrated intensities to assess the number, positions, and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterization of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework, which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far-range depth imaging.

We demonstrate subcentimeter depth profiling at a stand off distance of 330 m using a time-of-flight approach based on time-correlated single-photon counting. For the first time to our knowledge, the photoncounting time-of-flight technique was demonstrated at a wavelength of 1550 nm using a superconducting nanowire single-photon detector. The performance achieved suggests that a system using superconducting detectors has the potential for low-light-level and eye-safe operation. The system's instrumental response was 70 ps full width at half-maximum, which meant that 1 cm surface-to-surface resolution could be achieved by locating the centroids of each return signal. A depth resolution of 4 mm was achieved by employing an optimized signal-processing algorithm based on a reversible jump Markov chain Monte Carlo method.

Abstract-Standard 3D imaging systems process only a single return at each pixel from an assumed single opaque surface. However, there are situations when the laser return consists of multiple peaks due to the footprint of the beam impinging on a target with surfaces distributed in depth or with semitransparent surfaces. If all these returns are processed, a more informative multilayered 3D image is created. We propose a unified theory of pixel processing for Lidar data using a Bayesian approach that incorporates spatial constraints through a Markov Random Field with a Potts prior model. This allows us to model uncertainty about the underlying spatial process. To palliate some inherent deficiencies of this prior model, we also introduce two proposal distributions, one based on spatial mode jumping and the other on a spatial birth/death process. The different parameters of the several returns are estimated using reversible-jump Markov chain Monte Carlo (RJMCMC) techniques in combination with an adaptive strategy of delayed rejection to improve the estimates of the parameters.

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We describe improvements to a time-of-flight sensor utilising the time-correlated single-photon counting technique employing a commercially-available silicon-based photon-counting module. By making modifications to the single-photon detection circuitry and the data analysis techniques, we experimentally demonstrate improved resolution between multiple scattering surfaces with a minimum resolvable separation of 1.7 cm at ranges in excess of several hundred metres.