Bayesian analysis of lidar signals with multiple returns

Sergio Hernández-Marín, Andrew M. Wallace, Gavin J. Gibson

Research output: Contribution to journalArticlepeer-review

118 Citations (Scopus)


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 analyse 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 characterisation 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. © 2007 IEEE.

Original languageEnglish
Pages (from-to)2170-2180
Number of pages11
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number12
Publication statusPublished - Dec 2007


  • Burst illumination laser
  • Delayed rejection
  • Lidar
  • Photon counting
  • Reversible jump MCMC
  • Three-dimensional reconstruction


Dive into the research topics of 'Bayesian analysis of lidar signals with multiple returns'. Together they form a unique fingerprint.

Cite this