Range estimation from single-photon Lidar data using a stochastic EM approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the problem of estimating range profiles from single-photon waveforms in the photon-starved regime, with a background illumination both high and unknown a priori such that the influence of nuisance photons cannot be neglected. We reformulate the classical observation model into a new mixture model, adopt a Bayesian approach and assign prior distributions to the unknown model parameters. First, the range profile of interest is marginalised from the Bayesian model to estimate the remaining model parameters (considered as nuisance parameters) using a stochastic EM algorithm. The range profile is then estimated via Monte Carlo simulation, conditioned on the previously estimated nuisance parameters. Results of simulations conducted with controlled data demonstrate the possibility to maintain satisfactory range estimation performance in high-background scenarios with less than 10 signal photons per pixel on average.
Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages1112-1116
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 3 Dec 2018

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISSN (Electronic)2076-1465

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optical radar
photons
profiles
waveforms
estimating
simulation
illumination
pixels
estimates

Cite this

Altmann, Y., & McLaughlin, S. (2018). Range estimation from single-photon Lidar data using a stochastic EM approach. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 1112-1116). (European Signal Processing Conference (EUSIPCO)). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553536
Altmann, Yoann ; McLaughlin, Stephen. / Range estimation from single-photon Lidar data using a stochastic EM approach. 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. pp. 1112-1116 (European Signal Processing Conference (EUSIPCO)).
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title = "Range estimation from single-photon Lidar data using a stochastic EM approach",
abstract = "This paper addresses the problem of estimating range profiles from single-photon waveforms in the photon-starved regime, with a background illumination both high and unknown a priori such that the influence of nuisance photons cannot be neglected. We reformulate the classical observation model into a new mixture model, adopt a Bayesian approach and assign prior distributions to the unknown model parameters. First, the range profile of interest is marginalised from the Bayesian model to estimate the remaining model parameters (considered as nuisance parameters) using a stochastic EM algorithm. The range profile is then estimated via Monte Carlo simulation, conditioned on the previously estimated nuisance parameters. Results of simulations conducted with controlled data demonstrate the possibility to maintain satisfactory range estimation performance in high-background scenarios with less than 10 signal photons per pixel on average.",
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language = "English",
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Altmann, Y & McLaughlin, S 2018, Range estimation from single-photon Lidar data using a stochastic EM approach. in 2018 26th European Signal Processing Conference (EUSIPCO). European Signal Processing Conference (EUSIPCO), IEEE, pp. 1112-1116. https://doi.org/10.23919/EUSIPCO.2018.8553536

Range estimation from single-photon Lidar data using a stochastic EM approach. / Altmann, Yoann; McLaughlin, Stephen.

2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. p. 1112-1116 (European Signal Processing Conference (EUSIPCO)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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N2 - This paper addresses the problem of estimating range profiles from single-photon waveforms in the photon-starved regime, with a background illumination both high and unknown a priori such that the influence of nuisance photons cannot be neglected. We reformulate the classical observation model into a new mixture model, adopt a Bayesian approach and assign prior distributions to the unknown model parameters. First, the range profile of interest is marginalised from the Bayesian model to estimate the remaining model parameters (considered as nuisance parameters) using a stochastic EM algorithm. The range profile is then estimated via Monte Carlo simulation, conditioned on the previously estimated nuisance parameters. Results of simulations conducted with controlled data demonstrate the possibility to maintain satisfactory range estimation performance in high-background scenarios with less than 10 signal photons per pixel on average.

AB - This paper addresses the problem of estimating range profiles from single-photon waveforms in the photon-starved regime, with a background illumination both high and unknown a priori such that the influence of nuisance photons cannot be neglected. We reformulate the classical observation model into a new mixture model, adopt a Bayesian approach and assign prior distributions to the unknown model parameters. First, the range profile of interest is marginalised from the Bayesian model to estimate the remaining model parameters (considered as nuisance parameters) using a stochastic EM algorithm. The range profile is then estimated via Monte Carlo simulation, conditioned on the previously estimated nuisance parameters. Results of simulations conducted with controlled data demonstrate the possibility to maintain satisfactory range estimation performance in high-background scenarios with less than 10 signal photons per pixel on average.

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Altmann Y, McLaughlin S. Range estimation from single-photon Lidar data using a stochastic EM approach. In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE. 2018. p. 1112-1116. (European Signal Processing Conference (EUSIPCO)). https://doi.org/10.23919/EUSIPCO.2018.8553536