@InProceedings{8553536,
  author = {Y. Altmann and S. McLaughlin},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Range Estimation from Single-Photon Lidar Data Using a Stochastic Em Approach},
  year = {2018},
  pages = {1112-1116},
  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.},
  keywords = {Bayes methods;mixture models;Monte Carlo methods;optical radar;parameter estimation;single-photon lidar data;single-photon waveforms;photon-starved regime;background illumination;nuisance photons;mixture model;unknown model parameters;Bayesian model;stochastic EM algorithm;estimated nuisance parameters;satisfactory range estimation performance;high-background scenarios;Bayesian approach;Monte Carlo simulation;Photonics;Estimation;Laser radar;Bayes methods;Surface emitting lasers;Signal processing algorithms;Computational modeling;Single-photon Lidar;Bayesian estimation;mixture model;Stochastic Expectation-Maximisation},
  doi = {10.23919/EUSIPCO.2018.8553536},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439171.pdf},
}

{"_id":"b9XmZawsEBP3K56wt","bibbaseid":"altmann-mclaughlin-rangeestimationfromsinglephotonlidardatausingastochasticemapproach-2018","authorIDs":[],"author_short":["Altmann, Y.","McLaughlin, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Y."],"propositions":[],"lastnames":["Altmann"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["McLaughlin"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Range Estimation from Single-Photon Lidar Data Using a Stochastic Em Approach","year":"2018","pages":"1112-1116","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.","keywords":"Bayes methods;mixture models;Monte Carlo methods;optical radar;parameter estimation;single-photon lidar data;single-photon waveforms;photon-starved regime;background illumination;nuisance photons;mixture model;unknown model parameters;Bayesian model;stochastic EM algorithm;estimated nuisance parameters;satisfactory range estimation performance;high-background scenarios;Bayesian approach;Monte Carlo simulation;Photonics;Estimation;Laser radar;Bayes methods;Surface emitting lasers;Signal processing algorithms;Computational modeling;Single-photon Lidar;Bayesian estimation;mixture model;Stochastic Expectation-Maximisation","doi":"10.23919/EUSIPCO.2018.8553536","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439171.pdf","bibtex":"@InProceedings{8553536,\n author = {Y. Altmann and S. McLaughlin},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Range Estimation from Single-Photon Lidar Data Using a Stochastic Em Approach},\n year = {2018},\n pages = {1112-1116},\n 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.},\n keywords = {Bayes methods;mixture models;Monte Carlo methods;optical radar;parameter estimation;single-photon lidar data;single-photon waveforms;photon-starved regime;background illumination;nuisance photons;mixture model;unknown model parameters;Bayesian model;stochastic EM algorithm;estimated nuisance parameters;satisfactory range estimation performance;high-background scenarios;Bayesian approach;Monte Carlo simulation;Photonics;Estimation;Laser radar;Bayes methods;Surface emitting lasers;Signal processing algorithms;Computational modeling;Single-photon Lidar;Bayesian estimation;mixture model;Stochastic Expectation-Maximisation},\n doi = {10.23919/EUSIPCO.2018.8553536},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439171.pdf},\n}\n\n","author_short":["Altmann, Y.","McLaughlin, S."],"key":"8553536","id":"8553536","bibbaseid":"altmann-mclaughlin-rangeestimationfromsinglephotonlidardatausingastochasticemapproach-2018","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439171.pdf"},"keyword":["Bayes methods;mixture models;Monte Carlo methods;optical radar;parameter estimation;single-photon lidar data;single-photon waveforms;photon-starved regime;background illumination;nuisance photons;mixture model;unknown model parameters;Bayesian model;stochastic EM algorithm;estimated nuisance parameters;satisfactory range estimation performance;high-background scenarios;Bayesian approach;Monte Carlo simulation;Photonics;Estimation;Laser radar;Bayes methods;Surface emitting lasers;Signal processing algorithms;Computational modeling;Single-photon Lidar;Bayesian estimation;mixture model;Stochastic Expectation-Maximisation"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2018url.bib","creationDate":"2021-02-13T15:38:40.475Z","downloads":0,"keywords":["bayes methods;mixture models;monte carlo methods;optical radar;parameter estimation;single-photon lidar data;single-photon waveforms;photon-starved regime;background illumination;nuisance photons;mixture model;unknown model parameters;bayesian model;stochastic em algorithm;estimated nuisance parameters;satisfactory range estimation performance;high-background scenarios;bayesian approach;monte carlo simulation;photonics;estimation;laser radar;bayes methods;surface emitting lasers;signal processing algorithms;computational modeling;single-photon lidar;bayesian estimation;mixture model;stochastic expectation-maximisation"],"search_terms":["range","estimation","single","photon","lidar","data","using","stochastic","approach","altmann","mclaughlin"],"title":"Range Estimation from Single-Photon Lidar Data Using a Stochastic Em Approach","year":2018,"dataSources":["yiZioZximP7hphDpY","iuBeKSmaES2fHcEE9"]}