Markov Chain Monte Carlo Algorithms for 3D Ranging and Imaging
نویسندگان
چکیده
We propose a new approach for the processing of TimeCorrelated Single Photon Count (TCSPC) and Burst Illumination Laser (BIL) data. This data can be used to measure range, surface shape and determine a characteristic signature for remote targets. 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 target surfaces. The Markov chain Monte Carlo (MCMC) methodology, combined with a random sampling of the search space, enables us to detect and characterise both near and far targets from a fuller, more sensitive analysis than existing methods.
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