Approach for LIDAR signals with multiple returns.

Abstract

Time-correlated single photon counting (TCSPC) and burst illumination laser (BIL) data can be used for depth reconstruction of a target surface; the problem is how to analyze the response for the reconstruction. We propose a fast-approach STMCMC (Simulated Tempering Markov Chain Monte Carlo) for LIDAR signals with multiple return, in order to obtain a complete characterization of a 3D surface by the laser range system. STMCMC is used to explore the spaces by the preset distributions instead of the prior distributions. Added active intervention tempering makes the Markov chain mix better through the temporary expansion of the solutions. The added step keeps the operation under control and yet retains the Markov characteristic of the operation. The theoretical analysis and the demonstrations on the practical data show flexible operation, and the parameters can be estimated to a high degree of accuracy.