Detecting and characterising returns in a pulsed ladar system

Abstract

A new multi-spectral laser radar (ladar) system based on the time-correlated single photon counting, time-of-flight technique has been designed to detect and characterise distributed targets at ranges of several kilometres. The system uses six separated laser channels in the visible and near infrared part of the electromagnetic spectrum. The authors present a method to detect the numbers, positions, heights and shape parameters of returns from this system, used for range profiling and target classification. The algorithm has two principal stages: non-parametric bump hunting based on an analysis of the smoothed derivatives of the photon count histogram in scale space, and maximum likelihood estimation using Poisson statistics. The approach is demonstrated on simulated and real data from a multi-spectral ladar system, showing that the return parameters can be estimated to a high degree of accuracy.

Ranging and Three-Dimensional Imaging Using Time-Correlated Single-Photon Counting and Point-by-Point Acquisition

“…In the first approach, described fully in [15], we first apply a scale-space filtering [16], or bump hunting [17] procedure, that provides an initial estimate of the number, amplitude, and positions of the suspected returns. Depending on the amplitude and separation of discrete returns, a histogram of many returns may be multimodal or multitangential (implied by multimodality).…”

“…Since each photon return can be regarded as an independent measurement of the photon return time, the collection of multiple returns (typically >10 5 return events) can yield time measurements with considerably shorter time resolution than the system jitter, which is typically tens of picoseconds. The good depth resolution means that surface profile detail can be measured and that distances between reflecting surfaces can Manuscript received January 9, 2007; revised June 15,2007. This work was supported in part by the U.K. Engineering and Physical Sciences Research Council, Swindon, U.K., in part by QinetiQ, U.K., in part by BAE Systems, Farnborough, U.K., in part by Selex, Sutton-on-the-Forest, North Yorkshire, U.K., and in part by the Royal Society, London, U.K.…”

ALART: a novel lidar system for vegetation height retrieval from space

“…Gaussians and/or Generalized Gaussians [14][15] (see Figure 4 for an example) or adaptive optimization and update of the model complexity by the method of "Reversible Jump Markov-Chain Monte Carlo" (RJMCMC) [5]. As mentioned before, vegetation height can be further derived by analyzing the distribution of histograms, e.g.…”

Bayesian Analysis of Lidar Signals with Multiple Returns

“…The SEM algorithm replaces the E step by incorporating a stochastic step to avoid entrapment in a local maxima of the likelihood function. A hybrid approach is found in Wallace et al [19]; they used a deterministic nonparametric "bump-hunting" procedure that provided the number, amplitudes, and positions of the suspected returns. Once the initial estimates were obtained, they computed the MLE of the set of parameters that best explained the data.…”

“…To fully explore the advantages of our Bayesian approach, we compare the RJMCMC algorithm developed with a basic centroid algorithm and the nonparametric bump-hunting and maximum likelihood estimation (BH-MLE) algorithm described by Wallace et al [19]. These two algorithms are in essence deterministic as opposed to the stochastic nature of the RJMCMC algorithm.…”