This project investigates a laser radar system. The system is based on the principles of time-correlated single photon counting, and by measuring the times-of-flight of reflected photons it can find range profiles and perform three-dimensional imaging of scenes. Because of the photon counting technique the resolution and precision that the system can achieve is very high compared to analog systems. These properties make the system interesting for many military applications. For example, the system can be used to interrogate non-cooperative targets at a safe distance in order to gather intelligence. However, signal processing is needed in order to extract the information from the data acquired by the system. This project focuses on the analysis of different signal processing methods. The Wiener filter and the Richardson-Lucy algorithm are used to deconvolve the data acquired by the photon counting system. In order to find the positions of potential targets different approaches of non-linear least squares methods are tested, as well as a more unconventional method called ESPRIT. The methods are evaluated based on their ability to resolve two targets separated by some known distance and the accuracy with which they calculate the position of a single target, as well as their robustness to noise and their computational burden. Results show that fitting a curve made of a linear combination of asymmetric super-Gaussians to the data by a method of non-linear least squares manages to accurately resolve targets separated by 1.75 cm, which is the best result of all the methods tested. The accuracy for finding the position of a single target is similar between the methods but ESPRIT has a much faster computation time.

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