Date of Award


Document Type


Degree Name

Master of Science in Electrical Engineering


Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD


Range estimation algorithms have been applied to Laser Detection and Ranging (LADAR) data to test for accuracy and precision. Data was acquired from Matlab® simulations and an experiment using the Advanced Scientific Concepts 3-D flash LADAR camera. Simulated LADAR data was based on a Gaussian pulse shape model with Poisson noise added. Simulations were performed to test range estimation algorithm performance with respect to waveform position within the range gate. The effectiveness of each algorithm is presented in terms of its average root mean square error and standard deviation in 1000 trials. The measured data experiment examined the effectiveness of an algorithm's ability to determine a range difference between 2 flat surfaces. The algorithms compared for analysis include a peak, maximum likelihood, and matched filter estimator. Various interpolation strategies were implemented in the peak estimator. The matched filter was implemented in the time and frequency domains. A normalized version of the matched filter was also developed and applied to the LADAR data. Three different methods based on averaging were developed to calibrate the pulse width of the reference waveform used in the matched filters and maximum likelihood algorithms. Simulation results show that a matched filter produces a bias when waveforms are off center, but normalizing waveforms before computing the cross correlation can reduce the average bias from 0.335 meters to 0.124 meters. The maximum likelihood algorithm also produces a bias in shifted waveforms, while the peak estimator maintains a nearly constant level of bias in its measurements due to the effect of shot noise on waveforms. In the measured data sets, normalization did not reduce the bias in measurements because it increased the algorithm's sensitivity to errors in the reference waveform model. The maximum likelihood algorithm's sensitivity to errors in modeling were revealed due to its poor performance in the measured data.

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