Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD


Three-dimensional laser imaging systems offer important advantages for battlefield applications, such as night-time targeting and tactical reconnaissance. Recently developed technologies used by coherent detection systems that collect temporally resolved images include arrays of Avalanche Photo-Diodes (APD), Geiger mode APDs, and photo-diodes. Frequently, LADAR systems produce waveforms from each detector that characterize the convolution of the transmitted laser pulse with the target surface. The pulse convolution generates uncertainty as to the precise location of a target surface, which can severely impact various weapon systems' targeting capability. This work analyzes two deconvolution techniques: Wiener filtering and an iterative process derived from the Richardson-Lucy algorithm. Measured and simulated data are used for testing both methods. 3-D imaging LADAR systems with rapid frame acquisition may lose range resolution due to the transmitted pulse duration. Because of the tradeoff between the need for sufficient target illumination and obtaining high range resolution, LADAR systems often forfeit range resolution performance to improve the signal-to-noise ratio of the observed signal. This work also utilizes these two deconvolution techniques on a sequence of LADAR return images gathered at a very high sampling rate to improve the system's range resolution. Deconvolving the data amplifies any noise present in the data and decreases the signal-to-noise ratio of the reconstructed return pulse profiles. Based on the presence of photon and speckle noise, the work explores the degree to which range resolution can be improved for both measured and simulated data. Squared error and variance calculations are used to evaluate the performance of both signal reconstruction algorithms. The work shows that applying the iterative algorithm to measured and simulated data significantly improves the data's temporal resolution compared to the Wiener filter results.

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