Date of Award:

5-2011

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Scott E. Budge

Committee

Scott E. Budge

Committee

Robert T. Pack

Committee

YangQuan Chen

Abstract

Automatic target recognition (ATR) relies on images from various sensors including 3-D imaging ladar. The accuracy of recognizing a target is highly dependent on the number of points on the target. The highest spatial frequencies of a target are located on edges. Therefore, a higher sampling density is desirable at these locations. A ladar receiver captures information on edges by detecting two surfaces when the beam lands partially on one surface and partially on another if the distance between the surfaces is greater than the temporal pulse width of the laser.

In recent years, the ability to digitize the intensity of the light seen at the ladar receiver has led to digitized ladar waveforms that can be post-processed. Post-processing the data allows signal processing techniques to be implemented on stored waveforms. The digitized waveform provides more information than simply a range from the sensor to the target and the intensity of received light. Complex surfaces change the shape of the return.

This thesis exploits this information to enhance the resolution on the edges of targets in the 3-D image or point cloud. First, increased range resolution is obtained by means of deconvolution. This allows two surfaces to be detected even if the distance between them is less than the width of the transmitted pulse. Second, the locations of multiple returns within the ladar beam footprint are computed.

Using deconvolution on the received waveform, an increase from 30 cm to 14 cm in range resolution is reported. Error on these measurements has a 2 cm standard deviation. A method for estimating the width of a 19 cm slot was reported to have a standard deviation of 3.44 cm. A method for angle estimation from a single waveform was developed. This method showed a 1.4° standard deviation on a 75° surface. Processed point clouds show sharper edges than the originals.

The processing method presented in this thesis enhances the resolution on the edges of targets where it is needed. As a result, the high spatial frequency content of edges is better represented. While ATR applications may benefit from this thesis, other applications such as 3-D object modeling may benefit from better representation of edges as well.

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This work made publicly available electronically on April 11, 2011.

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