DID YOU KNOW? DTIC has over 3.5 million final reports on DoD funded research, development, test, and evaluation activities available to our registered users. Click
HERE to register or log in.
Accession Number:
ADA235449
Title:
Multisensor Target Detection and Classification
Descriptive Note:
Master's thesis
Corporate Author:
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING
Report Date:
1987-12-01
Pagination or Media Count:
105.0
Abstract:
In this thesis a new approach to the detection and classification of tactical targets using a multifunction laser radar sensor is developed. Targets of interest were tanks, jeeps, trucks, and other vehicles. Doppler images were segmented by developing a new technique which compensates for spurious doppler returns. Relative range images were segmented using an approach based on range gradients. The resultant shapes in the segmented images were then classified using Zernike moment invariants as shape descriptors. Two classification decision rules were implemented a classical statistical nearest-neighbor approach and a new biologically-based neural network multilayer perceptron architecture. The doppler segmentation algorithm was applied to a set of 180 real world sensor images. An accurate segmentation was obtained for 89 percent of the images. The new doppler segmentation proved to be a robust method, and the moment invariants were effective in discriminating the tactical targets. Tanks were classified correctly 86 percent of the time. The most important result of this research is the demonstration of the use of a new information processing architecture for military applications. The multilayer perceptron outperformed the nearest-neighbor classifier in every test.
Distribution Statement:
APPROVED FOR PUBLIC RELEASE
