Eric T. Kouba

Date of Award

12-1992

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Steven K. Rogers, PhD

Abstract

A real-time recurrent learning algorithm was applied to a five class radar target identification problem. The wideband radar was assumed to measure both kinematic (tracking information expressed as estimated aspect angles) and high range resolution data from a single, isolated aircraft. The aspect angles (azimuth and elevation) of the aircraft relative to the radar were assumed to be constantly chancing. This created temporal sequences of high range resolution radar signatures that changed as the aspect angles changed. These sequences were used as input features to a recurrent neural network for three radar target identification test cases. The first test case demonstrated the feasibility of using recurrent neural networks for radar target identification. The second test case demonstrated the relationship between sequence length and target recognition accuracy. For the third test case, the recurrent net achieved 96% test set accuracy under the following conditions: 5 aircraft classes, azimuth range between 60° and 90°, elevation range between +5° and -5°, 1° signature granularity, and signatures corrupted by 5 dBsm scintillation noise.

AFIT Designator

AFIT-GSO-ENG-92D-02

DTIC Accession Number

ADA259127

Comments

The author's Vita page is omitted.

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