Date of Award
3-4-2008
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Michael A. Saville, PhD
Abstract
This research effective effort develops the necessary interfaces between the radar signal processing components and an optimization routine, such as genetic algorithms, to develop Electronic Countermeasure (ECM) waveforms under a Hardware-in-the-Loop (HILS) architecture. The various ECM waveforms are stored in an ECM library, where an operator selects the desired function to use against a particular system. This optimization works with modular components, compared to previous research that embedded a genetic algorithm into the Range Gate Pull-off (RGPO) waveform optimization loop, which can be interchanged based upon the operator's desired hardware/ software testing setup. The ECM library's first entries contain the RGPO and Velocity Gate Pull-off (VGPO) signals, developed mathematically for multiple polynomial profiles representing realistic moving false targets. The Lab-Volt™ training system and jammer pod provided a validation medium for the developed RGPO and VGPO waveforms. These waveforms were optimized using a Simulink model of the Lab-Volt™ radar system and the MATLAB® Genetic Algorithm (GA) and Direct Search toolbox, contained in Version 7.4 (R2007a), using a defined parameter set, specified for the RGPO waveform. Integration of MATLAB® code with Simulink models provides the necessary interfaces to later transition from software radar models to actual system hardware. Results from GA optimization illuminate the necessity to specifically define the necessary constrains, both linear and nonlinear, imposed upon the environmental conditions. Given defined constraints relative to the Lab-Volt™ training system, the HILS architecture produced multiple constant velocity range profiles with walk-off ranges and maximum velocities similar to the Lab-Volt™ Jammer Pod.
AFIT Designator
AFIT-GE-ENG-08-34
DTIC Accession Number
ADA485104
Recommended Citation
Townsend, James D., "Improvement of ECM Techniques through Implementation of a Genetic Algorithm" (2008). Theses and Dissertations. 2784.
https://scholar.afit.edu/etd/2784