Using multiple genetic algorithms to generate radar point-scatterer models
This paper covers the use of three different genetic algorithms applied sequentially to radar cross-section data to generate point-scatterer models. The aim is to provide automatic conversion of measured 2-D/3-D data of low, medium, or high resolution into scatterer models. The resulting models are intended for use in a missile-target engagement simulator. The first genetic algorithm uses multiple species to locate the scattering centers. The second and third algorithms are for model fine tuning and optimization, respectively, Both of these algorithms use nondominated ranking to generate Pareto-optimal sets of results. The ability to choose results front the Pareto sets allows the designer some flexibility in the creation of the model. A method for constructing compound models to product! full 4 pi sr coverage is detailed. Example results from the model generation process are presented.
multiple genetic algorithms point-scatterer models multiple species pareto optimal radar cross section