Asset allocation in frequency and in 3 spatial dimensions for electronic warfare application

by Jonah Greenfield Crespo

Institution: IUPUI
Year: 2016
Keywords: Optimization; Swarm; Electronic Warfare; Electrical Engineering; Computer Engineering; PSO; EW; CMA-ES
Posted: 02/05/2017
Record ID: 2075023
Full text PDF: http://hdl.handle.net/1805/10797


Indiana University-Purdue University Indianapolis (IUPUI) This paper describes two research areas applied to Particle Swarm Optimization (PSO) in an electronic warfare asset scenario. First, a three spatial dimension solution utilizing topographical data is implemented and tested against a two dimensional solution. A three dimensional (3D) optimization increases solution space for optimization of asset location. Topography from NASA's Digital Elevation Model is also added to the solution to provide a realistic scenario. The optimization is tested for run time, average distances between receivers, average distance between receivers and paired transmitters, and transmission power. Due to load times of maps and increased iterations, the average run times were increased from 123ms to 178ms, which remains below the 1 second target for convergence speeds. The spread distance between receivers was able to increase from 86km to 89km. The distance between receiver and its paired transmitters as well as the total received power did not change signi cannily. In the second research contribution, a user input is created and placed into an unconstrained 2D active swarm. This human in the swarm' scenario allows a user to change keep-away boundaries during optimization. The blended human and swarm solution successfully implemented human input into a running optimization with a time delay. The results of this research show that a electronic warfare solutions with real 3D topography can be simulated with minimal computational costs over two dimensional solutions and that electronic warfare solutions can successfully optimize using human input data. Advisors/Committee Members: Christopher, Lauren Ann, Dos Santos, Euzeli Cipriano, Jr., Rizkalla, Maher, Li, Lingxi, King, Brian.