Phase II Amount
$1,476,318
The proposed Hierarchical Evolutionary Programming (H-EP) technology focuses on improving threat classification and electronic warfare battle management using the Genetic Programming (GP) optimization paradigm. The applications of the technology include: (1) on-shore emitter reference library optimization for classification performance, (2) forward deployed near real-time emitter reference library optimization for rapid adaptability, and (3) coordination of spectrum use and electronic attack tactics across multiple platforms. Leveraging the Hierarchical Emitter Library Optimization (HELO) prototype from Phase I, the Phase II program seeks to mature this technology, demonstrate its operational utility as a forward deployed capability, and illustrate improved effectiveness in both offensive and defensive electronic warfare operations. Using Lakotas Genetic Programming framework, each of these optimization problems can be solved by defining appropriate hierarchical hypotheses, fitness functions, and evolution operators. Once implemented, Lakotas GP analysis and test framework provides a formal methodology for selecting the most appropriate GP implementation and runtime parameters to solve the optimization problem within the desired time and with the desired accuracy.
Benefit: This Phase I prototyped H-EP approach is beneficial because it generically applies to the optimization of any feature-based classifier that relies on a reference library for feature correlation. This is accomplished by employing a fitness function that assesses the performance of the classifier against candidate reference libraries without knowledge of the classifier algorithm(s). This black box approach means that the H-EP approach can be quickly adapted to a wide range of classification problem domains. In addition, the H-EP avoids the need to have SME operators develop the reference libraries and allows for repeatable/comparable optimization results across users with varying levels of expertise. The Phase II benefits extend beyond on-shore emitter library optimization to include near real-time emitter library optimization onboard tactical platforms using current environmental/weather information as well as intelligence reports. This forward deployment of the technology enables emitter classification optimization: (1) tailored to forecasted weather conditions and (2) adapting to threats via intelligence reports in theatre. Both of these capabilities allow for more rapid adaptation to the threat environment in a matter of hours rather than months, which improves survivability as well as electronic warfare operational success. Under the Phase II Option, the benefits of the evolutionary programming framework extend to: (1) different sensor suites with varying capabilities and performance, (2) spectrum management for effective joint operations, and (3) electronic attack tactic selection for defensive and offensive postures. The benefits of the proposed work under the Phase II Option include the ability to improve emitter classification and electronic attack tactics in a multi-platform joint environment with varying electronic warfare capabilities, resources, and performance characteristics. By applying optimization techniques to this problem space, joint operations can increase effectiveness for mission such as Integrated Air Defense (IAD) suppression and battle group force protection.
Keywords: Operational Shipboard Electromagnetic Environment, Electronic Attack, BATTLE MANAGEMENT, Genetic Programming, Electronic Warfare, Situational Awareness, Electronic Intelligence (ELINT), Electronic Order of Battle
