Applications of Machine Learning for Electronic Warfare Emitter Identification and Resource Management

Abstract

Electronic warfare (EW) operators face a multitude of challenges when performing single- and distributed-platform sensing and jamming tasks in increasingly dense and agile threat environments. During an engagement timeline, actions often must be taken quickly and based on the partial information available. Recently, the world has observed a boom in artificial intelligence, a suite of data-driven lateral technologies that has already disrupted multiple fields where autonomy and big data are key elements. Although it is not the solution to all EW tasks, artificial intelligence shows promise in offering potential solutions to improve EW efficiency and effectiveness through informed decision-making beyond the capability of a human operator. The Johns Hopkins University Applied Physics Laboratory (APL) Precision Strike Mission Area has invested in research and development in the specific EW tasks of emitter identification and autonomous resource allocation. This article presents promising results from these projects and describes recommended future work in these areas, as well as additional EW applications that may benefit from research in artificial intelligence.