The goal of this effort is to design and develop advanced jam-resistant waveforms for EP that minimize the probability of intercept/probability of detection to help harden against electronic attack (EA). In the event that these waveforms are detected and attacked, a cognitive radar resource manager (RM) will perform CEA by dynamically parameterizing the waveforms to the extent needed to maintain anti-jam performance margins against the most sophisticated EA. The radar RM will utilize machine learning and artificial intelligence techniques as appropriate, to determine waveform parameterization. The Phase 1 effort will develop a set of jam-resistant radar waveforms for air-to-surface maritime surveillance and imaging modes, and identify a subset of these waveforms that can be hosted on existing or planned near term Navy maritime surveillance radars. The wideband waveform modes will be designed to minimize dispersion loss induced by target motion. The intent is to go undetected, but if detected, dynamically adapt under the control of LSIs RM (that is sensing the jamming environment) to render jamming attacks to be of little to no utility. The Phase 1 effort will also include the development of anti-jam measures of effectiveness (MOEs) to assess the performance impacts of using these waveforms relative to traditional waveforms in both quiescent and jamming environments. The Phase 1 Option will develop prototype plans to demonstrate the effectiveness of these waveforms under the control of a cognitive RM in a lab environment and on a Navy test asset as part of the Phase 2 effort.The goal of this effort is to design and develop advanced jam-resistant waveforms for EP that minimize the probability of intercept/probability of detection to help harden against electronic EA. In the event that these waveforms are detected and attacked, a cognitive radar RM will perform CEA by dynamically parameterizing the waveforms to the extent needed to maintain anti-jam performance margins against the most sophisticated EA. The radar RM will utilize machine learning (ML) and artificial intelligence (AI) techniques as appropriate, to determine waveform parameterization. The Phase 1 effort will develop a set of jam-resistant radar waveforms for air-to-surface maritime surveillance and imaging modes, and identify a subset of these waveforms that can be hosted on existing or planned near term Navy maritime surveillance radars. The wideband waveform modes will be designed to minimize dispersion loss induced by target motion. The intent is to go undetected, but if detected, dynamically adapt under the control of LSIs RM (that is sensing the jamming environment) to render jamming attacks to be of little to no utility. The Phase 1 effort will also include the development of anti-jam measures of effectiveness (MOEs) to assess the performance impacts of using these waveforms relative to traditional waveforms in both quiescent and jamming environments.
Benefit: The Phase I effort seeks to design and develop advanced jam-resistant waveforms for EP that minimize the probability of intercept/probability of detection and utilize a cognitive radar resource manager (RM) will perform CEA by dynamically parameterizing the waveforms using ML AI methods as appropriate. Follow-on activities would seek to demonstrate this capability in the laboratory and field testing for eventual transition to a Navy maritime radar capable of hosting these capabilities. The proposed effort will lead to substantial improvements in CEA for Navy maritime surveillance radars. These capabilities are applicable to a broad class of platforms with diverse sensors and missions.
Keywords: Waveform, Waveform, Cognitive Radar, anti-jam
