The proposed Sirens system combines multiple TRL7-9 technologies currently in operation across the DoD into a highly capable RF sensor package capable of detecting, geolocating, and electronic attacking adversarial radar networks using a team of coordinated UAVs. The Sirens system leverages the Teams vast combined experience developing TRL9 hardware and software for electronic intelligence and attack with a strong background in AI/ML and prototype integration for its development. The core RF expertise and capabilities not only directly provide a pathway to developing relevant RF detection, parameterization, geolocation, and electronic attack methods but also provide the ability to create high-fidelity training environments for training AI/ML methods for detecting new signals and reinforcement learning based multi-agent coordination. To support the proposed Sirens concept of operations and a rapid prototyping environment demonstration at the end of Phase 2, the team will leverage its other existing capabilities including a TRL7 quadrotor with an ELINT package, a TRL9 high-assurance differential GPS system to provide precise geolocation of adversarial emitters, and TRL9 anomaly detection systems. Developing the architecture of the Sirens system will leverages the teams vast expertise in developing sensor and software systems for airborne systems, airborne control of multiple interconnected systems, modular/open software architecture, data assurance, artificial intelligence, manned-unmanned teaming/collaborative robotics, and machine learning. This collection of knowledge and experiences drastically reduces the risk associated with the development of the Sirens system for manned-unmanned teaming in a modern environment.
Benefit: Enhanced methods of manned-unmanned teaming (MUM-T) has-reaching applicability in both the DoD and law enforcement sectors as both are rapidly pushing towards higher level of autonomy for searching large areas for activity within the US airspace or along the border. The Sirens MUM-T capability, though specifically targeted for defeating adversarial radar networks can be tailored to other potential applications may be integrated with additional capabilities like small Size, Weight, and Power (SwaP) ground moving target indicator (GMTI), full motion video (FMV), and other sensing capabilities. Example applications include DoD programs such as Intelligence, Surveillance, and Reconnaissance (ISR), special operations or base defense. When MUM-T is completed, LJL will work with partners in the aerospace industry to promote the developed technologies in both the DoD and private sector, such as leading manufacturers of Small and Medium UAS and UGVs for ISR and ground robotics solutions. Additionally, SNC has multiple relevant active programs within the DoD that could leverage the developed technology and increase the chances of its adoption. Multi-agent planning and control is also a key element of the rapidly expanding Urban Air Mobility and package delivery market. The combination of the LJL DL algorithms and Kuttas DO-178 safety expertise provides a prime entry into this commercial market.
Keywords: RF detection and parameterization., RF detection and parameterization., Autonomous Path Planning, distributed time-difference of arrival, distributed multi-agent coordination, Artificial Intelligence (AI) and Machine Learning (ML), Reinforcement Learning, Electronic Attack, collaborative robotics and manned-unmanned teaming
