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Advanced Signal Detection and Characterization Utilizing Artificial Intelligence (IL)/Machine Learning (ML)

Description:

TECHNOLOGY AREA(S): Electronics 

OBJECTIVE: Design and build an electronic signal detection and characterization unit that utilizes artificial intelligence (AI) and machine learning (ML), that can perform continuous monitoring of the electromagnetic spectrum (EMS), and that can provide signal characteristics to an interface. 

DESCRIPTION: Recent advances in the computing world has allowed for algorithmic advances in the detection and characterization of the electromagnetic spectrum (EMS). Specifically, the incorporation of such things as neural networks and training processes has elevated artificial intelligence (AI) and machine learning (ML) as key innovation areas for detecting, characterizing and cataloging highly complex signal types in the EMS. The current effort would mature these AI/ML concepts to develop a signal detection and characterization system for electronic signals. The unit would be able to detect and characterization various signal types and modulations. It would also provide performance and monitoring tools to provide real-time feedback to operators. The incorporation of data analytics for validation and visualization would be included in the unit. The system would follow a Modular, Open Systems Approach (MOSA) to allow integration into a variety of Army systems. The MOSA approach would also provide extensible ML and Deep Learning (DL) functions to expand upon key features and signal types. The system would contain only Commercial, Off-The-Shelf (COTS) products. 

PHASE I: Develop system design that includes artificial intelligence (AI) and machine learning (ML) algorithms and concepts, hardware and software specifications, and protocol operation (both internal and external). 

PHASE II: Develop and demonstrate a prototype system in a realistic environment. Conduct testing to prove feasibility over extended operating conditions. 

PHASE III: This system could be used in a broad range of military and civilian communication applications where equipment is susceptible to electromagnetic interference - for example, in military exercises/operations or in enhancing critical industrial operations in electromagnetic saturated environments. Integrate the product as a prototype adjunct to an already existing tactical system/architecture. Demonstrate that the product can be integrated and utilized in a tactical system with minor modifications to include form, fit, function changes and minor interface upgrades. Demonstration will provide key decision points on interoperability, MOSA integration, and tactical feasibility. 

REFERENCES: 

1: Szepesvari, Caleb. Algorithms for Reinforcement Learning. 2009

2:  Ioffe, Sergey and Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 2015

3:  Kinds of RL Algorithms. https://spinninup.openai.com/en/latest/spinningup/rl_intro2.html. 2018

4:  Bharati, K Swetha and Ashok Jhunjhunwala. Implementation of machine learning applications on a fixed-point DSP. 2015

KEYWORDS: Artificial Intelligence, Machine Learning, Deep Learning, Signal Detection, Signal Characterization, Modular Open Systems Approach (MOSA), Commercial Off-The-Shelf (COTS) 

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