Spectrum Situational Awareness through Distributed RF Sensing - Dr. Andrew Portune, Senior Research Scientist, Perspecta Labs

#Securesense

Spectrum awareness is increasingly critical across both civil and military applications.  Reliable spectrum Situational Awareness (SA) provides visibility to essential information needed for communications, interference mitigation, dynamic shared spectrum, network optimization, and electromagnetic maneuverability.  Effective spectrum SA should be driven by low cost RF sensors that can be broadly distributed to provide spatial diversity and avoid common sensing problems such as multipath and shadowing.

 This presentation discusses the importance of spectrum SA for multidisciplinary operations and how advances in RF technology are enabling a revolution in this space.  Technology developed by Perspecta Labs is presented as an exemplar spectrum SA system which exhibits scalability, autonomy, and dynamic responsiveness.  The SecureSense system employs a distributed network of heterogeneous static and mobile RF sensors which leverage recent advancements in low cost Software Defined Radios (SDRs).  Features and methods for signal detection, classification, automated sensor cross-cueing, and spectral visualization are described in and around a virtual demonstration of the SecureSense system.  Specific attention is paid to leveraging machine learning and artificial intelligence for classification and in future development for autonomous sensor network configuration.

 Bio:  Dr. Andrew Portune

Dr. Portune currently serves as Sr. Research Scientist at Perspecta Labs in Red Bank, NJ.  Dr. Portune is an accomplished technologist and researcher with an extensive background in data analytics, RF sensing, and materials science.  Dr. Portune is a key contributor on the Comprehensive Spectrum Monitoring (COSMOS) program, where he leads development of the SecureSense technology for spectrum situational awareness.  Additional activities include technical leadership on multi-objective reasoning using machine learning techniques.  Dr. Portune previously served as Director at a small business focused on exploitation of unintended electromagnetic emissions.