Scalable spectrum situational awareness using devices of opportunity

Herein, a solution is presented to address the problem of providing scalable dynamic spectrum awareness for military (and commercial) applications opportunistically using RF devices that are deployed for tasks other than spectrum mapping. We consider challenging urban environments with a large heterogeneous mix of devices and signals. Additional challenges we address include sparsely distributed receivers and the requirement for a system that can be scaled based on the mission and the number of users that must be supported. In order to address these challenges in a scalable distributed fashion, we put forward a solution that uses the following three techniques we have developed: (1) Sparse signal reconstruction techniques to fill in the spatial gaps from limited receiver measurements; (2) Kanerva Sparse Distributed Memory (SDM) to store and retrieve large amounts of data and perform anomaly detection; (3) Feature extraction algorithms to allow for the use of different radio devices that are able to provide varying levels of information.