Optimal Detection and Classification in a Centralized Network of Asynchronous Sensors

IvySys proposes novel asynchronous signal sensing and automatic modulation classification (AMC) approaches that leverage a centralized network of low-cost asynchronous sensors to enable weak signal detection and classification. These innovative approaches will provide detection performance within 3 to 5 dB Signal-to-Interference-plus-Noise Ratio (SINR) of a centralized network of synchronous sensors. This asynchronous signal sensing processing architecture combines weak signal Cross Ambiguity Function (CAF) detection with Maximal Ratio Combining (MRC) to improve probability of detection, while minimizing the probability of false alarm. We plan to extend the CAF mathematical algorithm framework to address asynchronous signal correlations and build upon existing MRC techniques, which are inherently robust to signal fading. We will investigate both traditional and cyclic CAF processing algorithms. The cyclic CAF is inherently robust to channel distortion allowing for increased SINR for a given Signal of Interest (SOI), thereby enhancing weak signal detection. We will also leverage a sensor network simulation tool (LPIsimNET) to provide both planning and prediction capability for sensor placement based on maximizing SINR for single input multiple-output (SIMO) configurations.