SBIR-STTR Award

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.

Keywords:
Asynchronous, Ivysys, Caf, Mrc, Cyclic Caf

This research project seeks to adapt signal processing algorithms developed for the detection and estimation of weak RF network signals to the application of detecting weak social media signals diffusing across multiple, heterogeneous social networking environments (SNEs). We propose to develop quantitative predictive models that capture the dynamics of information diffusion processes over multiple SNE domains to include the cross-domain ripple effect, where a social trending topic in one domain can penetrate into another domain. We will leverage the predictive models to accurately detect early in the diffusion process weak social media signals that will propagate widely in the future. The IvySys model-based predictive analytics approach exploits the fact that orchestrated and automated efforts to spread information across SNEs can produce different patterns. The IvySys approach uses signal-processing techniques, such as Kalman filtering, to adaptively estimate cross-domain model parameters, representing the underlying dynamics of the social diffusion process, in real time using sampled streams of SNE data.