The Global Information Grid (GIG) has access to massive amounts of data from a vast number of sensors and sources. Extracting information from this quantity of sensor data is a significant challenge. Sensor data is often large, which taxes limited-bandwidth GIG interconnections. Current approaches rely almost entirely on end-user analytics, which is inefficient in that it requires raw data to reach the analysts from distant nodes of the GIG before information is extracted. The proposed effort overcomes this issue, both saving limited bandwidth and improving analytic efficiency by extracting information close to the source. We apply concepts of both compressive sensing (CS) and compressive processing (CP) to the information-generating chain that flows from the sensor through the GIG to analytical resources, and then back to the GIG as a product. Objective, quantitative means are used to optimize state-of-the-art CS and CP services deployed at locations throughout the distributed sensor-GIG-analyst network. The result is a Distributed Compressive Sensing and Processing (DCSP) framework with a comprehensive joint application of CS and CP to a large, deployed network.
Keywords: Sensors, Sensors, Persistent Surveillance , Classification, Intelligence Surveillance Reconnaissance (Isr), Imagery, Wide-Area Motion Imagery (Wami), Compressive Sensing,
