This SBIR Phase I project will investigate the possibility of using a network of low-cost, battery-powered radio frequency (RF) transceivers to detect, localize and track the movements of people illegally trying to enter the US borders through wooded areas. The system uses radio tomography (RT), i.e., the changes in radio signal strength (RSS) measurements on the static links of the network over time, to localize and track stationary and moving targets. In a wooded environment, a major challenge is represented by the environmental noise, i.e., the changes in RSS introduced by e.g. wind and rain. In this project, we will demonstrate noise reduction algorithms capable of learning the characteristics of the environmental noise and filtering it from the collected RSS signals. This allows the system to detect a person crossing a link line despite the environmental noise. Moreover, in remote wooded areas, the battery-powered RF sensors composing the system must function for an extended period of time (e.g., one year). We will address the issue of power efficiency first by developing a self-synchronizing communication protocol which enables radio duty-cycling. Second, the system will adaptively adjust the sampling (i.e., communication) rate of the sensors depending on the detection of significant events (i.e., intrusions of people in the monitored area). We will test large-scale deployments of RF sensors in wooded areas and process the collected data off-line to demonstrate the ability of our RT system to accurately track people as they move through the deployment area.
