Radio Frequency Tomography for Reduction of Improvised Explosive Device Threats

The Michigan Technological University Research Institute (MTRI), led by Dr. Mark Stuff, and Integrity Applications Incorporated (IAI), propose to exploit data collected from a constellation of single tone RF sensors arrayed around a scene to provide an all-weather day/night method for detecting IED placement activity. In our model problem, we consider a high-traffic road with a few dozen sensors placed on posts or buildings of opportunity around the road. These sensors produce surveillance data to allow monitoring and detection of suspicious activity. In particular, we will exploit the collected data in two ways. First, a Bayes-optimal nonlinear filter will process the RF data to detect and track vehicles moving through the scene. Kinematic features provided by the filter will show if particular vehicles are likely to have placed an IED (e.g., move-stop-move behavior demonstrate increased likelihood that placement activity is taking place). Second, when the filter detects candidate placement activity, we will combine a tomographic imaging approach with compressed sensing methods to form high-resolution change-detection images in the region of the suspicious kinematic behavior. These queued change images will be used to determine if IED sweep activities are again required. BENEFIT: The methods and capabilities developed under this SBIR will provide a means of tracking vehicles through an urban area and detecting suspicious events. As such, they can be used in a variety of other military and civilian applications, including citywide traffic analysis, border protection, and surveillance of protected sites like airports. Furthermore, the tomographic imaging and compressed sensing change detection strategies can be exploited in the commercial sector to monitor protected sites for unusual activity.