A self-organizing, cooperative sensor network for remote surveillance: Target tracking while optimizing the geometry between bearing-reporting sensors and the target

The current trend to develop low cost, miniature unattended ground sensors (UGS) will enable a cost-effective, covert means for surveillance in both urban and remote border areas. Whereas the functionality (e.g., sensing range and life in the field) of smaller UGS may be limited due to size and cost constraints, a network of these sensors working cooperatively together can provide an effective surveillance capability. A key factor is the ability of these sensors to work cooperatively to achieve a "collective" functionality that can meet the surveillance objective. For example, to provide surveillance in a mountain canyon area for vehicle passage, the "collective" functions of the deployed network should minimize sensor use (i.e., maintain a longer sensor field life and covertness) while reliably detecting, identifying and tracking all vehicles entering into the canyon area. In this situation, the sensor network would have to assess the effect of the environmental conditions (e.g., wind direction and temperature) on the sensing range of its acoustic sensors, turn on those sensors that can initially detect vehicles and dynamically activate other appropriate sensors (e.g., seismic, acoustic or imaging sensors) that can provide additional target features as the vehicles move into and across the canyon area covered by the sensor network. To achieve this type of functionality requires system algorithms that are capable of optimizing the utilization of the sensors based on target data derived from the sensors. This paper describes results of target tracking using real bearing measurement data collected simultaneously from six independent, passive acoustic sensors. As the target moved across the sensor field, triplets of sensors were selected dynamically to minimize the error of the estimated target state estimations that was due to the geometry of the sensors to the target. The geometric dilution of precision (GDOP) metric was used as a criterion to select bearing measurements from sensor triplets. Estimated target positions using different fixed, sensor triplets were compared to the position estimates when the sensor triplets were selected dynamically to minimize the GDOP metric. Results confirm that selecting bearing measurements from sensors with favorable sensors-to-target geometries reduced the error of the estimated target locations from the ground truth.