A Self-Organizing; Cooperative Sensor Network for Remote Surveillance: Improved Target Tracking Results

摘要

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 borer areas. Whereas the functionality (e.g., sensing range and life in the field) of these smaller UGS (i.e., acoustic, seismic, magnetic, chemical or biological) 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 desert canyon area for drug interdiction, 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 access 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 using target identification (ID) features (i.e., the ID feature space of the target) to improve the tracking of closely spaced targets (i.e., the kinematic space of the targets). A Multiple Level Identification (MLID) approach was used to determine and maintain confidences for multiple target identifications for each target. These confidences were incorporated into the processing of kinematic data (i.e., target bearing reports) to improve the tracker's estimated position of the target's location. Results describing the effectiveness of using MLID on target tracking performance are reported using simulated target trajectory and ID data.