Proc. SPIE. 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV
KEYWORDS: Sensors, Sensor networks, Detection and tracking algorithms, Motion models, Signal processing, Filtering (signal processing), Data processing, Fusion energy, Systems modeling, Energy efficiency
Target tracking is an essential capability for Wireless Sensor Networks (WSNs) and is used as a canonical problem for collaborative signal and information processing to dynamically manage sensor resources and efficiently process distributed sensor measurements. In existing work for target tracking in WSNs, such as the information-driven sensor query (IDSQ) approach, the tasking sensors are scheduled based on uniform sampling interval, ignoring the changing of the target dynamics and obtained estimation accuracy. This paper proposes the adaptive sensor scheduling strategy by jointly selecting the tasking sensor and determining the sampling interval according to the predicted tracking accuracy and tracking cost. The sensors are scheduled in two tracking modes, i.e., the fast tracking approaching mode when the predicted tracking accuracy is not satisfactory, and the tracking maintenance mode when the predicted tracking accuracy is satisfactory. The approach employs an Extended Kalman Filter (EKF) based estimation technique to predict the tracking accuracy, and adopts a linear energy model to predict the energy consumption. Simulation results demonstrate that, compared to the non-adaptive approach, the proposed approach can achieve significant improvement on energy consumption without degrading the tracking accuracy.