Skip to Main Content
Fault-tolerant target detection and localization is a challenging task in collaborative sensor networks. The paper introduces our exploratory work toward identifying a stationary target in sensor networks with faulty sensors. We explore both spatial and temporal dimensions for data aggregation to decrease the false alarm rate and improve the target position accuracy. To filter out extreme measurements, the median of all readings in the close neighborhood is used to approximate the local observation to the target. The sensor whose observation is a local maximum computes a position estimate at each epoch. Results from multiple epochs are combined to decrease the false alarm rate further and improve the target localization accuracy. Our algorithms have low computation and communication overheads. A simulation study demonstrates the validity and efficiency of our design.