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Fault-tolerant target detection and localization is a challenging task in collaborative sensor networks. This paper introduces our exploratory work toward identifying the targets 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 a close neighborhood of a sensor is used to approximate its local observation to the targets. The sensor whose observation is a local maxima computes a position estimate at each epoch. Results from multiple epoches are combined together to further decrease the false alarm rate and improve the target localization accuracy. Our algorithms have low computation and communication overheads. Simulation study demonstrates the validity and efficiency of our design.