Nowadays, Radio Frequency Identification (RFID) technology has been widely employed in the fields of object positioning, tracking and monitoring. However, there are a large number of redundant data generated in RFID systems due to duplicate detection and cross detection. Since RFID data is usually streaming, uncertain and mobile data, traditional static data and data stream filtering strategies cannot be applied to filter the RFID data effectively. In the paper, we first present a three-phase filtering framework under a block-based sliding window model. Aiming to filter the temporal redundant events, we propose an approximate Probability Synthesis Bloom Filter (PSBF) and discuss its filter principle, update rules and error rate in details. Comparing with the existing RFID filters, PSBF can not only filter the redundant probabilistic events, but also can calculate object existential probabilities with temporal decaying, and handle with the situations of location movement and staying at the overlapping areas among multiple readers correctly. The experiments on the simulated dataset show that the proposed filter outperforms the state-of-the-art filtering method.