关键词: 多传感器, 火灾检测, 模糊理论, D-S证据理论, 数据融合

Abstract: Traditional fire detection algorithms confront such drawbacks as low accuracy and poor adaptability in complex fire detection environments. To deal with those problems, a new algorithm is proposed for fire detection with multi-sensor data fusion by combining fuzzy sets and with Dempster-Shafer theory. The proposed algorithm first estimates the fire state using the flame, smoke and temperature sensors, and then calculates the fuzzy memberships of these sensors according to the given fuzzy membership function. Besides, a method of sensor credibility calculation is introduced to improve the robustness of the detection system, and each membership and the corresponding credibility measured from the sensors will be transferred to the basic probability assignment function (mass function). Finally, evidence theory is applied to integrate the information of multiple measurements within a period. The results suggest that the proposed algorithm overcomes the drawbacks of instability and uncertainty in single-sensor algorithms, and improves the fire detection accuracy as well as the robustness.

Key words: multi-sensor, fire detection, fuzzy theory, D-S evidence theory, data fusion