Abstract:  The existing tunnel fire alarms are relatively single in detection and limited during measurement, which leads to false alarms in the system. As a result, a multisensor gradient data fusion model is employed to monitor tunnel fires. An intermediate station is designed between the sensor and the fusion center, the data information collected by the temperature, flame, and smoke sensors are uploaded to the intermediate station for preprocessing, and the correlation function is used to delete the data with low sensor support. Then, the multisource data from the same sensor is locally fused by the least squares method at the intermediate station, and the optimal fusion data is obtained. Moreover, the DempsterShafer evidence theory algorithm is used to transmit these optimal fusion data to the fusion center for global fusion to obtain the final fusion value, and the current occurrence of fire in the tunnel is determined by judging the output results. The final results do not depend on a single sensor or monitoring point data. Through the experimental simulation of some data of the existing tunnel fire detectors, the experimental results show that, compared with the traditional method, the tunnel fire monitoring and alarm system based on multisensor data fusion can more accurately identify the fire occurrence in the tunnel.

Key words: tunnel, fire monitoring, multisensor data fusion, least squares, DempsterShafer evidence theory