## 1 Introduction  
With the rapid development of the economy, the quantity and complexity of buildings have increased, leading to increasing fire hazards. Therefore, fire alarms have become a major issue for many modern buildings. In many buildings, fire alarm systems are equipped with wired or wireless sensors as part of an overall building management system [1]. A building fire alarm system consists of sensors, data acquisition and processing modules. Fire detection sensors are the automatic components of the fire alarm system, including sensors for light, smoke, temperature, gas and humidity [2]. They are able to promptly detect the physical and chemical information generated by a fire, such as the light (flame), smoke (smoke particles), heat (temperature), gas (combustion gas) and moisture (air humidity). They can also accurately transmit the physical or chemical information into an electric signal, then rapidly send the signal to the fire alarm controller. This is the process of data collection. There are two kinds of collectors of building fire information: a single-sensor detector and a multi-sensor detector [3; 4; 5]. A single-sensor fire detector is vulnerable to interference, such as dust, electromagnetism, water vapor, air, light, vibration and other environmental conditions. It cannot effectively distinguish between the early fire signals and environmental interference signals, so it cannot send early fire warnings correctly. According to statistics from the 1990s, smoke detectors in the United States had an 85% false alarm rate. In Switzerland, the correct alarm rate (true positives) of single-sensor detectors increased from 9% in 1995 to 15% in 2000 [6]. Fire alarm systems currently use three or more sensors to improve the reliability of the system, in order to reduce the false negatives of the fire alarm [7]. When fire information coming from multi-sensor detectors is transmitted into the data center of the processing system, how to deal with the information becomes a major problem. A simple method is to use the maximum value of information to realize the warning. That is, when the observation values of one or more sensors exceed the threshold value, the warning system will be triggered. However, this mode increases the false alarm rate and greatly reduces credibility. In many cases, fire occurs when all of the observation values coming from the multiple sensors do not exceed the threshold value. How to fuse the information collected by a multi-sensor is an urgent problem of building fire alarm technology. Information fusion technology offers an effective method to combine multi-sensor information. In fact, the fusion process of multi-sensor information is a simulation of the integrated human brain functions for complex problems [8; 9]. The basic principle of multi-sensor fusion technology is to utilize the different and complementary sensors in a reasonable manner to make a reliable judgment. In the process of information fusion, certain algorithms and optimization criteria are needed. Combined with multi-sensor and information fusion technology, a multi-sensor intelligent fire monitoring system (MIFMS) can identify the type of fire accurately and improve the correct fire alarm rate. In Switzerland, the spread of MIFMS from 1995 to 1998 increased the correct alarm rate to 50%. The application of multi-sensor information fusion technology to a building fire alarm is mainly via an information fusion algorithm [10]. The algorithms of information fusion technology are predominantly the Bayesian information fusion method, D-S evidence theory, neural networks or fuzzy set theory methods. D-S evidence theory has been widely applied in many research areas. The basic idea of D-S evidence theory was firstly proposed by [PERSON] when he was studying statistical problems, which was then extended to a more general rule by [PERSON]. The D-S evidence theory has three main features. The first one is a condition more weakly satisfied than Bayes probability theory, that is to say, additive probability is not necessary.