1.1. Background of the Subject
Report that China's coal production in the year 2015 up to about 36.8 tons, accounting for about 1/2 of the world's total output. China's energy structure is more special, so the coal occupies an important position in the field of energy. The coal industry can be said that China's basic industries, as the pillars of economic sustainable development. With the rapid development of the coal mine, underground safety incidents are increasingly frequent. For a long time, the mining of the narrow space, harsh environment so that underground mining risk index would have been high; In addition, the underground environmental monitoring is not comprehensive, not timely, or there is a blind spot monitoring, will directly affect the effective treatment of the accident and the rescue work after the accident.
Under the mine, due to the narrow roadway and there are a lot of corners and branches, at the same time, there are a lots of coal dust in air and accompanied by water, so that both sides of the road can not be a lot of cable laying cable transmission. With the development of pervasive computing, sensing technology, large data and wireless technology, Wireless Sensor Network (WSN) (Song, Wang & Zhou, 2007) is playing an important role in all fields, and it has played an increasing role in various fields.
There are two kinds of localization systems in WSN at present: Location based on non-ranging and Location based on ranging (Zhou, 2015). The former use network connectivity to complete the positioning, the defect is relatively low accuracy; the latter depends on the measured distance between the nodes or angle to achieve positioning results, positioning accuracy is relatively high. Based on the ranging algorithm commonly used TOA, TDOA, AOA, and RSSI, RSSI (Signal Strength Indication) location method uses the empirical transmission loss model to calculate the distance between nodes, and then uses the distance to calculate the unknown node location according to the relevant positioning algorithm. It does not require additional hardware devices. It has the advantages of low cost and easy realization. It has become a hot research topic at home and abroad. Aiming at the disadvantage that the RSSI is greatly influenced by the environment, the researchers have made great efforts to improve the precision of the centroid location algorithm based on RSSI. The literature (Cheng & Tan, 2012) used genetic algorithm to improve the centroid algorithm to locate, however, it has many disadvantages such as large computation and time-consuming. In literature (Feng, Wang & Zhang, 2012), particle swarm optimization (PSO) is used to optimize the centroid location. PSO is easier to implement than genetic algorithm, its calculation is small, convergence is fast, but the accuracy is not greatly improved. The method of GSA is adopted in the literature (Chen, Chen & Tang, 2015), but the particle is easy to fall into local oscillation and get the local optimum.
In this context, this paper proposes an optimized downhole weighting centering personnel location scheme based on GSA-PSO. Under the condition of knowing the characteristics of the wireless communication in the mining tunnel, we use the triangle weighted centroid localization algorithm to monitor the personnel coordinates on the basis of RSSI. Considering the randomness of the real-time movement and the dynamic change of the environment, the PSO-GSA optimization model is introduced to optimize the estimated coordinates and related parameters, which reduces the dependency of the algorithm on the application environment, achieves the dynamic positioning effect and improves the positioning accuracy.