Abstract
Accurate target localization technology plays a very important role in ensuring mine safety production and higher production efficiency. The localization accuracy of a mine localization system is influenced by many factors. The most significant factor is the non-line of sight (NLOS) propagation error of the localization signal between the access point (AP) and the target node (Tag). In order to improve positioning accuracy, the NLOS error must be suppressed by an optimization algorithm. However, the traditional optimization algorithms are complex and exhibit poor optimization performance. To solve this problem, this paper proposes a new method for mine time of arrival (TOA) localization based on the idea of comprehensive optimization. The proposed method utilizes particle filtering to reduce the TOA data error, and the positioning results are further optimized with fingerprinting based on the Manhattan distance. This proposed method combines the advantages of particle filtering and fingerprinting localization. It reduces algorithm complexity and has better error suppression performance. The experimental results demonstrate that, as compared to the symmetric double-sided two-way ranging (SDS-TWR) method or received signal strength indication (RSSI) based fingerprinting method, the proposed method has a significantly improved localization performance, and the environment adaptability is enhanced.
Highlights
The Internet of Things and pervasive computing technology have continually progressed since their inception [1, 2]
This paper proposes a time of arrival (TOA) fingerprinting localization method based on particle filtering
It is evident from the results that the proposed TOA fingerprinting localization method based on particle filtering greatly improves both the positioning accuracy and the root mean square error in the line corridor non-line of sight (NLOS) propagation condition
Summary
The Internet of Things and pervasive computing technology have continually progressed since their inception [1, 2]. The Internet of Things technology has become increasingly used in a variety of fields. Internet of Things technology for the mining industry was developed in order to obtain the status of coal mining and control mine production safety. Determining how to achieve an accurate localization of mine underground personnel has become a popular research topic. Mine target localization usually utilizes the range-based method; that is, the location estimation is based on the distance measurement. Some researchers used range-free methods to solve the problem of target localization. Problems such as node connectivity [4, 5] and beacon drift [6] are new research fields in the localization algorithm
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