Knowing the precise and real-time location of underground mining workers is essential for their health and safety in any emergency. However, the standard Global Positioning System (GPS) is insufficient for such indoor environments as it requires new infrastructure based on different technologies and algorithms. Instead, Radio Frequency Identification (RFID)-based real-time indoor localization systems and a hybrid algorithm are developed. The received-signal-strength (RSS) based positioning techniques are investigated and applied in indoor environments. A unique hybrid approach based on fingerprinting is proposed and developed to solve the disadvantages of the existing techniques. Consequently, the accuracy of this one-of-a-kind algorithm is found to be 2.52 m in an office and 3.13 m in an underground mine. We also compared the proposed hybrid algorithm to the Weighted K-Nearest Neighbor (WKNN). WKNN, on the other hand, has an accuracy of 4.01 m in the office and 4.33 m in underground mining environments.

了解地下采矿工人的精确和实时位置对于他们在任何紧急情况下的健康和安全至关重要。然而，标准的全球定位系统 (GPS) 不足以应对此类室内环境，因为它需要基于不同技术和算法的新基础设施。相反，开发了基于射频识别 (RFID) 的实时室内定位系统和混合算法。基于接收信号强度（RSS）的定位技术在室内环境中进行了研究和应用。提出并开发了一种独特的基于指纹识别的混合方法，以解决现有技术的缺点。因此，这种独一无二的算法在办公室中的精度为 2.52 m，在地下矿井中为 3.13 m。我们还将提出的混合算法与加权 K 近邻 (WKNN) 进行了比较。另一方面，WKNN 在办公室的精度为 4.01 m，在地下采矿环境中的精度为 4.33 m。