A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network

Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is used as a robust location estimator to locate the mobile station (MS) using the MS geo-fencing area data within 9 km from a serving base station. Extensive evaluations and comparisons have been performed, and a set of statistical parameters has been obtained. From the comparison of the proposed ANFIS estimator with the neural-network-based estimators, it is found that ANFIS estimator is faster and more robust. Its average computation time (ACT) is 0.076 sec. While the ACT for multilayer perceptron (MLP) and radial-based function (RBF) neural networks is 0.88 and 1.7, respectively. Whereas on comparing ANFIS with other techniques, it is found that in ANFIS estimator, 67 percent of the estimated location errors do not exceed 149 m, while these for the statistical, multiple linear regression, and geometric are 170, 280, and 2,346 m, respectively. Thus, the results clearly reveal that the proposed ANFIS estimator outperforms all other techniques.