SVM-based Models for Mobile Users' Initial Position Determination

Abstract

A large interest in developing commercial Location-Based Services (LBS) and the necessity of implementing emergency call services, have led to the intensive development of techniques for mobile users' localisation. In this paper, a Public Land Mobile Networks (PLMN) -based technique for initial position determination is proposed as an alternative to satellite-based methods in environments with obstructed satellite signals. Two positioning models, based on handset available Received Signal Strength (RSS) measurements from Global System for Mobile Communications (GSM) base stations and the use of Support Vector Machine (SVM) algorithms, are proposed. Performances of proposed models are verified using field measurements, collected in a suburban environment. Models are analysed in terms of positioning accuracy, complexity and latency, and compared to some other promising PLMN-based techniques. Using proposed SVM-based positioning models a median error of 4·3 m-6·2 m and latency of less than a second can be achieved. K E Y

The Knowledge Mapping of Mobile Commerce Research: A Visual Analysis Based on I-Model

“…Compared with the neural network method, SVM take global variables into account. We can introdece SVM into mobile commerce acceptance model [137]. Most of the research discovered the key influence factor of consumer acceptance behavior.…”