원문정보
초록
영어
Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquitous. The user-based localization algorithm utilizes the information of the Received Signal Strength(RSS) from the surrounding access points(APs) to determine the user position. In this paper, focusing on the development of a user localization uses existing WiFi environment for its low cost and ease of deployment. We propose an indoor localization of WiFi based on support vector machines(ILW-SVM), and use the bilinear median interpolation method(BMIM) to reduce the calibration effort on creating fingerprint map while still retaining the accuracy of user localization. According to comparison of accuracy of three different kernel functions, choosing the radial basis function(RBF) as kernel function. In addition, we also propose improved ILW-SVM algorithm to solve the indoor localization that nearest neighbor points are not concentrated. At last, overall comparison of kNN, ILW-SVM and improved ILW-SVM in consideration of accuracy. Experimental results indicate that the proposed algorithm can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and stabilization.
목차
1. Introduction
2. Related Work
3. Indoor Localization System
3.1. Offline Training Phase
3.2. Online Localization Phase
4. Increasing Localization Accuracy by Using ILW-SVM
4.1. Support Vector Classification Model
4.2. Localization Based on Improved ILW-SVM
5. Experiment Results and Evaluation
5.1. Experimental Platform Building
5.2. Reconstruction Fingerprint Map by BMIM
5.3. Performance of ILW-SVM algorithm
5.4. Improved ILW-SVM: Expanded Real-time Measurement of RSS
5.5. Error Analysis of the BMIM
5.6. Comparison of Localization Error
6. Conclusion
Acknowledgements
References