Modified Support Vector Machine for Wireless Localization
Received date: 2016-09-12
Revised date: 2016-11-27
Online published: 2017-11-30
Using support vector machine (SVM) for wireless localization suffers from instability of accuracy as the parameters are generally chosen based on experience. To solve the problem, we use simulated annealing (SA) to modify support vector machine (SA-SVM) to improve positioning accuracy. We obtain the training samples from simulation of the cellular communication system model to the SVM, and find the optimal SVM parameters in an iterative search based on SA. The obtained optimal parameters are then used in the positioning. Simulations show that, compared with the original SVM positioning method, SA-SVM method effectively improves localization accuracy, and therefore has application values.
YANG Jin-sheng, LIN Zhen-jun . Modified Support Vector Machine for Wireless Localization[J]. Journal of Applied Sciences, 2017 , 35(6) : 685 -692 . DOI: 10.3969/j.issn.0255-8297.2017.06.002
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