Using Artificial Bee Colony Algorithm to Optimize Location Estimation of Mobile Station Based on Practical RSSI Value

• Main Authors: Nan-ChunHuang, 黃南鈞
• Other Authors: Jen-Fa Huang
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/h34fk7

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 102 === Since the rapid development of the wireless communication technology, the ability of how to estimate the location of mobile station (MS) is indispensable. By the accurately positioning, the location technology can be applied to various applications, like the E911 emergency assistance, security services, and intelligent transportation systems. The accuracy of MS location estimation depends on signal propagation environment closely. In practical, non-line-of-sight (NLOS) is existence everywhere and it leads to the error of signal measurement. Therefore, in a wireless location system, the main task is to cut down the error generated from NLOS environments. Artificial Bee Colony (ABC) algorithm is a widely used technique to solve problems in various areas. It is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm. MS location was estimated based on three received signal strength indication (RSSI) measurements in this thesis. To enhance the prediction accuracy, the proposed scheme employs the object function to mitigate the additional NLOS error. In this work, searching the optimal MS location with best object function value is accomplished by ABC algorithm. To deal with the practical RSSI measurements, different calculation principles and two stages are designed. To prove the reliability and feasibility of the proposed location algorithm, the numerical simulations and practical measurements was implemented simultaneously. Different error distributions were used for numerical simulations, and the performance of the proposed scheme was compared with other existing methods. The results with simulation and measurement show that the proposed ABC-based location algorithm offers the MS location with best accuracy and the efficient positioning procedure.