The Hybrid Taguchi-Genetic Algorithm for Mobile Location

Abstract

To estimate the mobile location is an important topic in wireless communication. It is well known that non-line-of-sight (NLOS) problem is the most pivotal part that causes the estimated error. When we transmit the signal from mobile station (MS) to base stations (BSs), the direct path between MS and BS is sealed off by some obstacles, and the signal measurements will measure the error due to the signal reflection or diffraction. The hybrid Taguchi-genetic algorithm (HTGA) combines the Taguchi method with the genetic algorithm (GA). In this paper, we bring up a novel HTGA algorithm that utilizes time of arrival (TOA) measurements from three BSs to locate MS. The proposed algorithm utilizes the intersections of three TOA circles based on HTGA to estimate the MS location. Finally, we compare HTGA with GA and find that the Taguchi algorithm can enhance genetic algorithm. We also can find that the average convergence of generation number will not be affected no matter which propagation models we use. Obviously HTGA is more robust, statistically sound, and quickly convergent than the other algorithms. The simulation results show that the HTGA can converge more quickly than GA and furthermore the HTGA can enhance the accuracy of the mobile location.