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.
Highlights
The mobile positioning is an important research topic in wireless communication
Five thousand independent trials are performed for each simulation
We propose the schemes based on hybrid Taguchi-genetic algorithm (HTGA) to estimate mobile station (MS) location from three base stations (BSs)
Summary
The mobile positioning is an important research topic in wireless communication. In recent years, it has gained considerable attention. The hybrid Taguchi-genetic algorithm (HTGA) was proposed by Jyh-Horng Chou. It combines both the Taguchi method [18,19,20] and the GA [21]. HTGA has smaller standard deviations of function values than the orthogonal genetic algorithm with quantization (OGA/Q) proposed by [25]. The proposed algorithm utilizes the intersections of three TOA circles, based on HTGA, to estimate the MS location in NLOS environments. Simulation results will show that we proposed a better method of location estimation compared with the Taylor series algorithm (TSA), linear lines of position algorithm (LLOP), and RSA.
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