MS Location Estimation with Genetic Algorithm

Huang

et al. 2020

Sensors

Self Cite

With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.

Section: Related Workmentioning

confidence: 99%

Section: The Proposed Abc-based Location Estimationmentioning

confidence: 99%

MS Location Estimation Based on the Artificial Bee Colony Algorithm

Huang

et al. 2020

Sensors

Self Cite

“…The authors of this article proposed a new positioning algorithm based on GA to determine the MS location in an NLOS environment. 33 Because PSO performs better than GA, [34][35][36] it is particularly suitable for determining the MS location in a wireless positioning system. PSO is a simpler search method because of its simple conception, easy implementation, and efficient computation.…”

Section: Psomentioning

confidence: 99%

A non-line-of-sight error mitigation method for location estimation

International Journal of Distributed Sensor Networks

2017

Self Cite

Because there are always non-line-of-sight effects in signal propagation, researchers have proposed various algorithms to mitigate the measured error caused by non-line-of-sight. Initially inspired by flocking birds, particle swarm optimization is an evolutionary computation tool for optimizing a problem by iteratively attempting to improve a candidate solution with respect to a given measure of quality. In this article, we propose a new location algorithm that uses time-of-arrival measurements to improve the mobile station location accuracy when three base stations are available. The proposed algorithm uses the intersections of three time-of-arrival circles based on the particle swarm optimization technique to give a location estimation of the mobile station in non-line-of-sight environments. An object function is used to establish the nonlinear relationship between the intersections of the three circles and the mobile station location. The particle swarm optimization finds the optimal solution of the object function and efficiently determines the mobile station location. The simulation results show that the proposed algorithm performs better than the related algorithms in wireless positioning systems, even in severe non-line-of-sight propagation conditions.

“…And we can obtain the optimal chrome. The part of estimation method proposed by GA can be found in [17]. The following steps describe the HTGA approach, and Figure 1 shows the signal flow diagram.…”

Section: The Hybrid Taguchi-genetic Algorithm (Htga)mentioning

confidence: 99%

“…GA has been utilized in many applications in a variety of fields such as control engineering, aeronautics, image processing, and structure analysis. The authors of this paper proposed a novel positioning algorithm based on GA to locate MS when three BSs are available for location purpose [17].…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Taguchi-Genetic Algorithm for Mobile Location

International Journal of Distributed Sensor Networks

Lin

Lee

et al. 2014

Self Cite

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.