• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.775-780
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• 2013

Localization, or positioning, is gaining the increasing attention of researchers around the world. The location information, especially the indoor location, is important for navigation systems, heating and air conditioning systems, illumination adjustment, humidity control, robot service, and so on. In this paper, we propose a three-dimensional indoor localization technique using visible light. The main goal of our proposed scheme is to improve the accuracy of VLC-based indoor localization by utilizing multiple VLC transmitters. The simulation results validate the performance of our proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3166-3181
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• 2012

Localization of mobile station (MS) has now gained considerable attention. Since the hybrid measurements can help to improve the positioning accuracy, several hybrid localization methods have been proposed in the literature. However, the high performance estimator with the closed-form solution and complete performance analysis for time-of-arrival/signal strength (TOA/SS) localization technique is still an opening issue. Two TOA/SS localization algorithms with the closed-form solutions are proposed for the cases with or without uncertainty in the positions of base stations. Furthermore, performance analysis for the TOA/SS localization technique is presented. Both the theoretical variances and Cramer-Rao lower bounds (CRLBs) are derived and the relationship between the cases with or without uncertainty is given. The paper also proves that the TOA/SS scheme has a lower CRLB than the TOA (or SS) scheme. Theoretical analysis and simulations show that the proposed method can reach its CRLB.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.29-36
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• 2012

In Wireless Sensor Network(WSN)s, the detection of precise location of each node is essential for utilizing sensing data acquired from sensor nodes effectively. Among various location methods, the received signal strength(RSS) based localization scheme is mostly preferable in many applications because it can be easily implemented without any additional hardware cost. Since a RSS-based localization scheme is mainly affected by radio channel or obstacles such as building and mountain between two nodes, the localization error can be inevitable. To enhance the accuracy of localization in RSS-based localization scheme, a number of RSS measurements are needed, which results in the energy consumption. In this paper, a RSS based localization using Bayesian Compressive Sensing(BSS) with path-loss exponent estimation is proposed to improve the accuracy of localization in the energy-efficient way. In the propose scheme, we can increase the adaptative, reliability and accuracy of localization by estimating the path-loss exponents between nodes, and further we can enhance the energy efficiency by the compressive sensing. Through the simulation, it is shown that the proposed scheme can enhance the location accuracy of multiple unknown nodes with fewer RSS measurements and is robust against the channel variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.32-38
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• 2012

Each sensor node can know its location in several ways, if the node process the information based on its geographical position in sensor networks. In the localization scheme using GPS, there could be nodes that don't know their locations because the scheme requires line of sight to radio wave. Moreover, this scheme is high costly and consumes a lot of power. The localization scheme without GPS uses a sophisticated mathematical algorithm estimating location of sensor nodes that may be inaccurate. AHLoS(Ad Hoc Localization System) is a hybrid scheme using both GPS and location estimation algorithm. In AHLoS, the GPS node, which can receive its location from GPS, broadcasts its location to adjacent normal nodes which are not GPS devices. Normal nodes can estimate their location by using iterative triangulation algorithms if they receive at least three beacons which contain the position informations of neighbor nodes. But, there are some cases that a normal node receives less than two beacons by geographical conditions, network density, movements of nodes in sensor networks. We propose an indirect localization scheme for low-density sensor nodes which are difficult to receive directly at least three beacons from GPS nodes in wireless network.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.606-614
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• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• Journal of IKEEE
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• v.11 no.4
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• pp.297-306
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• 2007

A sensor network is composed of a large number of sensor nodes that are densely deployed in a field. Each sensor performs a sensing task for detection specific events. After detecting this event, location information of the sensor node is very important. Range-based scheme of the proposed approaches typically achieve high accuracy on either node-to-node distances or angles, but this scheme have a drawback because all sensor nodes have the special hardware. On the other hand, range-free scheme provides economic advantage because of no needed hardware even if that leads to coarse positioning accuracy. In this paper, we propose a range-free localization algorithm without range information in wireless sensor networks. This is a range-free approach and uses a small number of anchor nodes and known sensor nodes. This paper develops a localization mechanism using the geometry conjecture (perpendicular bisector of a chord). The conjecture states that a perpendicular bisector of a chord passes through the center of the circle.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1448-1451
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• 2004

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.