• The Journal of Korea Robotics Society
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• v.9 no.2
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• pp.117-123
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• 2014

Localization is one of the essential tasks necessary to achieve autonomous navigation of a mobile robot. One such localization technique, Monte Carlo Localization (MCL) is often applied to a digital surface model. However, there are differences between range data from laser rangefinders and the data predicted using a map. In this study, commonly observed from air and ground (COAG) features and candidate selection based on the shape of sensor data are incorporated to improve localization accuracy. COAG features are used to classify points consistent with both the range sensor data and the predicted data, and the sample candidates are classified according to their shape constructed from sensor data. Comparisons of local tracking and global localization accuracy show the improved accuracy of the proposed method over conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.215-236
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• 2017

Obtaining accurate location information is important in practical applications of wireless sensor networks (WSNs). The distance vector hop (DV-Hop) is a frequently-used range-free localization algorithm in WSNs, but it has low localization accuracy. Moreover, despite various improvements to DV-Hop-based localization algorithms, maintaining a balance between high localization accuracy and good stability and convergence is still a challenge. To overcome these shortcomings, we proposed an improved DV-Hop localization algorithm based on the bat algorithm (IBDV-Hop) for WSNs. The IBDV-Hop algorithm incorporates optimization methods that enhance the accuracy of the average hop distance and fitness function. We also introduce a nonlinear dynamic inertial weight strategy to extend the global search scope and increase the local search accuracy. Moreover, we develop an updated solutions strategy that avoids premature convergence by the IBDV-Hop algorithm. Both theoretical analysis and simulation results show that the IBDV-Hop algorithm achieves higher localization accuracy than the original DV-Hop algorithm and other improved algorithms. The IBDV-Hop algorithm also exhibits good stability, search capability and convergence, and it requires little additional time complexity and energy consumption.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.796-805
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• 2016

In many wireless sensor network (WSN) applications, the information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in node localization is mostly geared towards multi-hop range-free localization algorithms to achieve accuracy by minimizing localization errors between the node's actual and estimated position. The existing localization algorithms are focused on improving localization accuracy without considering efficiency in terms of energy costs and algorithm convergence time. In this work, we show that our proposed localization scheme, called DV-maxHop, can achieve good accuracy and efficiency. We formulate the multi-objective optimization functions to minimize localization errors as well as the number of transmission during localization phase. We evaluate the performance of our scheme using extensive simulation on several anisotropic and isotropic topologies. Our scheme can achieve dual objective of accuracy and efficiency for various scenarios. Furthermore, the recently proposed algorithms require random uniform distribution of anchors. We also utilized our proposed scheme to compare and study some practical anchor distribution schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2567-2586
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• 2012

Localization of sensor nodes is a key technology in Wireless Sensor Networks(WSNs). Trilateration is an important position determination strategy. To further improve the localization accuracy, a novel Trilateration based on Point In Triangle testing Localization (TPITL)algorithm is proposed in the paper. Unlike the traditional trilateration localization algorithm which randomly selects three neighbor anchors, the proposed TPITL algorithm selects three special neighbor anchors of the unknown node for trilateration. The three anchors construct the smallest anchor triangle which encloses the unknown node. To choose the optimized anchors, we propose Point In Triangle testing based on Distance(PITD) method, which applies the estimated distances for trilateration to reduce the PIT testing errors. Simulation results show that the PIT testing errors of PITD are much lower than Approximation PIT(APIT) method and the proposed TPITL algorithm significantly improves the localization accuracy.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.3-7
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• 2006

Indoor localization is getting more and more importance with the increasing demand for location based service. Location based service necessarily requires the information about customers' locations to provide them the right service according to their changing locations. To satisfy that requirement, GPS is used to achieve outdoor localization. However, there is no leading technology to achieve indoor localization. Indoor localization through UWB wave and TDOA algorithm is considered as the most accurate method until now. In implementing that method, configuration of base stations that serve as control points affects the localization accuracy. Thus, this paper discusses about optimal configuration of base stations. The variation in localization accuracy according to spatial relationship between an object and base stations Is mentioned through SEP also.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.901-920
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• 2015

Wireless Sensor Networks (WSNs) are widely used in geographically isolated applications like military border area monitoring, battle field surveillance, forest fire detection systems, etc. Uninterrupted power supply is not possible in isolated locations and hence sensor nodes live on their own battery power. Localization of sensor nodes in isolated locations is important to identify the location of event for further actions. Existing localization algorithms consume more energy at sensor nodes for computation and communication thereby reduce the lifetime of entire WSNs. Existing approaches also suffer with less localization coverage and localization accuracy. The objective of the proposed work is to increase the lifetime of WSNs while increasing the localization coverage and localization accuracy. A novel intelligent unmanned aerial vehicle anchor node (IUAN) is proposed to reduce the communication cost at sensor nodes during localization. Further, the localization computation cost is reduced at each sensor node by the proposed intelligent arc selection (IAS) algorithm. IUANs construct the location-distance messages (LDMs) for sensor nodes deployed in isolated locations and reach the Control Station (CS). Further, the CS aggregates the LDMs from different IUANs and computes the position of sensor nodes using IAS algorithm. The life time of WSN is analyzed in this paper to prove the efficiency of the proposed localization approach. The proposed localization approach considerably extends the lifetime of WSNs, localization coverage and localization accuracy in isolated environments.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.28-38
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• 2014

This paper verifies the performance of Extended Kalman Filter(EKF) and MCL(Monte Carlo Localization) approach to localization of an underwater vehicle through experiments. Especially, the experiments use acoustic range sensor whose measurement accuracy and uncertainty is not yet proved. Along with localization, the experiment also discloses the uncertainty features of the range measurement such as bias and variance. The proposed localization method rejects outlier range data and the experiment shows that outlier rejection improves localization performance. It is as expected that the proposed method doesn't yield as precise location as those methods which use high priced DVL(Doppler Velocity Log), IMU(Inertial Measurement Unit), and high accuracy range sensors. However, it is noticeable that the proposed method can achieve the accuracy which is affordable for correction of accumulated dead reckoning error, even though it uses only range data of low reliability and accuracy.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.1-6
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• 2009

Localization of nodes is a key technology for application of wireless sensor network. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. We also suggest a criterion which is used to select mobile anchor node which involve in computing the position of nodes for improving localization accuracy. Weighted centroid localization algorithm is simple, and no communication is needed while locating. The localization accuracy of weighted centroid localization algorithm is better than maximum likelihood estimation which is used very often. It can be applied to many applications.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.204-213
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• 2008

In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.640-651
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• 2016

We propose an effective ToA-based localization method considering accuracy in indoor environments. The purpose of the localization system is to estimate the coordinates of the geographic location of target device. In indoor environments, accurately estimating the location of a target device is not easy due to various errors. The accuracy of wireless localization is influenced by NLOS errors. ToA-based localization measures the location of a target device using the distances between a mobile device and three or more base stations. However, each of the NLOS errors along a distance estimated from a target device to a base station is different because of dissimilar obstacles. To accurately estimate the target's location, an optimized localization process is needed in indoor environments. In this paper, effective ToA-based localization method process is proposed for improving accuracy in wireless sensor networks. Performance evaluations are presented, and the experimental localization system results are proved through comparisons of various localization methods with the proposed methods.