• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.666-669
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• 2000

Localization is the process of aligning the robot's local coordinates with the global coordinates of a map. A mobile robot's location is basically computed by a dead reckoning scheme, but this position information becomes increasingly inaccurate during navigation due to odometry errors. In this paper, the method of building a map of a robot's environment using ultrasonic sensor data and the occupancy grid map scheme is briefly presented. Then, the search and matching algorithms to compensate for the odometry error by comparing the local map with the reference map are proposed and verified by experiments. It is shown that the compensated error is not accumulated and exists within the limited range.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.15-22
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• 2006

This paper presents an efficient localization scheme for an indoor mobile robot using RFID tags on the floor. The mobile robot carries an RFID reader at the bottom, which reads the RFID tags on the floor to localize the mobile robot. Each RFID tar on the floor stores its own absolute position which is used to calculate the position and velocity of the mobile robot. Locating the RFID tags on the floor, which constructs an intelligent sensor space, may require several factors to be considered: economics feasibility and accuracy. In this paper, the optimal allocation scheme of the RFID tags on the floor to satisfy the accuracy constraint has been proposed and verified by the experiments. Based on the RFID reading, the mobile robot navigation has been successfully demonstrated to avoid obstacles and to reach the goal within a pre-specified time.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.159-162
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• 2002

This paper presents a modified localization scheme of a mobile robot. When it navigates, the position error of a robot is increased and doesn't go to a goal point where the robot intends to go at the beginning. The objective of localization is to estimate the position of a robot precisely. Many algorithms were developed and still are being researched for localization of a mobile robot at present. Among them, a localization algorithm named continuous localization proposed by Schultz has some merits on real-time navigation and is easy to be implemented compared to other localization schemes. Continuous Localization (CL) is based on map-matching algorithm with global and local maps using only ultrasonic sensors for making grid maps. However, CL has some problems in the process of searching the best-scored-map, when it is applied to a mobile robot. We here propose fast and powerful map-matching algorithm for localization of a mobile robot by experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2627-2647
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• 2022

The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.55-63
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• 2013

In cognitive radio networks, acquirement of position information of primary user is very important to secondary network since localization information of primary users can be utilized for improving the spectrum efficiency of secondary network and for avoiding harmful interference to primary users by using proper power control. Among various location methods, Received Signal Strength (RSS)-based localization has been widely used for distance measurements in the location detection process despite its inherent inaccuracy because it can be easily implemented without any additional hardware cost. In the RSS-based localization, the distance is measured by the received signal strength, and distance error can be caused by many factors such as fading, shadowing and obstacle between two nodes. In the paper, therefore we propose a localization scheme based on estimation of path-loss exponent to localize the location of primary users more accurately. Through simulations, it is shown that the proposed scheme can provide less localization error and interference rate to primary users than other schemes.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.45-56
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• 2013

The precise identification of vehicle positions, known as the vehicle localization problem, is an important requirement for building intelligent vehicle ad-hoc networks (VANETs). To solve this problem, two categories of solutions are proposed: stand-alone and data fusion approaches. Compared to stand-alone approaches, which use single information including the global positioning system (GPS) and sensor-based navigation systems with differential corrections, data fusion approaches analyze the position information of several vehicles from GPS and sensor-based navigation systems, etc. Therefore, data fusion approaches show high accuracy. With the position information on a set of vehicles in the preprocessing stage, data fusion approaches is used to estimate the precise vehicular location in the local map building stage. This paper proposes an efficient local map building scheme, which increases the accuracy of the estimated vehicle positions via V2V communications. Even under the low ratio of vehicles with communication modules on the road, the proposed local map building scheme showed high accuracy when estimating the vehicle positions. From the experimental results based on the parameters of the practical vehicular environments, the accuracy of the proposed localization system approached the single lane-level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.268-274
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• 2013

In this paper, the performance analysis of optics-based sensor node localization using ToF (Time of Flight) scheme is conducted. Generally, the position of the sensor node is calculated on the base station. And the base station scans neighboring sensor nodes with a laser. The laser which is reflected from one sensor node, however, can be reached to the base station at different angles according to the scanning resolution. This means that the error of the reached angle can increase and one node may be recognized as different nodes. Also the power of laser can decrease because the laser signal spread. Thus the sensor node which is located at a long distance from the base station cannot be detected. In order to overcome these problems which can be occurred in localization using ToF, the beam spot, the scanning resolution, the size of reflector and the power of laser at the sensor node were analyzed. It can be expected that the consequence of analysis can be provided in acquisition of accurate position of sensor node and construction of optics-based sensor node localization system.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.77-85
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• 2013

this study examined hybrid Time of Arrival/Angle of Arrival (TOA/AOA) localization technique in a cellular network. Based on the linearized equations from the TOA and AOA measurements, the weighted least square (WLS) method is proposed to obtain the location estimation of a mobile station (MS) by analyzing the statistical properties of the error vector in Line of Sight (LOS) and Non-line of Sight (NLOS) environments, respectively. Moreover, the precise expression of the Cramer-Rao lower bound (CRLB) for hybrid TOA/AOA measurements in different LOS/NLOS conditions was derived when the LOS error is a Gaussian variable and the NLOS error is an exponential variable. The idea of cooperative localization is proposed based on the additional information from short-range communication among the MSs in fourth generation (4G) cellular networks. Therefore, the proposed hybrid TOA/AOA WLS method can be improved further with the cooperative scheme. The simulation results show that the hybrid TOA/AOA method has better performance than the TOA only method, particularly when the AOA measurements are accurate. Moreover, the performance of the hybrid TOA/AOA method can be improved further by the cooperative scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.19-25
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• 2013

In this paper, we proposed an enhanced monopulse MIMO radar system for the tactical scenario where the ground receivers are connected wireless backhaul and closely spaced. By applying the ${\alpha}{\beta}$ filter to the conventional monopulse MIMO radar, we show that the localization performance can be improved significantly. We also propose an efficient localization algorithm for a system with lower rate feedback. Using numerical simulations, we demonstrate that the proposed scheme can improve the localization performance while reducing the feedback over conventional scheme.