• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.228-233
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• 2010

In this paper, the iteration localization algorithm that NLOS signal is iteratively removed to get the exact location in the wireless sensor network is proposed. To evaluate the performance of the proposed algorithm, TDOA location estimation method is used, and readers are located on every 150m intervals with rectangular shape in $300m{\times}300m$ searching field. In that searching field, the error distance is analyzed according to increasing the number of iteration, sub-blink and the estimated sensor node locations which are located in the iteration range. From simulation results, the error distance is diminished according to increasing the number of the sub-blink and iteration with the proposed location estimation algorithm in NLOS environment. Therefore, to get more accurate location information in wireless sensor network in NLOS environments, the proposed location estimation algorithm removing NLOS signal effects through iteration scheme is suitable.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.125-130
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• 2013

Compared to OWR (One-Way Ranging) method that requires precise network time synchronization, TWR (Two-Way Ranging) method has advantages in building an indoor WPAN (Wireless Personal Area Network) location system with lower cost. However, clock offsets of nodes in WPAN system should be eliminated or compensated to improve location accuracy of the TWR method. Because conventional clock offset elimination methods requires multiple TWR transactions to reduce clock offset, they produce network traffic burden instead. This paper presents a clock offset estimation method that can reduce clock offset error with a single TWR transaction. After relative clock offsets of sensor nodes are estimated, clock offsets of mobile tags are estimated using a single TWR communication. Simulation results show that location accuracy of the proposed method is almost similar to the conventional clock offset elimination method, while its network traffic is about a half of the conventional method.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.101-108
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• 1999

In this paper, a real time diagnostic algorithm for estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). First, ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. Second, IEM by use of the arrival time method estimates the impact location of loose parts. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.395-401
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• 2019

This paper proposes a method for estimating the location of a magnetized tip that is inside a non-transparent space or body by using arrayed giant magnetoresistance (GMR) sensors. In general, an object located in such an opaque space can be detected using X-rays, magnetic fields, ultra-sonic sensors, etc., depending on its characteristics. X-ray is mostly used for medical purposes but frequent exposure to it could cause harm to patients as well as doctors. In this study, how well a GMR sensor is applicable instead of an X-ray is investigated. The sensor's voltage output is experimentally fitted to distance with a relationship of 3rd degree polynomial. To detect a small magnetized tip with 900 Oe inside a human body, a 2×2 arrayed GMR sensor and a location estimation algorithm based on information acquired from four sensors is developed. Evaluation tests show that the suggested method is applicable to limited cases with a distance less than 33-55 mm, and the location of a magnet tip is estimated relatively well with an error less than 1.5 mm.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.752-759
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• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.329-334
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• 2011

This paper presents the method for location estimation with obstacle tracking method. A laser scanner is used to implement the system, and we assume that the map information is known. We matches the measurement of the laser scanner to estimate the location of the robot by using sequential monte carlo (SMC) method. After estimating the robot's location, the pose of obstacles are detected and tracked, hence, we can predict the collision risk of them. Finally, we present the experiment results to verify the proposed method.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.297-306
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• 2012

In this paper, we propose a Time Delay Traceback Scheme for the TDOA location estimation performance enhancement in NLOS environment and analyze the performance in various conditions. We place multiple readers in a square($300m{\times}300m$) searching area for reuse of received signal. Also, we use more active NLOS reader detection methode for NLOS error mitigation. when NLOS time delay 70 m, the number of the NLOS reader is 3 and the received sub-blinks number 3, proposed time delay trace-back scheme improve the RMSE about 16 m. From these results, we confirm that the proposed time delay traceback scheme is well-suited for the high precision location estimation to offer the location based service.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.163-168
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• 2008

Navigation system based on indoor location estimation is one of the core technologies in mobile robot systems. Wireless sensor network has great potential in the indoor location estimation due to its characteristics such as low power consumption, low cost, and simplicity. In this paper we present an algorithm to estimate the indoor location of mobile robot based on wireless sensor network and fuzzy modeling. ZigBee-based sensor network usually uses RSSI(Received Signal Strength Indication) values to measure the distance between two sensor nodes, which are affected by signal distortion, reflection, channel fading, and path loss. Therefore we need a proper correction method to obtain accurate distance information with RSSI. We develop the fuzzy distance models based on RSSI values and an efficient algorithm to estimate the robot location which applies to the navigation algorithm incorporating the time-varying data of environmental conditions which are received from the wireless sensor network.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.370-375
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• 2006

This paper suggests the method for estimating a partial discharge (PD) location using divide of the inside transformer as a grid. The PD location is found swiftly and economically compared with the typical method detecting a PD. The reason is that the location of PD is detected in the section. The estimation of PD location is trained using the Neural Network. JavaNNS(Java Neural Network Simulator) and SNNS(Stuttgart Neural Network Simulator) are used for searching the location of PD. The simulation procedure is following, The transformer is assumed that the case is a regular hexahedron. The sensor is installed in a proper location. A section of PD location is set as a target, and training set is studied with several PD locations in the inside of the transformer. As a result of training process, the learning capability of neural network is excellent. The PD location is detected by division of internal structure of transformer and application of neural network.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.87-96
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• 2006

Selectivity estimation is one of the query optimization techniques. It is difficult for the previous selectivity estimation techniques for moving objects to apply the position change of moving objects to synopsis. Therefore, they result in much error when estimating selectivity for queries, because they are based on the extended spatial synopsis which does not consider the property of the moving objects. In order to reduce the estimation error, the existing techniques should often rebuild the synopsis. Consequently problem occurs, that is, the whole database should be read frequently. In this paper, we proposed a moving object histogram method based on quad tree to develop a selectivity estimation technique for moving object queries. We then analyzed the performance of the proposed method through the implementation and evaluation of the proposed method. Our method can be used in various location management systems such as vehicle location tracking systems, location based services, telematics services, emergency rescue service, etc in which the location information of moving objects changes over time.