• Journal of the Korea Safety Management & Science
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• v.20 no.2
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• pp.37-44
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• 2018

The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, and therefore the location determination technology has been placed in a very important position. This study suggests an algorithm that can provide the estimate of users' location by using smartphone sensors. And in doing so we will propose a methodology for the creation and update of indoor map through the more accurate position estimation using smartphone sensors such as acceleration sensor, gyroscope sensor, geomagnetic sensor and rotation sensor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.449-452
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• 2007

An intelligent robot with RSSI based indoor location estimation function was designed and implemented. A wireless sensor node was attached to the robot to received the location data from the indoor location estimation function. Spartan III was used as the main control device in the mobile robot. The current location data collected from the indoor location estimation system was transferred to the mobile robot and server through Zigbee/IEEE 802.15.4 wireless communication of the sensor node. Once the location data is received, the sensor node senses the direction of the robot head and directs the robot to move to its destination. Indoor location estimation intelligent robot is able to move efficiently and actively to the user appointed location by implementing the proposed obstacles avoidance algorithm. This system is able to monitor real-time environmental data and location of the robot using PC program. Indoor location estimation intelligent robot also can be controlled by executing the instructions sent from the PC program.

• 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 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.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1158-1168
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• 2020

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

• 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.13 no.7
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• pp.711-717
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• 2007

Location information will become increasingly important for future Pervasive Computing applications. Location tracking system of a moving device can be classified into two types of architectures: an active mobile architecture and a passive mobile architecture. In the former, a mobile device actively transmits signals for estimating distances to listeners. In the latter, a mobile device listens signals from beacons passively. Although the passive architecture such as Cricket location system is inexpensive, easy to set up, and safe, it is less precise than the active one. In this paper, we present a passive location system using Cricket Mote sensors which use RF and ultrasonic signals to estimate distances. In order to improve accuracy of the passive system, the transmission speed of ultrasound was compensated according to air temperature at the moment. Upper and lower bounds of a distance estimation were set up through measuring minimum and maximum distances that ultrasonic signal can reach to. Distance estimations beyond the upper and the lower bounds were filtered off as errors in our scheme. With collecting distance estimation data at various locations and comparing each distance estimation with real distance respectively, we proposed an equation to compensate the deviation at each point. Equations for proposed algorithm were derived to calculate relative coordinates of a moving device. At indoor and outdoor tests, average location error and average location tracking period were 3.5 cm and 0.5 second, respectively, which outperformed Cricket location system of MIT.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.2058-2065
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• 2010

This paper presents the sensor location and step estimation algorithm for personal navigation system (PNS). PNS has the disadvantage in that the position of the sensor must be fixed on a human body. Three-axis acceleration sensor is used to solve the disadvantage and to consider the real situation. We simplify the measurement data by using the band pass filter, witch It has the advantage in the detection of characteristic point. Through the detected characteristic points, it is possible to setup the parameter for the pattern detection. Depending on the sensor location, the parameters have the different type of noise covariance. Particularly, when the position of the sensor is changed, the impulse noise shows up. Considering the noise, we apply the recursive least square algorithm using the variable forgetting factors, which can classify the sensor location based on the estimated parameters. We performed the experiment for the verification of the proposed algorithm in the various environments. Through the experimental results, the effectiveness of the proposed method is verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.375-378
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• 2007

This paper describes indoor location estimation intelligent robot. It is loaded indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks. Spartan III(Xilinx, U.S.A.) is used as a main control device in the mobile robot and the current direction data is collected in the indoor location estimation system. The data is transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1195-1200
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• 2007

This paper describes indoor location estimation intelligent robot. Indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks were implemented in the robot. Spartan III(Xilinx, U.S.A.) was used as a main control device in the mobile robot and the current direction data was collected in the indoor location estimation system. The data was transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.