• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2968-2974
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• 2014

When lighting has installed indoor, we control lighting using human-detecting sensors for people who pass at night and places that are lack of quantity of light. However, malfunction can be caused by positions of inappropriate sensors, and in the case of passages of big buildings, it is a problem that even after a person pass, light apparatuses are turned on for a long time. In this paper, we propose lighting control system based on location positioning for energy saving that control lighting in accordance with passers's position through indoor location positioning. This system use the fingerprinting technology that is one of the location positioning technologies and RSSI data that is collected by a smart device. Using those, it can turn on only lightings that are included in the positioned location and reduce unnecessary power consumption. As a result of experiment, on condition that four people were existing and illumination was 308 lux, we assured reduction effect of 49 Wh.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.335-345
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• 2017

Recently, location systems using RFID technology have been studied in indoor environments. However, the existing techniques require high computational cost to compute the location of a moving object because they compare the location proximity of all reference tags and objects. In this paper, we propose an RFID based location positioning scheme using event filtering, which reduces the computation cost of calculating the locations of moving objects while maintaining the accuracy of location estimation. In addition, we propose an incremental location update policy to reduce the location update cost for moving objects. We also compare the proposed scheme with one of the localization schemes, LANDMARC using a performance evaluation. As a result, the proposed scheme outperforms LANDMARC in terms of the computational cost of location estimation. The proposed scheme also reduces the cost of location update by using the RFID-based update policy.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.149-149
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• 2020

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.209-215
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• 2012

This paper deals with the construction of system for correct location determination of fixed tag. We adapted to construct the above method. Also we adapted the several filtering method. This system was constructed through using of several filtering methods to decrease the location determination error and fingerprint method which is composed of training phase and positioning phase. We constructed this system using Labview 2010 and MS-SQL 2000 as database. This system results in less location determination error than least square method, triangulation positioning method, and other fingerprint methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.323-334
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• 2014

In this paper, we proposed indoor positioning system with improved accuracy. The proposed indoor location measurement system is based pedestrian location measurement method that use the embedded sensor of smartphone. So, we do not need wireless external resources, such as GPS or WiFi signals. The conventional methods measure indoor location by generating a movement route of pedestrian by step and direction recognition. In this paper, to correct the direction sensor error, we use the common feature of the normal indoor floor map that the indoor path is lattice-structured. And we quantize moving directions depending on the direction of indoor path. In addition, we propose moving direction measuring method using geomagnetic sensor and gyro sensor to improve the accuracy. Also, the proposed step detection method uses angle and accelerometer sensors. The proposed step detection method is not affected by the posture of the smartphone. Direction errors caused by direction sensor error is corrected due to proposed moving direction measuring method. The proposed location error correction method corrects location error caused by step detection error without the need for external wireless signal resources.

• ETRI Journal
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• v.38 no.3
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• pp.560-567
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• 2016

To solve the problem of parameter optimization in image sensor-based visible light positioning systems, theoretical limits for both the location and the azimuth angle of the image sensor receiver (ISR) are calculated. In the case of a typical indoor scenario, maximum likelihood estimations for both the location and the azimuth angle of the ISR are first deduced. The Cramer-Rao Lower Bound (CRLB) is then derived, under the condition that the observation values of the image points are affected by white Gaussian noise. For typical parameters of LEDs and image sensors, simulation results show that accurate estimates for both the location and azimuth angle can be achieved, with positioning errors usually on the order of centimeters and azimuth angle errors being less than $1^{\circ}$. The estimation accuracy depends on the focal length of the lens and on the pixel size and frame rate of the ISR, as well as on the number of transmitters used.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.789-796
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• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.795-800
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• 2008

The paper presents research for the established experiment environment of multi vehicle robot, localization algorithm that is based on vehicle control, and path tracking. The established experiment environment consists of ultrasonic positioning system, vehicle robot, server and wireless module. Ultrasonic positioning system measures positioning for using ultrasonic sensor and generates many errors because of the influence of environment such as a reflection of wall. For a solution of this fact, localization algorithm is proposed to determine a location using vehicle kinematics and selection of a reliable location data. And path tracking algorithm is proposed to apply localization algorithm and LOS, finally, that algorithms are verified via simulation and experimental

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.962-967
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• 2006

Indoor positioning is highlighted recently, and various kinds of indoor positioning systems are under developments. Since positioning systems have their own characteristics, proper positioning scheme should be chosen according to the required specifications. Positioning methods are classified into time-based and angle-based one. This paper presents the error analysis of time-based and angle-based location methods. Because measurements of these methods are nonlinear, linearizations are needed in both cases to estimate the user position. In the linearization, Gauss-Newton method is used in both cases. To analyze the position error, we investigate the error ellipse parameters that include eccentricity, rotation angle, and the size of ellipse. Simulation results show that the major axes of TOA and AOA method lie in different quadrants at most region of workspace, especially where the geometry is poor. When the TOA/AOA hybrid is employed, it is found that the error ellipse is reduced to the intersection of ellipses of TOA and AOA.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.