• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.187-189
• /
• 2009

Position estimation in indoor is significant problem, because GPS which is usually used for outdoor positioning cannot be utilized to indoor positioning. Sensor network can be a solution for the positioning. Recently, chirp spread spectrum(CSS) specified in IEEE 802.15.4a provides an ability of ranging. Based on the results of the ranging, a position of a CSS node can be calculated by using trilateration. In this case, Kalman filter can be applied to the trilateration because of the measurement noise. In this paper, we apply the unscented Kalman filter for the trilateration. The trilateration can be represented to a nonlinear state space equation, and the unscented Kalman filter is suitable for nonlinear state space equation. Through the experimental results. we show the accuracy of the estimated position.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.3
• /
• pp.243-257
• /
• 2010

Rapid developments in wireless sensor networks have extended many applications, hence, many studies have developed wireless sensor network positioning systems for indoor environments. Among those systems, the Global Position System (GPS) is unsuitable for indoor environments due to Line-Of-Sight (LOS) limitations, while the wireless sensor network is more suitable, given its advantages of low cost, easy installation, and low energy consumption. Due to the complex settings of indoor environments and the high demands for precision, the implementation of an indoor positioning system is difficult to construct. This study adopts a low-cost positioning method that does not require additional hardware, and uses the received signal strength (RSS) values from the receiver node to estimate the distance between the test objects. Since many objects in indoor environments would attenuate the radio signals and cause errors in estimation distances, knowing the path loss exponent (PLE) in an environment is crucial. However, most studies preset a fixed PLE, and then substitute it into a radio propagation loss model to estimate the distance between the test points; such method would lead to serious errors. To address this problem, this study proposes a Path Loss Exponent Estimation Algorithm, which uses only four beacon nodes to construct a radio propagation loss model for an indoor environment, and is able to provide enhanced positioning precision, accurate positioning services, low cost, and high efficiency.

• The Journal of Korea Robotics Society
• /
• v.11 no.3
• /
• pp.127-132
• /
• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.06a
• /
• pp.364-367
• /
• 2007

We combined CC2431(Chipcon, Norway), as the platform for the Indoor Location Tracking, which follows Zigbee/IEEE802.15.4 standards in RSSI (Received Signal Strength Indicator) and Base Station Node and then, embodied Indoor Location Tracking System. CC2431 is composed of the Reference Node that transfer its current position at the designated place and the Blind Node. The Blind node receives the current position(X and Y coordinates) of the Reference Node fields which are being contiguous and also, calculates its current position and transfers it to the Base Station Node. The base station node is used for receiving the current position of blind node and passing its data to the PC as a gateway. We can make sure where is the Blind Node not only from the out-of-the-way place of the server side but from the outside in a real-time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.8
• /
• pp.1655-1666
• /
• 2015

In this paper, user-position estimation method is proposed by using a single camera for both indoor and outdoor environments. Conventionally, the GPS of RF-based estimation methods have been widely studied in the literature for outdoor and indoor environments, respectively. Each method is useful only for indoor or outdoor environment. In this context, this study adopts a vision-based approach which can be commonly applicable to both environments. Since the distance or position cannot be extracted from a single still image, the reference images pro-stored in image database are used to identify the current position from the single still image captured by a single camera. The reference image is tagged with its captured position. To find the reference image which is the most similar to the current image, the SURF algorithm is used for feature extraction. The outliers in extracted features are discarded by using RANSAC algorithm. The performance of the proposed method is evaluated for two buildings and their outsides for both indoor and outdoor environments, respectively.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.2
• /
• pp.211-218
• /
• 2020

The radio wave propagation characteristics of the transmitter and receiver position change in the indoor environment were predicted through simulation, then the results obtained through the transmission loss measurement were compared and analyzed with the simulation results. The conference room was chosen as the environment for measuring transmission loss, and the radio transmission characteristics of the two environments were compared by selecting the exhibition hall without interior decorations and fixtures. In each indoor environment, the position of the transmitter chose two cases. One located in the center of the front wall and the other in the center of the side wall, and the position of the receiver moved along the centerline of the conference room and the side wall, measuring the receiving power. For each change in transmitter-receiver position, received power of 3GHz and 6GHz band were measured and compared with the simulation forecast results. The changes in received power at each receiving point were analyzed according to the location of the transmitter and the frequency band variation.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.11
• /
• pp.1442-1447
• /
• 2018

Since the indoor location recognition system using RFID is a method for predicting the indoor position, an error occurs due to the surrounding environment such as an obstacle. In this paper, we plan to reduce errors using back propagation neural networks. The neural network adjusts and trains the connection values between the layers to reduce the error between the actual position of the object with the reader and the expected position of the object through the experiment. In this paper, we propose a method that uses the median method and the radiation method as input to the neural network. Among the two methods, we want to find out which method is more effective in recognizing the actual position in an environment with obstacles and reduce the error. Consequently, the method using the median has less error, and we confirmed that the more the number of data, the smaller the error.

• Journal of Sensor Science and Technology
• /
• v.16 no.2
• /
• pp.110-114
• /
• 2007

In this paper we present 3-D Navigation View, a three-dimensional visualization of indoor environment which serves as an intuitive and unified user interface for our developed indoor location tracking system via Virtual Reality Modeling Language (VRML) in web environment. The extracted user's spatial information from indoor location tracking system was further processed to facilitate the location indication in virtual 3-D indoor environment based on his location in physical world. External Authoring Interface (EAI) provided by VRML enables the integration of interactive 3-D graphics into web and direct communication with the encapsulated Java applet to update position and viewpoint of user periodically in 3-D indoor environment. As any web browser with VRML viewer plug-in is able to run the platform independent 3-D Navigation View, specialized and expensive hardware or software can be disregarded.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.2
• /
• pp.537-542
• /
• 2016

The demand for LBS (Location Based Services) is increasing in the development of communication and mobile technologies. Positioning technology is a core technology for LBS, because LBS is based on a position of each device or user. But positioning technology especially for indoor environments is getting a lot of attention. Indoor positioning errors usually occur seriously in indoor environments where APs (Access Points) are set in a very concentrated and complex arrangement. In order to reduce indoor positioning errors, we adopt DOP (Dilution of Precision) which reflects an APs configuration information. In this paper, we propose an enhanced indoor positioning method using IEEE 802.11 RSSI measurements and AP configuration information.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.1
• /
• pp.19-25
• /
• 2017

Recently, indoor LBS has been attracting much attention because of its promising prospect. One of key technologies for its success is indoor location estimation. A popular one for indoor positioning is to find the location based on the strength of received Wi-Fi signals. Since the Wi-Fi services are currently prevalent, it can perform indoor positioning without any further infrastructure. However, it is found that its accuracy depends heavily on the surrounding radio environment. To alleviate this difficulty, we present a novel indoor position technique employing the geomagnetic characteristics as well as Wi-Fi signals. The geomagnetic characteristic is known to vary according to the location. Therefore, employing the geomagnetic signal in addition to Wi-Fi signals is expected to improve the location estimation accuracy.