Jaehun Joo

Extended Kalman Filter for wireless LAN based indoor positioning

A WLAN (Wireless Local Area Network) based Extended Kalman Filter (EKF) method for indoor positioning is introduced in this paper. WLAN based indoor positioning is more economical than other methods because it does not require any special equipment dedicated to positioning. The most popular technique used for indoor positioning is the fingerprinting method, but the EKF method is easier to deploy because, unlike fingerprinting, it does not require a time consuming off-line phase. This paper also provides experimental comparisons of our EKF method with other indoor positioning methods.

Improvement of Kalman filters for WLAN based indoor tracking

Location Based Service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, Extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (Wireless Local Area Network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. This paper proposes a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. After analyzing the experimental results of it, we modified it to use K-NN method in the measurement stage of it. Then we propose to further improve the accuracy of indoor tracking by adjusting the parameter values referring to the map information. Experimental results comparing our method with other previous methods are discussed.

Protecting cultural heritage tourism sites with the ubiquitous sensor network

Cultural heritage tourism resources are vulnerable to natural and human disasters: once damaged or destroyed, it is hard to restore them to their original condition. This study examines an online and real-time early fire detection system, using advanced information technology and a ubiquitous sensor network to protect cultural heritage tourism resources. It notes the danger of accidental and deliberate fires being started by cultural site visitors, as well as natural fires from lightning strike or nearby forest fires. The system architecture, sensor and network design, and software design of the fire detection system are presented, based on experimental work at the Bulguksa temple in South Korea, a UNESCO World Heritage Site. Lessons learned from building and operating the Ubiquitous(U)-Bulguksa system are presented to provide guidelines for applying ubiquitous sensor networks to protect other cultural heritage tourism resources.

Utilizing Map Information for WLAN-Based Kalman Filter Indoor Tracking

Extended Kalman filter (EKF), which has long been used in the field of GPS positioning, has recently been used for indoor positioning, introduced extended Kalman filter (EKF) indoor positioning method based on Bluetooth signal strength. However, the error range of the EKF is so large when it was applied in WLAN tracking that we modified it to use K-NN method for the measurement of the EKF. Furthermore, this paper proposes an idea of improving accuracy of EKF tracking by adjusting parameter values referring to map information. Experimental results comparing our method with other previous methods are discussed.

Development of Kalman Filters for WLAN Based Tracking

Location based service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (wireless local area network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. The main purpose of this paper is to propose a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. Experimental results comparing our method with other previous methods are discussed.

Petri Net Representation of Ontologies for Indoor Location- Based Services

This paper presents a Petri net representation of an ontology for indoor location-based services. The ontology is described within the Resource Description Framework (RDF). Therefore, this paper proposes a method for transforming the RDF model into a CPN (colored Petri net). The proposed method represents the semantics of the RDF model in the CPN by mapping the classes and properties of the RDF model onto CPN places and representing the relationships between those classes and properties as token transitions in the CPN. To represent an RDF statement in the CPN, the method introduces a transition that produces a (complex) token composed of two ordered (simple) tokens: one corresponding to the subject and the other corresponding to the object of the statement. Applying the proposed method, we build a sample CPN for an RDF model and perform simulations using the model to answer RDF queries. This paper also introduces a simple ontology for an indoor location- based service. Using the proposed transformation method, we transform the ontology into a CPN. Finally, we introduce our inference algorithm for the CPN and a prototype database system for demonstrating the practicality of our method. The results indicate that the proposed database system can resolve the semantic ambiguities in the query by using the ontology.

Updating Policy of Indoor Moving Object Databases for Location-Based Services: The Kalman Filter Method

This paper proposes an updating policy of indoor moving object databases (IMODB) for location-based services. our method applies the Ka1man filter on the recently collected measured positions to estimate the moving object`s position and velocity at the moment of the most recent measurement, and extrapolate the current position with the estimated position and velocity. If the distance between the extrapolated current position and the measured current position is within the threshold, in other words if they are close then we skip updating the IMODB. When the IMODB needs to know the moving object`s position at a certain moment T, it applies the Kalman filter on the series of the measurements received before T and extrapolates the position at T with the estimations obtained by the Kalman filter. In order to verify the efficiency of our updating method, we performed the experiments of applying our method on the series of measured positions obtained by applying the fingerprinting indoor positioning method while we are actually walking through the test bed. In the analysis of the test results, we estimated the communication saving rate of our method and the error increment rate caused by the communication saving.

Colored Petri Net Representation of RDF Models

This paper proposes a method of transforming RDF (resource description framework) model into a CPN (colored Petri net) model. The method represents the semantics of the RDF model in the CPN model by mapping the classes and properties of RDF model to CPN places and representing the relationships between classes and properties as token transitions on the CPN. In order to represent a RDF statement on the CPN, the method introduces a transition which produces a token consisting of two ordered tokens, one is corresponding to the subject and the other is corresponding to the object of the statement. Applying the proposed method, we built an example CPN model for a RDF model. We also perform simulations on the model to answer to RDF queries.

Integrated Indoor Positioning Systems Reflecting Map Information for Location Based Services

So many location based service systems, including automobile navigation system logistic management, taxi fleet management, and so on, are being used everywhere. However, these are all outdoors. This paper provides a stepping stone for commercial indoor location based services by developing an integrated system of our indoor positioning and map viewer modules. For the indoor positioning, we propose WLAN (Wireless Local Area Network) based EKF (Extended Kalman Filter) which estimates user`s current location and tracts user`s trace in the sequence of time. Our map viewer renders a map recorded in an Autocad DXF file and provides functions of map manipulation such as zoom-in, zoom-out, and move. We integrate our indoor positioning and map viewer modules and discuss the experimental results of the integrated system.