This paper describes a method for localizing wireless mobile clients in a multistory building using a public wireless Local Area Network (LAN) system. Physical location data on personal devices and mobile robots is important to information services and robot applications. Wireless mobile clients are localized in a multistory building using public wireless LAN access points placed, three-dimensionally in the building. Information on the floor number and client location is acquired probabilistically, with estimation providing a probabilistic model for localization based on sparse Bayesian learning. Results of experiments confirm the feasibility of our proposal.
This paper describes a method for location estimation of mobile wireless local area network (LAN) clients in multistory buildings using the strength of the received signals in a state space framework. Data pertaining to the physical positions of personal electronic devices or mobile robots are important for information services and robotic applications. We focus on integrating the estimation results with other sensor data based on a state space framework. The estimation model for location provides a variance of a mobile client's location. We integrate the estimation results and the motion results of the mobile client using a Kalman filter. The estimation model is re-initialized when the mobile client moves to another floor in the building by detecting the change in the floor number where the mobile client has moved. This is done by using the Bayesian inference. Experimental results show the feasibility of this method.
This paper describes a method for the localization of wireless mobile clients in multistory buildings using a public wireless LAN system. The global positioning system (GPS) is used for the outdoor localization of a mobile client carried by humans or mobile robots; however, it is difficult to estimate the global position of the mobile client in multistory buildings since the GPS is not suitable for indoor localization. The proposed method uses public wireless LAN access points which are settled three-dimensionally in a building. The application of the method involves the assumption that the humans or robots carrying the mobile client move horizontally on each floor in the building. The method simultaneously estimates the position of the mobile client and its floor number. Experimental results indicate that the proposed method is feasible.
This paper describes a method for localization of a mobile wireless LAN client in multistory buildings using the strength of received signals in a state space framework. Data pertaining to the physical positions of personal electronic devices or mobile robots are important for information services and robotic applications. We focus on integration of the estimation results with other sensor data based on a state space framework. The estimation model for location provides the variance of the position of the mobile client. The estimation model is re-initialized when the mobile client moves to the other floor in the building based on the detection of the change of number of the floor where the mobile client moves by the Bayesian inference. Experimental results show the feasibility of the method.
This paper describes a method for the localization of wireless mobile clients in multistory buildings using a wireless LAN system. The method uses public wireless LAN access points for users and robots that are settled at many places in a multistory building. Data on the physical position of personal electronic devices or mobile robots are important in information services and robot applications for construction automation such as the maintenance task in the large scale building. By integrating the data and stored information on objects and places, people and robots can be provided with information on the location of the devices for achieving the tasks. The localization method using few devices is expected for real application such as a navigation of the mobile robots, information services for users who have mobile clients. The paper focuses on the data acquisition for stochastic localization of the wireless LAN client in multistory building. The data acquisition is a crucial issue for making and verification of the localization system. Experimental results show feasibility of the proposed method.
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