Selectivity of multimple indoor positioning sensors

Recently, seamless positioning techniques in indoor and outdoor environment are required for the purpose of self-location acquisition for construction field and plant maintenance. Though we can acquire our own locations with outdoor positioning systems such as a Global Navigation Satellite Systems (GNSS) in outdoor environment, they're unavailable in indoor environment because the radio wave from GNSS satellites can't reach into indoor and underground. Moreover, each indoor positioning system such as wireless LAN, indoor messaging systems (IMES), radio frequency identification (RFID) tags, infrared tags and visible light communication system is severally developed and partially short of efficiency such as accuracy, stability and integrity in indoor environment. Therefore, sensor data selectivity and integration are required for crowded navigation systems consisted of existing and the latest, multiple sensor systems that have disparate kind of data format and property to achieve high efficiency positioning system. Thus, we firstly focus on selectivity of multiple indoor sensor data, and have proposed a reliable sensor data selection approach to achieve more accurate and stable positioning using the multiple indoor positioning sensor data. We then conducted indoor positioning experiments under a combination of multiple systems and sensors with 254 verification points. Two patterns were tested in this experiment. To produce the first pattern, the experimenter walked while holding the mobile PC to simulate navigation for pedestrians. Another pattern involved smooth movements by a truck to simulate navigation for autonomous robots. Moreover, we have set indices to each synchronized data received from IMES, RFID and Infrared Tags from the point of view of accuracy, integrity and continuity based on "Standards and Recommended Practices" (SARPs). Finally, we summarize this study with some parameters for indoor positioning data selections.