A Kalman filter updating method for the indoor moving object database

作者：Yim Jaegeol; Joo Jaehun^*; Park Chansik

来源：Expert Systems with Applications, 2011, 38(12): 15075-15083.

DOI：10.1016/j.eswa.2011.05.037

• Location-based service
• Moving object database
• Indoor positioning
• Kalman filter
• Updating strategy

A moving object database (MODB), a database representing information on moving objects, has many uses in a wide range of applications, such as the digital battlefield and transportation systems. In the transportation system, an MODS processes queries such as "How long should I wait until the next bus arrives here?" Therefore, location information on moving objects reflects the most important data the MODB has to manipulate. Most moving objects are equipped with a GPS (Global Positioning System) unit that sends location information to the MODB. However, GPS signals are usually very weak inside enclosed structures; thus, locating indoor moving objects requires more than the GPS. In this regard, indoor positioning for location-based services (LBSs) has been an important research topic for the last decade. There are many other differences between indoor and outdoor MODBs. For examples, the area where the indoor moving objects are moving around is much smaller than where the outdoor moving objects are moving around, and the speed of indoor moving objects is much slower than that of outdoor ones. Therefore, the indoor moving object database (IMODB) should be studied separately from the outdoor MODB or the MODB.
One of the most important problems that the MODB has to solve is the updating problem. In this regard, this paper proposes an updating method of IMODBs for location-based services. Our method applies the Kalman filter to the most recently collected series of measured positions to estimate the moving object's position and velocity at the last moment of the series of the measurements and extrapolates the current position with the estimated position and velocity. If the difference between the extrapolated current position and the measured current position is less than the threshold, that is, if the two positions are close, we skip updating the IMODB.
When the IMODB requires information on the moving object's position at a certain moment T, it applies the Kalman filter to the series of the recorded measurements at the moments before T and extrapolates the position at T with the Kalman filter in the same manner as the updating process described earlier. To verify the efficiency of our updating method, we applied our method to a series of measured positions obtained by employing the fingerprinting indoor positioning method while we walked through the test bed. We then analyzed the test results to calculate savings of communication cost and error.

• 出版日期2011-12