Bluetooth positioning Timo Kälkäinen
Background Bluetooth chips are cheap and widely available in various electronic devices GPS positioning is not working indoors Also indoor positioning is needed in large buildings
Current status There are theoretical and actual positioning methods available Bluetooth positioning is in use at some big buildings around the worlds Bluetooth th SIG positioning i Working Group is currently developing a future profile for the positioning i
5 Theoretical methods Angle of Arrival (AoA) Cell identity (CI) Time of Arrival (ToA) Time Difference of Arrival (TDoA) Rx Power level
Angle of Arrival ( AoA) Stationary device measures from mobile device incoming signal angle Requires at least two stationary devices with special antennas -> because of special antennas this method is not so easy to implement in actual application
Cell Identity y( (CI) Network is divided between stationary Bluetooth device cells Mobile device position is determined according to stationary device points Accuracy is typically y about 10 meters Accuracy can be improved by using triangle calculation with in multiplexed base station cells This method works very well with Bluetooth devices when accuracy can be ~10 meter
Time of Arrival ( ToA) Time of arrival can be calculated when mobile device send signal to stationary device. Stationary device send signal back to mobile device Range can be calculated from signal loop time Calculating position requires connection at least 3 stationary device This method requires very exact synchronized clock ( radio signal moves 300 meters in micro second) Current Bluetooth specification allows micro second delay in packet sending so this method is too inaccurate for exact Bluetooth positioning
Time Difference of Arrival (TDoA) Based on signal receiving differences method Is developed for replacing exact synchronization clock There are still no enough exact clocks available in Bluetooth devices in order to use this method for efficient positioning
Rx Power level Bases on received signal power level calculations Works as ToA method, but calculation is done via signal strength instead of time Position can be calculated from signal propagation model HCI command includes RSSI-parameter for reading signal strength With this method accuracy is about 3.7 meters
Valid methods for applications Rx power level and Cell identity methods are currently used with Bluetooth applications since those doesn t require necessary special antennas Both method location accuracy can be improved with Triangulation method
Triangulation method Triangulation method is used for improving the Triangulation method is used for improving the accuracy in valid methods
Determine position via Cell identity There are several stationary Bluetooth devices which are looking for mobile Bluetooth devices Device position can be determined via closest Bluetooth cell
Determine position via Rx There are several stationary Bluetooth devices which are measuring mobile Bluetooth devices RSSI RSSI values are 0-255
Calculating gposition with RSSI needed formulas in calculating Assume N 2 is the number of base stations in a mobile communication network and the position of a base station k is defined by It follows, that the distances between the base stations i, j and a point in the x-y-plain, given by If the distances between a number of N>2 base stations and a mobile terminal are already known the position estimation can be efficiently calculated by using the LSE method. In the numerical operations this method computes that point in the x-y plain which position provides the least square sum of the distance to all possible section boundaries given by equation (2). The section boundaries are derived from (3) We assume that the position estimation is defined by: Hence, the location estimation of the terminal is determined by: Assume S assign the received signal strength, the value of S is determined by: For converting the signal strength measurement to distances between a senderand receiver in freefields,the equation
Commercial possibilities Large shops can send advertisements according to user position For example museums could use this position method for quidance Big shopping malls can observe shopper walking routes and make advertisements according to shopper actions. Indoor navigation applications in large buildings