Motion Restricted Information Filter for Indoor Bluetooth Positioning

Motion Restricted Information Filter for Indoor Bluetooth Positioning

Liang Chen, Heidi Kuusniemi, Yuwei Chen, Ling Pei, Tuomo Kröger, Ruizhi Chen
DOI: 10.4018/jertcs.2012070104
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This paper studies wireless positioning using a network of Bluetooth signals. Fingerprints of received signal strength indicators (RSSI) are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, the authors propose a method of information filtering with speed detection, which combines the estimation information from the RSSI measurements with the prior information from the motion model. Speed detection is further assisted to correct the outliers of position estimation. The field tests show that the new algorithm proposed applying information filter with speed detection improves the horizontal positioning accuracy of indoor navigation with about 17% compared to the static fingerprinting positioning method, achieving a 4.2 m positioning accuracy on the average, and about 16% improvement compared to the point Kalman filter.
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Navigation and the related location based services are increasingly incorporated into mobile devices. The built-in GPS (Global Positioning System) on the handset is capable of providing location information in open signal environments. However, for indoor positioning, GPS is unable to provide the desired level of accuracy or even unavailable. One alternative to GPS is to utilize the signals of opportunity (SoOP), which are intended for purposes other than navigation.

Bluetooth is a technology for short-range wireless data and voice communication with low power consumption. It has been utilized in the communication and proximity market for a long time. As widely supported by mobile devices, Bluetooth is a potential technology to become an alternative for indoor positioning. However, indoor positioning using Bluetooth signals has not been widely studied so far. Bandara et al. (2004) developed a multi-antenna Bluetooth Access Point (AP) for location estimation based on received signal strength indicators (RSSI). The test obtained 2 meters of error in a 4.5 m×5.5 m area with four antennas. Sheng and Pollard (2006) modified the Bluetooth standard to estimate the distance between a reference transmitter and a mobile receiver, using RSSI measurements and a line-of-sight radio propagation model within a single cell. A high-density Bluetooth infrastructure is necessary to achieve an accurate position in the above two approaches. In order to minimize the Bluetooth infrastructure, Damian et al. (2008) used only one class 1 Bluetooth AP for a home localization system, which combined the measurements of the link quality, RSSI, and cellular signal quality to obtain room-level accuracy. Pei et al. (2010) present a Bluetooth locating solution in a reduced Bluetooth infrastructure area by using RSSI probability distributions. Other topics related to Bluetooth positioning can be found in Simon and Robert (2009), Anastasi et al. (2003), Bargh and Groote (2008), Jevring et al. (2008), and Naya et al. (2005).

New specifications and products have been developed for a relatively longer range of transmission. Compared with the class 2 device (e.g., the Bluetooth module in a smart phone), which has only the range of about 20-30 meters (Bluetooth, 2010), a class 1 Bluetooth device (e.g., the Bluegiga AP 3201) has an effective range up to 200 meters and the newly developed Bluegiga AP 3241 can even achieve an effective range of 800 m in an open area without obstructions (Bluegiga, 2010).

In this study, we investigate the indoor positioning in a Bluetooth network. 13 long range APs (Bluegiga 3201 and 3241) have been deployed in the area of interest. Utilizing the RSSI from Bluetooth APs, we consider a fingerprinting method for position estimation, which is based on database matching to particular fingerprints in the area at hand. We propose a method of information filtering with speed detection. The position is sequentially estimated by combining the information from the measurements and the prior motion model. Speed detection is further assisted to correct for the outliers of position estimation.

The paper is organized as follows: the system model and the problem of indoor positioning are formulated in the section of system description. Then, the next section considers the information filtering with speed detection as assistance. The experimental platform is described in detail and the next section shows the numerical results and a performance comparison is presented and discussed. Conclusions are drawn as well as future improvements discussed.

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