• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3534-3553
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• 2021

The low cost of Bluetooth technology has led to its wide usage in indoor positioning. However, some inherent shortcomings of Bluetooth technology have limited its further development in indoor positioning, such as the unstable positioning state caused by the fluctuation of Received Signal Strength Indicator (RSSI) and the low transmission frequency accompanied by a poor real-time performance in positioning and tracking moving targets. To address these problems, an indoor fusion positioning algorithm of Bluetooth technology and pedestrian dead reckoning (PDR) based on a particle filter with dynamic adjustment of weights calculation strategy (BPDW) will be proposed. First, an orderly statistical filter (OSF) sorts the RSSI values of a period and then eliminates outliers to obtain relatively stable RSSI values. Next, the Group-based Trilateration algorithm (GTP) enhances positioning accuracy. Finally, the particle filter algorithm with dynamic adjustment of weight calculation strategy fuses the results of Bluetooth positing and PDR to improve the performance of positioning moving targets. To evaluate the performance of BPDW, we compared BPDW with other representative indoor positioning algorithms, including fingerprint positioning, trilateral positioning (TP), multilateral positioning (MP), Kalman filter, and strong tracking filter. The results showed that BPDW has the best positioning performance on static and moving targets in simulation and actual scenes.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.207-214
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• 2021

A new positioning technique for positioning of LTE base stations is proposed. The positioning information of the base station is absolutely necessary for model-based wireless positioning, and is required in some of the various merhodologies for estimating signals in an uncorrected area when construnting a database for fingerprinting-based positioning. Using the acquired location-based Reference Signal Received Power (RSRP) information to estimate the location of the base station, it is impossible with the existing trilateration methods. Therefore, in this paper, a method using reference particles is proposed. Particles are randomly generated in the application area, and signal propagation modeling is performed assuming that a base station is located in each particle. Based on this, the errors of measurements are calculated. The particle group with the minimum measurement errors is selected, the position of the base station is estimated through weighted summation, and the signal propagation model of the corresponding base station is built at the same time. The performance of the proposed technology is verified using data acquired in Seocho-dong, Seoul.