I/Q Density-based Angle of Arrival Estimation for Bluetooth Indoor Positioning Systems

In recent years, indoor positioning system employing Bluetooth has attracted tremendous attention. However, it is investigated that its received channel information is significantly affected by hardware configuration and wireless environments, especially the received dataset of angle of arrival (AOA), which leads to inaccurate channel estimation and positioning. Further-more, AOA estimation at larger angle direction is severely influenced by the variant wireless environment and signal distortion, which has not been resolve in existing literature. In this paper, we propose an advanced I/Q density-based AOA estimation (IQDAE) to deal with the above-mentioned problem, which consists of two sub-schemes. We firstly employ the designed phase difference (PD) filter to convert I/Q signals to phase information and then select the candidate sets by eliminating outliers. Afterward, we conceive a PD density-based classification algorithm to estimate AOA. The experimental results show that the mean absolute error of proposed IQDAE algorithm is comparably smaller than that from the other schemes, including commercial solutions, especially at larger angles. The results indicate that we can effectively increase the service range for the Bluetooth positioning system by adopting the proposed algorithm.

[1]  Kai-Ten Feng,et al.  Enhanced Distance and Location Estimation for Broadband Wireless Networks , 2015, IEEE Transactions on Mobile Computing.

[2]  T. Kaiser,et al.  Combined AOA/TOA UWB localization , 2007, 2007 International Symposium on Communications and Information Technologies.

[3]  Kai-Ten Feng,et al.  Refined Autoencoder-Based CSI Hidden Feature Extraction for Indoor Spot Localization , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[4]  Emanuel A. P. Habets,et al.  Blind System Identification Using Sparse Learning for TDOA Estimation of Room Reflections , 2013, IEEE Signal Processing Letters.

[5]  Hui Wang,et al.  A Novel Localization Method Based on RSS-AOA Combined Measurements by Using Polarized Identity , 2019, IEEE Sensors Journal.

[6]  Kutluyil Dogançay,et al.  Optimal sensor deployment for 3D AOA target localization , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Nicolas Malm,et al.  On the Performance of AoA–Based Localization in 5G Ultra–Dense Networks , 2019, IEEE Access.

[8]  François Horlin,et al.  Experimental Demonstration of BLE Transmitter Positioning Based on AOA Estimation , 2018, 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[9]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[10]  Joon-Ho Lee,et al.  Performance of ESPRIT and Root-MUSIC for Angle-of-Arrival(AOA) Estimation , 2018, 2018 IEEE World Symposium on Communication Engineering (WSCE).

[11]  Zhi Ding,et al.  Cooperative Self-Navigation in a Mixed LOS and NLOS Environment , 2014, IEEE Transactions on Mobile Computing.

[12]  Fuqiang Liu,et al.  Indoor Location Position Based on Bluetooth Signal Strength , 2015, 2015 2nd International Conference on Information Science and Control Engineering.

[13]  Thomas Kailath,et al.  ESPRIT-estimation of signal parameters via rotational invariance techniques , 1989, IEEE Trans. Acoust. Speech Signal Process..

[14]  Bhaskar D. Rao,et al.  Performance analysis of Root-Music , 1989, IEEE Trans. Acoust. Speech Signal Process..

[15]  Xinrong Li,et al.  Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks , 2007, IEEE Transactions on Vehicular Technology.

[16]  Kai-Ten Feng,et al.  Hybrid Unified Kalman Tracking Algorithms for Heterogeneous Wireless Location Systems , 2012, IEEE Transactions on Vehicular Technology.