A Phase-Based Indoor Localization Algorithm Using EKF for Passive RFID Tags

This paper proposes a phase-based indoor localization algorithm using Extended Kalman Filter (EKF) for Passive RFID tags. Firstly, with multi frequency phase information obtained by frequency hopping technique, coarse estimation distance can be obtained by performing Inverse Fast Fourier Transform (IFFT), which is utilized to achieve the multi-path suppression (MPS) algorithm. Secondly, an MPS-assisted filtering method is proposed to select two frequencies with less phase errors. Finally, the phase ambiguity is solved by EKF with the multi-epoch phase information of the two selected frequencies, which realizes high accuracy localization. Experimental results show that the proposed algorithm achieves a median localization error of 935 cm, and the execution time is improved by nearly 10 times compared with Chinese Remainder Theorem (CRT).