Multistate Constraint Multipath-Assisted Positioning and Mismatch Alleviation

Multipath propagation greatly affects the accuracy of Time-of-Arrival (ToA)-based indoor positioning when line-of-sight (LOS) signals are only used. In this article, we present a novel real-time and low computation complexity multipath-assisted ToA positioning method, namely, multistate constraint (MSC)-MAP. The delays of reflected signals are taken as additional spatial observations to compensate for an insufficient number of physical transmitters to locate a moving user equipment (UE). Virtual anchors are used to model the propagation path of reflected signals, whose locations are obtained via an MSC estimator, along with the trajectory of UE. In addition, we demonstrate the mismatch problem in data association and its impact on positioning performance. To achieve real-time processing, we propose two robust multipath-assisted positioning methods with mismatch alleviation by randomly selecting subset and constraint relaxation, respectively, to meet various computational complexity requirements. Simulation results show that, for the MSC-MAP method, the mean-square error of the position is generally less than 0.2 m in challenging indoor environments. Among mismatch alleviation algorithms, positioning error is reduced by 69% even when the percentage of mismatched measurement data is as high as 42%. The proposed algorithms can also efficiently handle signals with non-Gaussian impairments, a common characteristic in real-world data. Moreover, these algorithms can substantially improve positioning performance while adding minimal computation time in the presence of measurement mismatches, outperforming state-of-the-art methods utilizing different data association techniques.