Robust Phase-Based Positioning Using Massive MIMO with Limited Bandwidth

This paper presents a robust phase-based positioning
framework using a massive multiple-input multiple-output
(MIMO) system. The phase-based distance estimates of MPCs
together with other parameters are tracked with an Extended
Kalman Filter (EKF), the state dimension of which varies with
the birth-death processes of paths. The iterative maximumlikelihood
estimation algorithm (RIMAX) and the modeling of
dense multipath component (DMC) in the framework further
enhance the quality of parameter tracking by providing an
accurate initial state and the underlying noise covariance.
The tracked MPCs are fed into a time-of-arrival (TOA) selfcalibration
positioning algorithm for simultaneous trajectory
and environment estimation. Throughout the positioning process,
no prior knowledge of the surrounding environment and
base station position is needed. The performance is evaluated
with the measurement of a 2D complex movement, which was
performed in a sports hall with an antenna array with 128 ports
as base station using a standard cellular bandwidth of 40 MHz.
The positioning result shows that the mean deviation of the
estimated user equipment trajectory from the ground truth is
13 cm. In summary, the proposed framework is a promising
high-resolution radio-based positioning solution for current and
next generation cellular systems.