Phase Difference Based Precise Indoor Tracking of Common Mobile Devices Using an Iterative Holographic Extended Kalman Filter

Abstract

The 3D indoor localization of low-cost standard mobile devices represents an important research topic. Since the implementation of ultra-wideband localization systems requires elaborated hardware, a localization concept based on phase-difference-of-arrival (PDOA) evaluation of narrow band communication signals at spatially distributed antennas is favorable in many applications. Typically, PDOA measurements are used to estimate the angle-of-arrival (AOA) at several receivers, which are then combined via multiangulation. However, AOA estimation requires far field conditions, thereby limiting measurement sensitivity, and distorts measurements in a non linear fashion. To overcome these limitations, this paper proposes the iterative holographic extended Kalman filter (IHEKF), which directly evaluates the phase differences between spatially distributed antenna pairs. The IHEKF requires neither a specific waveform nor emitter-receiver synchronization and, therefore, represents a good candidate for localization within communication systems such as 5G/6G. Since the evaluation of phase differences is affected by phase ambiguity, the IHEKF is designed so that closely spaced antenna pairs are evaluated first and then more distant antennas are included successively to improve accuracy. The IHEKF's capabilities are demonstrated via a 24 GHz narrow band measurement setup with strong multipath propagation, providing outstanding localization accuracy in the millimeter range without consuming any notable RF signal bandwidth.

A Wireless Joint Communication and Localization EMG-Sensing Concept for Movement Disorder Assessment

“…This error mainly results from the dilution of precision, in particular in the z-direction, due to the disadvantageous spatial setup, as the entire measurement area had to be covered within line of sight of the receivers and multiple cameras. In [26], a positioning with a root-meansquared error in the outstanding sub-cm range is shown using the same localization system, where a robotic arm is used as a reference instead of cameras, which reveals the potential of the utilized localization system. Although no medical analysis is conducted in this measurement, it proves the feasibility of the proposed concept and its potential for an all-in-one gait analysis measurement system.…”

“…to the spatially distributed antennas at the base stations. Utilizing a Holographic Extended Kalman Filter (HEKF), recently introduced by the authors in [25], [26], the beacons are localized by using the same RF signal as for data transmission. To this end, the HEKF recursively evaluats the relative phase of the RF signal by an Extended Kalman Filter (EKF).…”

“…Each receiver generates its own local clock, and thus, only a coarse synchronization between the receivers and no synchronization between the beacons and the receivers is necessary. The aperture consists of multiple antennas arranged to enable unambiguous position-dependent phase information of closely spaced antennas, as well as iteratively larger antenna distances to improve accuracy while retaining unambiguity, as explained in [26]. For each antenna, the receiving signal is mixed coherently at the base station to a complex valued IF signal, followed by a low-pass filter for mirror-frequency suppression and anti aliasing.…”

“…In the following, a brief description of the HEKF is presented, whereas in [26] the algorithm is derived in detail. First, an assumption on the movement model and the determination of the measurement model have to be made.…”

“…The beacon position is updated, to minimize the difference between the expected and measured phase differences. A full derivation is given in [26], where a suitable selection of the parameters i and j for choosing antenna combinations is also explained.…”