In wireless sensor networks, spatially distributed nodes provide location-dependent sensor information. Therefore, knowledge about the 3D position of all nodes is crucial for the numerous applications that require autonomous mobility. Furthermore, to acquire the nodes' poses and the complete 6D network constellation, the 3D orientation of each node is also required. While many theoretical localization concepts exist for wireless sensor networks, there is still a lack of reliable system and localization concepts which enable robust real-time tracking in real-world scenarios. Therefore, we present a system approach based on an advanced 24 GHz wireless local positioning system, providing distance and angle measurements between pairs of nodes. Furthermore, an extended Kalman filter based localization algorithm is proposed, which evaluates these measurements to track the time varying 6D poses of all nodes in the network. Because only relative measurements are available, one node is chosen to define a joint navigation system. Hence, the proposed system works without any previously installed infrastructure or prior information of the network. The system and localization algorithm are validated by measurements performed in a mobile wireless sensor network comprising six nodes in an indoor scenario with strong multipath propagation. However, despite the challenging environment, the system allows for a stable and accurate 6D pose estimation of all robots in the network with 3D positioning root mean square errors of 6 to 15 cm.
The localization of wireless devices in indoor scenarios presents a major challenge because of multipath propagation. Hence, the majority of the research community has focused on increasing the available bandwidth of localization systems, leading to the emergence of the ultra wide band (UWB) radar. However, the hardware implementation of UWB transceivers is challenging itself and, hence, their utilization in commercial low-cost wireless devices is not to be expected in the near future. Hence, instead of evaluating frequency dependent phases via UWB, the measurement of spatially distributed phases represents a valuable alternative. Therefore, this article presents a comparison of phase-difference-of-arrival (PDOA) and time-of-arrival (TOA) systems. For this purpose, we compare the measurement sensitivity, the effects of multipath propagation, and the hardware complexity. Based on the results, the applicability of typical position estimators is discussed. Thereby, we argue that PDOA-based localization with large receiver arrays appears to be the better choice to localize wireless devices, because it enables highly accurate positioning using narrow band signals without elaborated transmitter-receiver synchronization. To validate this, indoor localization measurements are presented and compared with UWB results in extant literature.
The direct measurement of distance-dependent information between wireless units represents a challenge for wireless locating systems, because it requires the exact time synchronization of separate wireless units. To avoid these synchronization efforts, many wireless locating systems only evaluate phase difference of arrival (PDOA) measurements. While simple PDOA localization techniques rely on multiangulation, advanced PDOA concepts like the holographic extended Kalman filter (HEKF) directly evaluate the measured phases without non-linear preprocessing. However, these differential phase measurement approaches are less sensitive than systems that can measure absolute phase variations, which allow the tracking of much smaller position changes than the signal's carrier wavelength. This paper proposes to extend the HEKF by the evaluation of absolute phases in an incoherent measurement setup, which consists of a continuous wave (CW) beacon and several receivers. The developed quasi-coherent holographic extended Kalman filter (QCHEKF) uses the overdetermined PDOA measurements to estimate the phase-frequency relation between each beacon-receiver pair. Then, the established phase-frequency relations allow the evaluation of absolute phase measurements and, thus, the accurate localization and tracking of a simple, unsynchronized, narrowband CW beacon, even under severe multipath conditions. This novel concept is experimentally validated via 3D localization results in a challenging indoor scenario using a 24 GHz CW measurement setup. Here, the QCHEKF improves the achieved localization accuracy in comparison to the HEKF by 35 % from 0.78 cm to 0.51 cm, while the maximum deviation from the trajectory reduces by 68 % from 5 cm to 1.6 cm. Furthermore, the QCHEKF enables the exact tracking of fast changes in direction, which is usually a significant challenge for standard wireless target tracking systems.INDEX TERMS Radar, Kalman filters, incoherent measurements, localization, array signal processing.
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