Institute of Microwaves and Photonics (LHFT)
Institution: Friedrich-Alexander-University of Erlangen-Nürnberg
Featured research (4)
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 IHEKFs 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.
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.
With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.
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.
- Institute of Microwaves and Photonics
About Martin Vossiek
- Martin Vossiek currently works at the Institute of Microwaves and Photonics (LHFT), Friedrich-Alexander-University of Erlangen-Nürnberg (FAU). Martin does in radar, transponder, RF identification, communication, and locating systems.