Self-contained Antenna Crosstalk and Phase Offset Calibration by Jointly Solving the Attitude Estimation and Calibration Problem

Abstract

Soeren Zorn received his M.Sc. in electrical engineering from RWTH Aachen University in 2015. His bachelor and master thesis covered spoofing detection and mitigation. He is currently pursuing his Ph.D. at RWTH as a member of the Chair of Navigation. His research interests include multi antenna GNSS receivers, attitude and calibration estimation, robust interference and spoofing detection and mitigation. Michael Niestroj received his M.Sc. in electrical engineering from RWTH Aachen Universtiy in 2016. His master thesis covered attitude and calibration estimation for multi antenna GNSS receivers. He is currently prusuing his Ph.D. at RWTH Aachen University as a member of the chair of Navigation. His research is focused on calibration methods for GNSS multi antenna systems. Marius Brachvogel received his M.Sc. degree in electrical engineering from the RWTH Aachen University in 2016. Since then, he works as a research assistant for the Chair of Navigation. His current research includes GNSS interference and spoofing mitigation on distributed antenna systems. Stefano Caizzone rceived the M.Sc. in Telecommunications Engineering and the Ph.D. degree in Geoinformation from the University of Rome Toer Vergata, Italy, in 2009 and 2015, respectively. Since 2010, he is with the Antenna group of the Institute of Communications and Navigation of the German Aerospace Center (DLR), Wessling, Germany, where he is responsible for the development of innovative miniaturized antennas. His main research interests concern small antennas for RFIDs and satellite navigation, antenna arrays and grids with enhanced sensing capabilities, controlled radiation pattern antennas for robust satellite navigation and high-performance antenna design for precise satellite navigation. He is a participant of RTCA SC-159, active in the definition of minimum operational performance specifications for avionic GNSS equipment. Michael Meurer received the diploma in electrical engineering and the Ph.D. degree from the University of Kaiserslautern, Germany. After graduation, he joined the Research Group for Radio Communications at the Technical University of Kaiserslautern, Germany, as a senior key researcher, where he was involved in various international and national projects in the field of communications and navigation both as project coordinator and as technical contributor. From 2003 till 2013, Dr. Meurer was active as a senior lecturer and Associate Professor (PD) at the same university. Since 2006 Dr. Meurer is with the German Aerospace Centre (DLR), Institute of Communications and Navigation, where he is the director of the Department of Navigation and of the center of excellence for satellite navigation. In addition, since 2013 he is a professor of electrical engineering and director of the Chair of Navigation at the RWTH Aachen University. His current research interests include GNSS signals, GNSS receivers, interference and spoofing mitigation and navigation for safety-critical applications.

Software Defined Radio for GNSS Radio Frequency Interference Localization

“…The largest difficulty with this is that calibrating antenna arrays has historically been difficult to do in situ because it requires signals with known arrival directions and various signals from different arrival directions to compute comprehensive calibration coefficients [ 27 ]. Recent research has focused around using GNSS signals, whose AoA measurements can be estimated in various ways, in order to compute the calibration coefficients of an antenna array [ 27 , 28 , 29 ]. These techniques generally update the antenna array model given in Equation (3) where y ( t ) = [y 1 (t),…, y M (t)] T is the observed signal at an M element array, s ( t ) = [s 1 (t),…, s L (t)] T contains the L present signals, A ( φ , θ ) is the M × L steering matrix built from the steering vectors related to each signal, and n ( t ) is the M × 1 noise [ 27 ].…”

“… y ( t ) = MDA ( φ , θ ) s ( t ) + n ( t ) From this updated form of the array, it is then possible to iteratively solve for the calibration coefficients in M and D provided a known signal azimuth and elevation of arrival by adjusting the cost function leveraged by the MUSIC algorithm [ 28 ]. A more complex model is presented by Zorn et al This model additionally accounts for crosstalk uncertainties and explicitly divides the phase and gain mismatch parameters into stable and time-varying, before iteratively solving for the parameters using the Levenberg–Marquardt method [ 29 ]. Both of these processes exploit other methods to obtain GNSS satellite azimuth and elevation angles to deal with the problem of known signal direction of arrival.…”

Enabling RTK Positioning Under Jamming: Mitigation of Carrier-Phase Distortions Induced by Blind Spatial Filtering

“…In practice, this approach is quite challenging due to the temporal change of the needed information. One promising attempt to estimate the time-changing components jointly is described by Zorn et al (2017Zorn et al ( , 2018.…”

A new array concept using spatially distributed subarrays for unambiguous GNSS interference mitigation in automotive applications

“…Furthermore it is assumed that in case of strong crosstalk in the front-end, such that the assumption of spatial white noise in the digitized signals is no longer justified, a suitable calibration method is incorporated. 8 A common approach to mitigate interference signals is to use a scaled inverse of the interference covariance matrix to derive the spatial precorrelation filter P, 3 eg,…”

“…Thereby it is assumed that the block length is chosen large enough such that can be modeled as wide‐sense stationary over the interval defined in Equation . Furthermore it is assumed that in case of strong crosstalk in the front‐end, such that the assumption of spatial white noise in the digitized signals is no longer justified, a suitable calibration method is incorporated . A common approach to mitigate interference signals is to use a scaled inverse of the interference covariance matrix to derive the spatial precorrelation filter , eg, where denotes the Frobenius norm and in order to attenuate the spatially correlated interference.…”