Felix Antreich

Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals

Abstract Important signal processing techniques need that the response of the different elements of a sensor array have specific characteristics. For physical systems this often is not achievable as the array elements’ responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches become necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays.

Multicorrelator signal tracking and signal quality monitoring for GNSS with extended Kalman filter

GNSS signals may present anomalies that degrade the positioning performance of GNSS receivers. Signal Quality Monitoring (SQM) is normally used to detect and to characterize these anomalies. This is required for the GNSS operators and integrity services to determine when a satellite should be considered as faulty and draw conclusions about the type of the fault. In this paper, we present a new SQM algorithm that tracks the GNSS signal and possible channel deformations by using a novel methodology based on the Extended Kalman Filter (EKF). The EKF is designed such that the measurement update is performed in post-correlation and using multiple correlators. After the estimation of the channel response, we add a detection step to determine if the channel deviates from the nominal signal transmission scenario (i.e., the single path propagation). Results suggests that the performance of the delay estimation with the proposed EKF structure outperforms the classical Delay-Locked-Loop (DLL) estimation, especially in the presence of distortions. Furthermore, it can reliably detect anomalous signal deformations as specified by ICAO threat model.

Dual polarization beamforming algorithm for multipath mitigation in GNSS

A multipath mitigation technique for GNSS is proposed.The technique exploites the change of polarization when a signal is reflected.A novel statistic based on the outputs of a correlator bank is introduced to estimate the multipath DoA and polarization.Applying the technique increases the estimation performance in the case of highly temporally or spatially correlated multipath and line-of-sight signal. This paper treats the problem of line-of-sight (LOS) parameter estimation in strong multipath environments. In the case of highly temporally and spatially correlated LOS and multipath signals, such as urban canyons, common multipath mitigation methods are highly degraded, as signal separation cannot be performed in the spatio temporal domain. In this case, we exploit the LOS and multipath polarization diversity to decouple the signals using an antenna array with right-hand-circular polarization (RHCP) and left-hand-circular polarization (LHCP) feeds. The multipath direction- of-arrival (DOA) and polarization coefficients can effectively be estimated from the LHCP spatial covariance matrix. The LOS DOA can be estimated from the RHCP spatial covariance matrix. The spatial covariance matrices are calculated from the outputs of a matched correlator bank. The DOA and polarization estimates are used to implement a dual polarization beamformer, which maximizes the LOS energy and suppresses multipath energy over both polarizations. The LOS time-delay is estimated from the beamformer output with a maximum-likelihood estimator with a significantly reduced number of parameters and computational complexity in comparison to a full model estimator. Simulation results for GPS show that the proposed dual polarization beamforming algorithm performs better than an equivalent single-polarization beamformer in a dense multipath environment.

Signals and Modulation

Satellite navigation relies on signals radiated by orbiting satellites and received by mobile satellite navigation receivers. This chapter addresses the fundamentals of such navigation signals and introduces the most important underlying concepts. It provides an introduction to radio frequency signals including the basics of electromagnetic waves, their carrier frequency, polarization, as well as group and phase velocity. The application of waves for carrying signals, their power and spectrum are addressed. It is shown how information-carrying signals can be modulated onto the wave using various modulation schemes such as binary phase shift keying, binary offset carrier, and alternating binary offset carrier. Setting out from international agreed allocations, the frequency bands used in GNSS are described. The concept of pseudo-random codes which is typically used for GNSS signals is introduced as well as their receiver side processing following a correlation principle.