GNSS Multipath Mitigation Using Channel Parameter Estimation Techniques

Multipath propagation can pose significant challenges to satellite based navigation systems. It remains a dominant source of accuracy degradation and is a major issue for high precision GNSS applications. Multipath can result in biased GNSS measurements, which can lead to inaccurate position estimates or, through fading and self-interference, can cause loss of lock of the signals. Without accurate LOS delay estimation in multipath environments GNSS receivers cannot provide reliable positions, velocity and time (PVT) estimates. Although there are many algorithms proposed in the literature which endeavor to mitigate the effects of multipath, this research topic is still active as no final solution has yet been found. Given the above, the problem of GNSS multipath mitigation is pursued in this work through the estimation of the parameters of multipath components. For this purpose, three different approaches are proposed and tested. First, a sequential ML-based approach is proposed that sequentially estimates the channel parameters with a smaller computational load compared to the conventional ML-based approaches. This approach uses a detection procedure to avoid over-estimating or underestimating the number of multipath components. For this reason, the proposed approach is more robust in dealing with severe multipath situations such as urban areas. Afterwards, this ML-Based approach is combined with a low-complexity Bayesian tracking algorithm to further decrease the computational load. In this way, the receiver switches between two modes of operation depending on the severity of the variations of the multipath channel. A set of simulation and data processing results is then used to assess the performance of this technique. The results show that the proposed system outperforms both the classical DLLs and the conventional ML-based algorithms. This algorithm is also used to characterize the distribution of the number of multipath components for some of the visible satellites in the collected data set. Second, some of the most well-known adaptive filters (LMS, NLMS, RLS and APA) are modified and developed to be used for the purpose of equalization of the multipath channel. The very low computational load associated with these techniques make them more suitable for implementation in hand-held receivers. The innovative hard decision block used in the structure of their feedback procedure increases their efficiency. The presented simulation and data processing results show that the estimation performances of some of these techniques (RLS and APA) are comparable to near-optimal ML-based techniques at higher SNR values. Third, the possibility of employing the Doppler shifted copies of the received signal in a fast fading channel for the purpose of improving the estimation performance of subspace-based methods is analyzed and tested through simulation and experimental results. The results demonstrate a considerable improvement in the estimation accuracy of the proposed system compared to the cases where diversity approaches are used

Precise Calibration of a GNSS Antenna Array for Adaptive Beamforming Applications

The use of global navigation satellite system (GNSS) antenna arrays for applications such as interference counter-measure, attitude determination and signal-to-noise ratio (SNR) enhancement is attracting significant attention. However, precise antenna array calibration remains a major challenge. This paper proposes a new method for calibrating a GNSS antenna array using live signals and an inertial measurement unit (IMU). Moreover, a second method that employs the calibration results for the estimation of steering vectors is also proposed. These two methods are applied to the receiver in two modes, namely calibration and operation. In the calibration mode, a two-stage optimization for precise calibration is used; in the first stage, constant uncertainties are estimated while in the second stage, the dependency of each antenna element gain and phase patterns to the received signal direction of arrival (DOA) is considered for refined calibration. In the operation mode, a low-complexity iterative and fast-converging method is applied to estimate the satellite signal steering vectors using the calibration results. This makes the technique suitable for real-time applications employing a precisely calibrated antenna array. The proposed calibration method is applied to GPS signals to verify its applicability and assess its performance. Furthermore, the data set is used to evaluate the proposed iterative method in the receiver operation mode for two different applications, namely attitude determination and SNR enhancement

DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts

Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc. While digitization of documents such as scientific articles and magazines is prevalent in literature, one of the main challenges for digitization of newspaper lies in its complex layout (e.g. articles spanning multiple columns, text interrupted by images) analysis, which is necessary to preserve human read-order. This work provides a major breakthrough in the digitization of newspapers on three fronts: first, releasing a dataset of 3000 fully-annotated, real-world newspaper images from 21 different U.S. states representing an extensive variety of complex layouts for document layout analysis; second, proposing layout segmentation as a precursor to existing optical character recognition (OCR) engines, where multiple state-of-the-art image segmentation models and several post-processing methods are explored for document layout segmentation; third, providing a thorough and structured evaluation protocol for isolated layout segmentation and end-to-end OCR