Multi-GNSS positioning for landslide monitoring: A case study at the Recica landslide

Global Navigation Satellite System (GNSS) positioning has characteristics of simple operation, high efficiency and high precision technique for landslide surface monitoring. In recent years, finalization of modern GNSS systems Galileo and BeiDou has brought a possibility of multi-GNSS positioning. The paper focuses on evaluation of possible benefits of multi-GNSS constellations in landslide monitoring. While simulating observational conditions of selected Recica landslide in the Czech Republic, one-month data from well-established permanent GNSS reference stations were processed. Besides various constellation combinations, differential and Precise Point Positioning techniques, observation data lengths and observation sampling intervals were evaluated. Based on the results, using a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems can be recommended, together with a static differential technique and observation periods for data collection exceeding eight hours. In the last step, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements.Web of Science19327025

Application of Multi-GNSS Positioning in Landslide Surface Deformation Monitoring

With a modernization of legacy GPS and GLONASS systems, as well as with a finalization of the new European Galileo and Chinese BeiDou systems, about 120 navigation satellites for Global Navigation Satellite System (GNSS) users around the world are available presently. Usage of multi-GNSS constellations has therefore become an important research topic in recent years, including the area of landslide monitoring. The main goal of this dissertation thesis was to analyze and study positioning accuracy and performance of different satellite systems combinations with focus on finding the optimal strategy for multi-GNSS data collection and processing in landslide monitoring applications. Five stabilized monitoring points allowing repetitive GNSS observation campaigns were established at the selected Recica landslide in the Czech Republic. Quality of current multi-GNSS precise products provided by different analysis centers (ACs) was evaluated to allow a selection of the optimal one. Although no substantial differences were found, products provided by GeoForschungsZentrum (GFZ) and Center for Orbit Determination in Europe (CODE) can be recommended in overall. Consequently, positioning accuracy provided by various constellation combinations was analyzed by using data from well-established GNSS reference stations while simulating observation conditions of the Recica landslide. The best results were obtained when processing signals from a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems, with a static relative differential technique and observation periods for data collection exceeding eight hours. Finally, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements. A horizontal displacement with an annual velocity of about 3 cm in the horizontal direction was found for three monitoring points while the other two points were more stable.With a modernization of legacy GPS and GLONASS systems, as well as with a finalization of the new European Galileo and Chinese BeiDou systems, about 120 navigation satellites for Global Navigation Satellite System (GNSS) users around the world are available presently. Usage of multi-GNSS constellations has therefore become an important research topic in recent years, including the area of landslide monitoring. The main goal of this dissertation thesis was to analyze and study positioning accuracy and performance of different satellite systems combinations with focus on finding the optimal strategy for multi-GNSS data collection and processing in landslide monitoring applications. Five stabilized monitoring points allowing repetitive GNSS observation campaigns were established at the selected Recica landslide in the Czech Republic. Quality of current multi-GNSS precise products provided by different analysis centers (ACs) was evaluated to allow a selection of the optimal one. Although no substantial differences were found, products provided by GeoForschungsZentrum (GFZ) and Center for Orbit Determination in Europe (CODE) can be recommended in overall. Consequently, positioning accuracy provided by various constellation combinations was analyzed by using data from well-established GNSS reference stations while simulating observation conditions of the Recica landslide. The best results were obtained when processing signals from a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems, with a static relative differential technique and observation periods for data collection exceeding eight hours. Finally, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements. A horizontal displacement with an annual velocity of about 3 cm in the horizontal direction was found for three monitoring points while the other two points were more stable.548 - Katedra geoinformatikyvyhově

Estimation parcimonieuse de biais multitrajets pour les systèmes GNSS

L’évolution des technologies électroniques (miniaturisation, diminution des coûts) a permis aux GNSS (systèmes de navigation par satellites) d’être de plus en plus accessibles et doncutilisés au quotidien, par exemple par le biais d’un smartphone, ou de récepteurs disponibles dans le commerce à des prix raisonnables (récepteurs bas-coûts). Ces récepteurs fournissent à l’utilisateur plusieurs informations, comme par exemple sa position et sa vitesse, ainsi que des mesures des temps de propagation entre le récepteur et les satellites visibles entre autres. Ces récepteurs sont donc devenus très répandus pour les utilisateurs souhaitant évaluer des techniques de positionnement sans développer tout le hardware nécessaire. Les signaux issus des satellites GNSS sont perturbés par de nombreuses sources d’erreurs entre le moment où ils sont traités par le récepteurs pour estimer la mesure correspondante. Il est donc nécessaire decompenser chacune des ces erreurs afin de fournir à l’utilisateur la meilleure position possible. Une des sources d’erreurs recevant beaucoup d’intérêt, est le phénomène de réflexion des différents signaux sur les éventuels obstacles de la scène dans laquelle se trouve l’utilisateur, appelé multitrajets. L’objectif de cette thèse est de proposer des algorithmes permettant de limiter l’effet des multitrajets sur les mesures GNSS. La première idée développée dans cette thèse est de supposer que ces signaux multitrajets donnent naissance à des biais additifs parcimonieux. Cette hypothèse de parcimonie permet d’estimer ces biais à l’aide de méthodes efficaces comme le problème LASSO. Plusieurs variantes ont été développés autour de cette hypothèse visant à contraindre le nombre de satellites ne souffrant pas de multitrajet comme non nul. La deuxième idée explorée dans cette thèse est une technique d’estimation des erreurs de mesure GNSS à partir d’une solution de référence, qui suppose que les erreurs dues aux multitrajets peuvent se modéliser à l’aide de mélanges de Gaussiennes ou de modèles de Markov cachés. Deux méthodes de positionnement adaptées à ces modèles sont étudiées pour la navigation GNSS

Structured Sub-Nyquist Sampling with Applications in Compressive Toeplitz Covariance Estimation, Super-Resolution and Phase Retrieval

Sub-Nyquist sampling has received a huge amount of interest in the past decade. In classical compressed sensing theory, if the measurement procedure satisfies a particular condition known as Restricted Isometry Property (RIP), we can achieve stable recovery of signals of low-dimensional intrinsic structures with an order-wise optimal sample size. Such low-dimensional structures include sparse and low rank for both vector and matrix cases. The main drawback of conventional compressed sensing theory is that random measurements are required to ensure the RIP property. However, in many applications such as imaging and array signal processing, applying independent random measurements may not be practical as the systems are deterministic. Moreover, random measurements based compressed sensing always exploits convex programs for signal recovery even in the noiseless case, and solving those programs is computationally intensive if the ambient dimension is large, especially in the matrix case. The main contribution of this dissertation is that we propose a deterministic sub-Nyquist sampling framework for compressing the structured signal and come up with computationally efficient algorithms. Besides widely studied sparse and low-rank structures, we particularly focus on the cases that the signals of interest are stationary or the measurements are of Fourier type. The key difference between our work from classical compressed sensing theory is that we explicitly exploit the second-order statistics of the signals, and study the equivalent quadratic measurement model in the correlation domain. The essential observation made in this dissertation is that a difference/sum coarray structure will arise from the quadratic model if the measurements are of Fourier type. With these observations, we are able to achieve a better compression rate for covariance estimation, identify more sources in array signal processing or recover the signals of larger sparsity. In this dissertation, we will first study the problem of Toeplitz covariance estimation. In particular, we will show how to achieve an order-wise optimal compression rate using the idea of sparse arrays in both general and low-rank cases. Then, an analysis framework of super-resolution with positivity constraint is established. We will present fundamental robustness guarantees, efficient algorithms and applications in practices. Next, we will study the problem of phase-retrieval for which we successfully apply the sparse array ideas by fully exploiting the quadratic measurement model. We achieve near-optimal sample complexity for both sparse and general cases with practical Fourier measurements and provide efficient and deterministic recovery algorithms. In the end, we will further elaborate on the essential role of non-negative constraint in underdetermined inverse problems. In particular, we will analyze the nonlinear co-array interpolation problem and develop a universal upper bound of the interpolation error. Bilinear problem with non-negative constraint will be considered next and the exact characterization of the ambiguous solutions will be established for the first time in literature. At last, we will show how to apply the nested array idea to solve real problems such as Kriging. Using spatial correlation information, we are able to have a stable estimate of the field of interest with fewer sensors than classic methodologies. Extensive numerical experiments are implemented to demonstrate our theoretical claims

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium