Time-delay estimation under non-clustered and clustered scenarios for GNSS signals

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2021.Aplicações que empregam sistemas globais de navegação por satélite, do inglês Global Navigation Satellite Systems (GNSS) para prover posicionamento acurado estão sujeitos a degradação drástica não só por intereferências eletromagnéticas, como também componentes de multipercurso causados por reflexões e refrações no ambiente. Aplicações de segurança crítica como veículos autonômos e aviação civil, e aplicações de risco crítico como gestão de pesca, pedágio automático, e agricultura de precisão dependem de posicionamento acurado sob cenários complicados. Tipicamente quanto mais agrupamento ocorre entre o componente de linha de visada, do inglês line-of-sight (LOS) e componentes de multipercurso ou não-linha de visada, do inglês non-line-of-sight (NLOS), menos acurada é a estimação da posição. Abordagens tensorials estado da arte para receptores GNSS baseado em arranjos de antenas utilizam processamento tensorial de sinais para separar o componente LOS dos componentes NLOS, assim mitigando os efeitos destes, utilizando decomposição em valores singulares multilinear, do inglês multilinear singular value decomposition (MLSVD) para gerar um autofiltro de order superior, do inglês higher-order eigenfilter (HOE) com pré-processamento por média frente-costas, do inglês forward-backward averaging (FBA), e suavização espacial expandida, do inglês expanded spatial smoothing (ESPS), estimação de direção de chegada, do inglês direction of arrival (DoA) e fatorização Khatri-Rao, do inglês Khatri-Rao factorization (KRF), estimação de Procrustes e fatorização Khatri-Rao (ProKRaft), e o sistema semi-algébrico de decomposição poliádica canônica por diagonalização matricial simultânea, do inglês semi-algebraic framework for approximate canonical polyadic decomposition via simultaneous matrix diagonalization (SECSI), respectivamente. Propomos duas abordagens de processamento para estimação de atraso, do inglês time-delay estimation (TDE). A primeira é a abordagem em lotes utilizando dados de vários períodos do sinal. Usando estimação em lotes propomos duas abordagens algébricas para TDE, em que diagonalizaçao é efetivada por decomposição generalizada em autovalores, do inglês generalized eigenvalue decomposition (GEVD), das primeiras duas fatias frontais do tensor núcleo do tensor de dados, estimado por MLSVD. Esta primeira abordagem, como os métodos citados, na quais simulações foram feitas com 1 componente LOS e 1 componente NLOS, assim os dados observados tem posto cheio em todos seus modos, não faz suposições sobre o posto do tensor de dados. A segunda abordagem supõe cenários nos quais mais de 1 componente NLOS está presente e são agregados (clustered em inglês), assim vários vetores de uma das matrizes-fator que formam o tensor de dados são altamente correlacionaiii dos, resultando num tensor de dados que é de posto deficiente em pelo menos um modo. Os esquemas algébricos baseados em tensores propostos utilizam a decomposição poliádica canônica por decomposição generalizada em autovalores, do inglês canonical polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD), e a decomposição em termos de posto-(Lr, Lr, 1) por decomposição generalizada em autovalores, do inglês decomposition in multilinear rank-(Lr, Lr, 1) terms via generalized eigenvalue decomposition ((Lr, Lr, 1)-GEVD) para melhorar a TDE do componente LOS sob cenários desafiadores. A segunda é a abordagem de processamento adaptativo de amostras individuais utilizando rastreamento de subespaço a cada período de código, epoch em inglês. Usando processamento adaptativo propomos duas abordagem, uma aplicando FBA expandido (EFBA) e ESPS ao dados e estimando um HOE, e outra usando usa estimação paramétrica para estimar a DoA. Estendendo o modelo para um arranjo retangular uniforme, do inglês uniform rectangular array (URA), o fluxo de dados são tensores de terceira ordem. Para este modelo propomos três abordagens para TDE baseado em HOE, CPD-GEVD, e ESPRIT tensorial, respectivamente e empregando uma estratégia de truncamento sequencial para reduzir a quantidade de operações necessárias para cada modo do tensorCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Applications employing Global Navigation Satellite Systems (GNSS) to provide accurate positioning are subject to drastic degradation not only due to electromagnetic interference, but also due to multipath components caused by reflections and refractions in the environment. Safety-critical applications such as autonomous vehicles and civil aviation, and liability-critical applications such as fisheries management, automatic tolling, and precision agriculture depend on accurate positioning under such demanding scenarios. Typically, the more clustering occurs between the line-of-sight (LOS) component and multipath or non-line-of-sight (NLOS) components, the more inaccurate is the estimation of the positioning. State-of-the-art tensor based approaches for antenna array-based GNSS receivers apply tensor-based signal processing to separate the LOS components from NLOS components, thus mitigating the effects of the latter, using the multilinear singular value decomposition (MLSVD) to generate a higher-order eigenfilter (HOE) with forward-backward averaging (FBA) and expanded spatial smoothing (ESPS) preprocessing, direction of arrival (DoA) estimation and Khatri-Rao factorization (KRF), Procrustes estimation and Khatri-Rao factorization (ProKRaft), and the semi-algebraic framework for approximate canonical polyadic decomposition via simultaneous matrix diagonalization (SECSI), respectively. These approaches use filtering, parameter estimation and filtering, iterative algebraic factor matrix estimation and filtering, and algebraic factor matrix estimation, respectively. We propose two processing approaches to time-delay estimation (TDE). The first is batch processing taking data from several signal periods. Using batch processing we propose two algebraic approaches to TDE, in which diagonalization is achieved using the generalized eigenvalue decomposition (GEVD) of the first two frontal slices of the measurement tensor’s core tensor, estimated via MLSVD. The former approach, like the cited methods, in which simulations were performed with 1 LOS component and 1 NLOS component, and thus the measured data has full-rank tensor in all its modes, makes no assumption about the rank of the measurement tensor. The latter approach assumes scenarios in which more than 1 NLOS component is present and these are clustered, thus several vectors of one of the factor matrices which forms the tensor data are highly correlated, resulting in a rank-deficient measurement tensor in at least one mode. These proposed algebraic tensor-based schemes utilize the canonical polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD) and the decomposition in multilinear rank-(Lr, Lr, 1) terms via generalized eigenvalue decomposition ((Lr, Lr, 1)-GEVD) in order to improve the TDE of the LOS component in challenging scev narios. The second approach is adaptive processing of individual samples utilizing subspace tracking to iteratively estimate the subspace at each epoch. Using adaptive processing we propose two approaches, one applying FBA and ESPS to the data and estimating a higherorder eigenfilter, and the other using a parametric approach using DoA estimation. By extending the data model for an uniform rectangular array, we have a data stream of third-order tensors. For this model we propose three approaches to TDE based on HOE, CPD-GEVD, and standard tensor ESPRIT, respectively and employing a sequential truncation strategy to reduce the amount of operations necessary for each tensor mode

Sidereal filtering for multi-GNSS precise point positioning and deformation monitoring

PhD ThesisFor earthquake and tsunami early-warning, it is crucial that displacements resulting from earthquakes are recorded with speed and accuracy. Traditional methods based on seismometer data often suffer from errors during integration which results in the maximum displacement not being accurately recorded. In contrast, Global Navigation Satellite Systems (GNSS) can measure permanent static displacement directly; however it too is subject to errors, the main error of which is multipath. Multipath can lead to errors in the measurement of small displacements or mask the displacement completely. Multipath is dependent on the geometry of the GNSS constellation orbits and the antenna’s surrounds. GPS satellites have an orbital period of half a sidereal day with a near-sidereal repeating ground track. Similarly, the GLONASS constellation geometry repeats about once every eight sidereal days thus the satellite-reflector geometry will repeat with these same periods. By accurately determining the repeat periods it is possible to remove the multipath error by analysing data from the previous repeat periods. This method is known as sidereal filtering and can be used to improve the precision of GNSS coordinate time series and hence improve displacement measurements. This thesis looks to find the optimum geometry repeat period for the GLONASS constellation, which was found to be 689248 s and combine GPS and GLONASS for observation domain near-sidereal filtering. GLONASS-only filtering improves GLONASS coordinate solution standard deviations, on average, by 22.3%, 18.1% and 17.6% in the East, North and Up, whereas GPS and GLONASS combined filtering improves GPS and GLONASS standard deviations by 21.2%, 23.4% and 25.1%. The average maximum stability improvement, in terms of Allan deviation for all components is approximately 21.0% for GLONASS-only and 29.0% for combined filtering. Combined filtering produces more stable coordinate time series for averaging intervals over a few hundred seconds. It also reduces coordinate time series standard deviations and thus aids the measurement of small coordinate displacements and reduces the number of false alarms by half during displacement detection. Filtering improves the accuracy and precision of displacement estimates on average by about 2 mm, in terms of the difference between filtered and unfiltered RMSD and mean displacement values.UK Natural Environment Research Council (NERC

Geodetic Sciences

Space geodetic techniques, e.g., global navigation satellite systems (GNSS), Very Long Baseline Interferometry (VLBI), satellite gravimetry and altimetry, and GNSS Reflectometry & Radio Occultation, are capable of measuring small changes of the Earth�s shape, rotation, and gravity field, as well as mass changes in the Earth system with an unprecedented accuracy. This book is devoted to presenting recent results and development in space geodetic techniques and sciences, including GNSS, VLBI, gravimetry, geoid, geodetic atmosphere, geodetic geophysics and geodetic mass transport associated with the ocean, hydrology, cryosphere and solid-Earth. This book provides a good reference for geodetic techniques, engineers, scientists as well as user community

Advanced Integration of GNSS and External Sensors for Autonomous Mobility Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

The determination of subtle deformation signals using a permanent CGPS network in the Aegean

Geophysical motions can occur over a broad temporal spectrum, from high frequency seismic movements to very long period tectonic deformation. The Aegean region is tectonically one of the most active areas on Earth. There have, over the past 15 years, been a range of campaign style GPS studies which have looked to increase our knowledge of the area and better define the geodynamic processes involved. In 2002 the Center for the Observation and Modelling of Earthquakes and Tectonics (COMET) established a network of continuously operating GPS receivers (CGPS) throughout the region in order to add to the knowledge gained from previous studies. This thesis focuses on which tectonic motions can be observed using the COMET continuous GPS network. Approaches for the precise analytical estimation of subtle tectonic motion are presented. Daily coordinate estimates of COMET sites and a number of ITRF (International Terrestrial Reference Frame) sites around Europe were calculated using a precise point positioning strategy and ambiguity resolution using NASA’s GIPSY – OASIS II processing software and IGS (International GPS Service) precise products. Time series produced showed post fit standard deviations of 2-3 mm in the horizontal and 6-8 mm in the vertical. Significant annual periodic variation is observed in the time series. The coordinate time series studies were further refined using a selection of filters. Firstly, gross and sigma filters were applied to remove outliers, the data then had a range of regional filters applied looking to best define and remove the common mode error in the area. These filters produced mixed results with time series improvement occurring on a site by site basis. In some cases noise was reduced by a factor of 2 whilst in other cases there was little or no improvement. This combined with a lack of knowledge of the individual site movements led to the use of a filtered baseline method, whereby common mode error was removed purely on a site by site basis. This method revealed expansion across the Hellenic arc of the order of a few millimetres per year and sub millimetre north-south compaction behind the arc. It also revealed first evidence of transient motion at a number of sites parallel to the Hellenic arc. The transient signals occurred every 12 months ±1.5 and lasting for 40 – 100 days. These signals were not so much a reversal of tectonic motion akin to the silent earthquakes observed in Cascadia, Japan and Mexico, instead they appeared more as a pause in the otherwise consistent movement of the Aegean microplate overriding the subducting African lithosphere. In addition to the observed tectonic signals, the effects and implications of the two post processing strategies are analysed and discussed. Higher temporal frequency positioning is carried out on seismic events (Mw 6.7 earthquake Kithera, Mw 8.1 and Mw 6.7 earthquakes, Macquarie island) using instantaneous positioning followed by “sidereal filtering” whereby integer-cycle phase ambiguities are resolved using only single epochs of dual frequency phase and pseudorange data. These positions are then siderealy stacked to reduce the effects of geometry related error. The technique reduces geometry related noise by a factor ≈2 using epoch by epoch 30 second data. The feasibility of the technique for observing pre, co and post seismic signals is demonstrated. A visualisation tool was developed to allow the simultaneous observation of the tectonic motion of a CGPS network data over any spatial and temporal regimes

GPS Lessons Learned from the International Space Station, Space Shuttle and X-38

This document is a collection of writings concerning the application of Global Positioning System (GPS) technology to the International Space Station (ISS), Space Shuttle, and X-38 vehicles. An overview of how GPS technology was applied is given for each vehicle, including rationale behind the integration architecture, and rationale governing the use (or non-use) of GPS data during flight

Array processing techniques for direction of arrival estimation, communications, and localization in vehicular and wireless sensor networks

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Técnicas de processamentos de sinais para comunicações sem fio tem sido um tópico de interesse para pesquisas há mais de três décadas. De acordo com o padrão Release 9 desenvolvido pelo consorcio 3rd Generation Partnership Project (3GPP) sistemas utilizando múltiplas antenas foram adotados na quarta geração (4G) dos sistemas de comunicação sem fio, também conhecida em inglês como Long Term Evolution (LTE). Para a quinta geração (5G) dos sistemas de comunicação sem fio centenas de antenas devem ser incorporadas aos equipamentos, na arquitetura conhecida em inglês como massive multi-user Multiple Input Multiple Output (MIMO). A presença de múltiplas antenas provê benefícios como o ganho do arranjo, ganho de diversidade, ganho espacial e redução de interferência. Além disso, arranjos de antenas possibilitam a filtragem espacial e a estimação de parâmetros, ambos podem ser usados para se resolver problemas que antes não eram vistos pelo prisma de processamento de sinais. O objetivo dessa tese é superar a lacuna entre a teoria de processamento de sinais e as aplicações da mesma em problemas reais. Tradicionalmente, técnicas de processamento de sinais assumem a existência de um arranjo de antenas ideal. Portanto, para que tais técnicas sejam exploradas em aplicações reais, um conjunto robusto de métodos para interpolação do arranjo é fundamental. Estes métodos são desenvolvidos nesta tese. Além disso problemas no campo de redes de sensores e redes veiculares são tratados nesta tese utilizando-se uma perspectiva de processamento de sinais. Nessa tesa métodos inovadores de interpolação de arranjos são apresentados e sua performance é testada utilizando-se cenários reais. Conceitos de processamento de sinais são implementados no contexto de redes de sensores. Esses conceitos possibilitam um nível de sincronização suficiente para a aplicação de sistemas de múltiplas antenas distribuídos, o que resulta em uma rede com maior vida útil e melhor performance. Métodos de processamento de sinais em arranjos são propostos para resolver o problema de localização baseada em sinais de rádio em redes veiculares, com aplicações em segurança de estradas e proteção de pedestres. Esta tese foi escrita em língua inglesa, um sumário em língua portuguesa é apresentado ao final da mesma.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Array signal processing in wireless communication has been a topic of interest in research for over three decades. In the fourth generation (4G) of the wireless communication systems, also known as Long Term Evolution (LTE), multi antenna systems have been adopted according to the Release 9 of the 3rd Generation Partnership Project (3GPP). For the fifth generation (5G) of the wireless communication systems, hundreds of antennas should be incorporated to the devices in a massive multi-user Multiple Input Multiple Output (MIMO) architecture. The presence of multiple antennas provides array gain, diversity gain, spatial gain, and interference reduction. Furthermore, arrays enable spatial filtering and parameter estimation, which can be used to help solve problems that could not previously be addressed from a signal processing perspective. The aim of this thesis is to bridge some gaps between signal processing theory and real world applications. Array processing techniques traditionally assume an ideal array. Therefore, in order to exploit such techniques, a robust set of methods for array interpolation are fundamental and are developed in this work. Problems in the field of wireless sensor networks and vehicular networks are also addressed from an array signal processing perspective. In this dissertation, novel methods for array interpolation are presented and their performance in real world scenarios is evaluated. Signal processing concepts are implemented in the context of a wireless sensor network. These concepts provide a level of synchronization sufficient for distributed multi antenna communication to be applied, resulting in improved lifetime and improved overall network behaviour. Array signal processing methods are proposed to solve the problem of radio based localization in vehicular network scenarios with applications in road safety and pedestrian protection