Tensor-based tracking schemes for time-delay estimation in GNSS multi-antenna receivers

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.Embora os receptores GNSS (Global Navigation Satellite Systems) alcancem atualmente alta precisão ao processar sua localização geográfica sob condições de Linha de Visão (Line of Sight), erros devido a interferência por componentes multipercurso e ruído são as fontes mais degradantes desse sistema. A fim de resolver a interferência multipercurso, receptores baseados em múltiplas antenas tornaram-se o foco de pesquisa e desenvolvimento tecnológico devido ao fato de que podem mitigar a ocorrência de multipercurso fornecendo as melhores estimativas para o atraso do sinal transmitido, que é um parâmetro relevante para determinar a geolocalização do usuário. Neste contexto, abordagens tensoriais baseadas em modelos PARAFAC (PArallel FActor Analysis) têm sido propostas na literatura, proporcionando um ótimo desempenho. Como essas técnicas são baseadas em subespaços, considerando um cenário de rastreamento em tempo real, o cálculo de uma EVD (Eigenvalue Decomposition)/SVD (Singular Value Decomposition) completa para estimativa de subespaço de sinal em cada instante de amostragem não é adequado, devido a razões de complexidade. Portanto, uma alternativa para reduzir o tempo de computação (Time of Computing) de estimativas de subespacos tem sido o desenvolvimento de algoritmos de rastreamento de subespaço. Este trabalho propõe o emprego de dois esquemas de rastreamento de subespaços para fornecer uma redução no desempenho computacional geral das técnicas de estimativa de atraso de tempo baseadas em tensores.Although Global Navigation Satellite Systems (GNSS) receivers nowadays achieve high accuracy when processing their geographic location under conditions of Line of Sight (LOS), errors due to interference by multipath and noise are the most degrading sources of accuracy. In order to solve the multipath interference, receivers based on multiple antennas have become the focus of technological research and development due to the fact they can mitigate multipath occurrence providing best estimates to the transmitted signal time-delay, which is a relevant parameter for determining the user’s geolocation. In this context, tensor-based approaches based on PArallel FActor Analysis (PARAFAC) models have been proposed in the literature, providing optimal performance. As these techniques are subspace-based, considering a real-time tracking scenario, the computation of a full Eigenvalue Decomposition (EVD)/Singular Value Decomposition (SVD) for signal subspace estimation at every sampling instant is not suitable, due to complexity reasons. Therefore, an alternative to reduce the Time of Computing (ToC) of subspace estimations has been the development of subspace tracking algorithms. This work proposes the employment of two subspace tracking schemes to provide a reduction in the overall computational performance of tensor-based time-delay estimation techniques

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

Advanced RFI detection, RFI excision, and spectrum sensing : algorithms and performance analyses

Because of intentional and unintentional man-made interference, radio frequency interference (RFI) is causing performance loss in various radio frequency operating systems such as microwave radiometry, radio astronomy, satellite communications, ultra-wideband communications, radar, and cognitive radio. To overcome the impact of RFI, a robust RFI detection coupled with an efficient RFI excision are, thus, needed. Amongst their limitations, the existing techniques tend to be computationally complex and render inefficient RFI excision. On the other hand, the state-of-the-art on cognitive radio (CR) encompasses numerous spectrum sensing techniques. However, most of the existing techniques either rely on the availability of the channel state information (CSI) or the primary signal characteristics. Motivated by the highlighted limitations, this Ph.D. dissertation presents research investigations and results grouped into three themes: advanced RFI detection, advanced RFI excision, and advanced spectrum sensing. Regarding advanced RFI detection, this dissertation presents five RFI detectors: a power detector (PD), an energy detector (ED), an eigenvalue detector (EvD), a matrix-based detector, and a tensor-based detector. First, a computationally simple PD is investigated to detect a brodband RFI. By assuming Nakagami-m fading channels, exact closed-form expressions for the probabilities of RFI detection and of false alarm are derived and validated via simulations. Simulations also demonstrate that PD outperforms kurtosis detector (KD). Second, an ED is investigated for RFI detection in wireless communication systems. Its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated ED outperforms KD and a generalized likelihood ratio test (GLRT) detector. The performance of ED is also assessed using real-world RFI contaminated data. Third, a blind EvD is proposed for single-input multiple-output (SIMO) systems that may suffer from RFI. To characterize the performance of EvD, performance closed-form expressions valid for infinitely huge samples are derived and validated through simulations. Simulations also corroborate that EvD manifests, even under sample starved settings, a comparable detection performance with a GLRT detector fed with the knowledge of the signal of interest (SOI) channel and a matched subspace detector fed with the SOI and RFI channels. At last, for a robust detection of RFI received through a multi-path fading channel, this dissertation presents matrix-based and tensor-based multi-antenna RFI detectors while introducing a tensor-based hypothesis testing framework. To characterize the performance of these detectors, performance analyses have been pursued. Simulations assess the performance of the proposed detectors and validate the derived asymptotic characterizations. Concerning advanced RFI excision, this dissertation introduces a multi-linear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for SIMO systems. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibits a faster convergence to a zero excision error than MLSEP which, in turn, converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller. With regard to advanced spectrum sensing, having been inspired by an F–test detector with a simple analytical false alarm threshold expression considered an alternative to the existing blind detectors, this dissertation presents and evaluates simple F–test based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna CRs. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are also robust against noise uncertainty

Low cost antenna array based drone tracking device for outdoor environment

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2019.Aplicações para técnicas de Direção de Chegada (DoA) têm crescido drasticamente em várias áreas, desde os tradicionais sistemas de comunicação sem fio e operações de resgate até os sistemas GNSS e rastreamento de drones. Particularmente, as forças policiais e as empresas de segurança têm voltado sua atenção para os dispositivos de rastreamento de drones, devido ao número de acidentes e incidentes envolvendo estes Veículos Aéreos não Tripulados (VANTs). Agora, novos sistemas e dispositivos que fornecem segurança a cidadãos e clientes cresceram e ganharam espaço no mercado. Para detectar a presença de drones e rastreá-los existe uma variedade de soluções altamente caras no mercado. Porém, a estimativa da localização de um alvo pode ser obtida usando hardware barato, comprado facimente no mercado, e com técnicas de Direção de Chegada. Data esta estimativa, algumas ações podem ser tomadas pelo responsável pela segurança no local. Trabalhos anteriores na estimativa de direção de chegada usando arranjo de antenas foram propostos, mas sem uma abordagem prática. Nesta dissertação, propõe-se um dispositivo de rastreamento de drones baseado em arranjo de antenas de baixo custo para ambientes externos. A solução proposta é dividida em partes de hardware e software. A parte de hardware do dispositivo proposto é baseada em componentes fáceis de serem encontrados no mercado, como um arranjo de antena omnidirecional, uma plataforma SDR (Rádio Definido por Software) de 4 canais com frequência de portadora variando de 70 MHz a 6 GHz, uma placa-mãe FPGA e um laptop. A parte do software inclui algoritmos para calibração, seleção de ordem de modelo (MOS) e estimativa de DoA, incluindo etapas específicas de pré-processamento para aumentar a precisão dos cálculos para os métodos de DoA. Avaliamos o desempenho de nossa solução de baixo custo, proposta para ambientes externos, e de acordo com as medições de campo, mostra-se que, quando o transmissor está na posição frontal, ou seja, com um DoA variando de -60° a 60°, o máximo e a média dos erros de DoA são 6° e 1,6°, respectivamente.Applications of Direction of Arrival (DoA) techniques have dramatically increased in various areas ranging from the traditional wireless communication systems and rescue operations to GNSS systems and drone tracking. Particularly, police forces and security companies have drawn their attention to drone tracking devices, due to the number of accidents and incidents involving Unmanned Aerial Vehicles (UAVs). Now, new systems and devices that provide the safeness of citizens and clients, have grown and gained space on the market. In order to detect the presence of drones and to track them, there is a variety of highly expensive solutions in the market. In this way the estimation of a target’s location can be obtained using off-the-shelf hardware with Direction of Arrival techniques. Consequently some actions can be taken by the responsible for the security in that place. Previous works in DoA estimation using antenna arrays have been proposed but with no practical approach. In this dissertation, it is proposed a low cost antenna array based drone tracking device for outdoor environments. The proposed solution is divided into hardware and software parts. The hardware part of the proposed device is based on off-the-shelf components such as an omni-directional antenna array, a 4 channel Software Defined Radio (SDR) platform with carrier frequency ranging from 70 MHz to 6 GHz, a FPGA motherboard and a laptop. The software part includes algorithms for calibration, model order selection (MOS) and DoA estimation, including specific pre-processing steps to increase the DoA accuracy. The performance of our proposed low cost solution is evaluated in outdoor scenarios. According to our measurement campaigns, it is shown that, when the array is in the front fire position, i.e. with a DoA ranging from -60° to 60°, the maximum and the average DoA errors are 6° and 1,6°, respectively

Methods of navigation: An introduction to technological navigation

Ihminen on historian aikana aina navigoinut. Teknologinen navigointi syntyi merenkulussa, koska avomerellä tarvittiin mittauksia oman sijainnin määrittämiseksi. Lentokoneet, ohjukset ja avaruusalukset sekä kuivalla maalla liikkuvat kulkuneuvot ja jopa jalankulkijat kaikki ”navigoivat” nykyteknologioiden avulla. Kehitys on pääosin kahden teknologian ansiota: satelliittipaikannuksen, kuten GPS:n (Global Positioning System), ja inertianavigoinnin. Myös tieto- ja viestintätekniikka on kehittynyt, erityisesti rekursiivinen lineaarinen suodatus eli Kalmanin suodin. Lisäksi pienet ja hinnaltaan huokeat digitaaliset anturit ovat mullistamassa jokapäiväisen navigoinnin. Tässä kirjassa käsiteltäviä aiheita ovat navigoinnin perusteet, stokastiset prosessit, Kalmanin suodin, inertianavigoinnin teknologiat ja menetelmät, GNSS-signaalien rakenne, kantoaallon vaihemittaukset ja kokonaistuntemattomat, tosiaikainen GNSSpaikannus ja navigointi, differentiaalikorjausten viestintäratkaisut ja standardit, GNSStukiasemat ja -verkot, satelliittipohjaiset parannusjärjestelmät, ilmagravimetria sekä anturifuusio ja sattuman anturit.Historically, humankind has always navigated. Technological navigation originated in seafaring, because on the open ocean, measurements are needed in order to determine one’s own location as a part of navigation. Aircraft, rockets and spacecraft as well as vehicles moving on dry land, and even pedestrians, all ”navigate” by means of modern technologies. This development is mainly due to two technologies: satellite positioning, such as GPS (the Global Positioning System) and inertial navigation. Also information and communication technologiy has evolved: especially recursive linear filtering or the Kalman filter. Furthermore, small and inexpensive digital sensors are revolutionising everyday navigation. Subjects explained in this book are the fundamentals of navigation, stochastic processes, the Kalman filter, inertial navigation technology and methods, GNSS signal structure, carrier-phase measurement and ambiguities, real-time GNSS positioning and navigation, communication solutions and standards for differential corrections, GNSS base stations and networks, satellite-based augmentation systems, airborne gravimetry, sensor fusion and sensors of opportunity

DoA and ToA Estimation, Device Positioning and Network Synchronization in 5G New Radio : Algorithms and Performance Analysis

Location information plays a significant role not only in our everyday life through various location-based services, but also in emerging technologies such as virtual reality, robotics, and autonomous driving. In contrast to the existing and earlier cellular generations, positioning has been considered as a key element in future cellular networks from the very beginning of the fifth generation (5G) standardization process. Even though the earlier generations are capably of providing coarse location estimates, the achieved accuracy is far from the expected even sub-meter positioning accuracy envisioned in the context of 5G networks. In general, 5G new radio (NR) networks provide a convenient infrastructure for positioning by means of wider bandwidths, larger antenna arrays, and even more densely deployed networks especially at high millimeter wave (mmWave) frequencies. Building on dense 5G NR networks, this thesis focuses on the development of novel network-centric positioning frameworks by exploiting the existing NR reference signals. The contributions in this thesis can be grouped into topics based on the considered frequency ranges and the employed beamforming (BF) schemes therein. First, novel cascaded algorithms for sequential device positioning are proposed assuming 5G NR networks operating at the lower sub-6 GHz frequency range and equipped with digital BF capabilities. In the first stage of the cascaded solution, two sequential estimators are proposed for joint direction of arrival (DoA) and time of arrival (ToA) estimation facilitating the received reference signals. Thereafter, the second-stage sequential estimators employing the obtained DoA and ToA estimates are proposed for joint positioning and network synchronization resulting in not only device location estimates, but also clock parameter estimates that are obtained as a valuable by-product. Such a choice stems from the fact that the ToA estimates are not feasible for positioning as such due to the clock instabilities in low-cost devices and the insufficient level of synchronization in the cellular networks. Second, a similar cascaded algorithm for joint positioning and network synchronization is proposed in the context of dense mmWave 5G networks and fundamentally different analog BFs. In particular, a novel joint DoA and ToA estimator is proposed by fusing information from multiple received beams based on a novel beam-selection method. In addition, the theoretical performance limits are derived and compared to those obtained using the digital BFs. The cascaded framework is completed with the second-stage positioning solution in a similar manner as in the case of digital BFs. The performance of both frameworks is evaluated and analyzed in various scenarios using extensive computer simulations relying on the latest 5G NR numerology and a ray-tracing tool. Overall, this thesis provides valuable insights into practical positioning algorithms and their performance when relying solely on the 5G NR networks and available signalling therein. The obtained results in this thesis indicate that the envisioned sub-meter positioning accuracy is technically feasible using NR-based solutions

Edge Artificial Intelligence for Real-Time Target Monitoring

The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties. In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial. In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device. Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set

Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications Surveys & Tutorial