438 research outputs found

    Edge Artificial Intelligence for Real-Time Target Monitoring

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    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

    Local positioning with sensor-enabled passive multistandard RFID transponders

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    RFID is used today in many fields of every day life like access control, anti-theft protection or logistics. Within this article a short overview of the basic RFID principles and the EPC protocol flow is given at first. Afterwards new design approaches for RFID systems within the scope of the research project RFID-S are presented

    AGV RAD: AGV positioning system for ports using microwave doppler radar

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    Automation and intelligence have become an inevitable trend in the development of container terminals. The AGV (Automated Guided Vehicle) positioning is a primary problem to build the automated ports. Although the existing Ultra-High Frequency(UHF) RFID technology has good measurement accuracy and stability in the port AGV positioning, the exposed magnetic tags are easy to damage under the common heavy load, and its construction and maintenance cost is unbearable to most ports. Among the candidate technologies for the AGV positioning, microwave Doppler radar has a strong penetrating ability, and can work well in a complex environment (day and night, foggy, rainy). Therefore, the microwave Doppler radar-based AGV positioning system has attracted a lot of attention. In this thesis, a test system using the above technique was established, together with a NI myRIO real-time Wi-Fi compatible computation platform. Several computation algorithms were implemented to extract the accurate values of range and velocity. Wavelet denoising with the adapted threshold function was considered to filter noise contained in radar signals. In the frequency domain analysis, FFT and Chirp-Z Transform (CZT) joint algorithm was proposed to suppress the influence of fence effects and also improves real-time performance. In addition, 2D-FFT is used to calculate velocity of AGV. According to the port-like environment, the suitable AGV positioning algorithm and communication method based on microwave Doppler radars and NI myRIO-1900s also be proposed. The effectiveness of the proposed system was experimentally tested and several results are included in this thesis.Automação e inteligĂȘncia artifical tornaram-se uma tendĂȘncia inevitĂĄvel no desenvolvimento dos terminais dos contentores. O posicionamento do VAG (VeĂ­culo AutĂłnomo Guiado) Ă© um dos problemas principais para construir as portas automatizadas. Embora a tecnologia RFID de frequĂȘncia ultra-alta (UHF) existente tenha uma boa precisĂŁo e estabilidade de medição no posicionamento VAG dos portos, as etiquetas magnĂ©ticas expostas sĂŁo fĂĄceis de danificar sob a comum carga pesada e o seu habitual custo de construção e manutenção Ă© insuportĂĄvel para a maioria das portos. Entre as tecnologias para o posicionamento VAG, o radar Doppler de microondas possui uma forte capacidade de penetração e pode funcionar bem em ambientes complexos (dia, noite, nevoeiro e chuva). Portanto, o sistema de posicionamento VAG baseado em radar Doppler de microondas atraiu muita atenção. Nesta tese, foi estabelecido um sistema de teste usando a tĂ©cnica acima mencionada, juntamente com uma plataforma de computação em tempo real, NI myRIO compatĂ­vel com Wi-Fi. VĂĄrios algoritmos de computação foram envolvidos para extrair os valores precisos de distancia e velocidade. O “denoising” de wavelets com a função de limiar adaptado foi utilizado para filtrar o ruĂ­do nos sinais de radar. Na anĂĄlise do domĂ­nio da frequĂȘncia, o algoritmo conjunto FFT e Chirp-Z Transform (CZT) foi proposto para suprimir a influĂȘncia dos efeitos de resolução e tambĂ©m melhorar o desempenho em tempo real. AlĂ©m disso, o algoritmo 2D-FFT Ă© usado para calcular a velocidade do VAG. De acordo com o ambiente dos portos, o algoritmo de posicionamento VAG e o mĂ©todo de comunicação adequado baseados em radares Doppler de microondas e NI myRIO-1900s tambĂ©m serĂŁo propostos. A eficiĂȘncia do sistema proposto foi testada experimentalmente e vĂĄrios resultados estĂŁo descritos nesta dissertação

    MicNest: Long-Range Instant Acoustic Localization of Drones in Precise Landing

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    We present MicNest: an acoustic localization system enabling precise landing of aerial drones. Drone landing is a crucial step in a drone's operation, especially as high-bandwidth wireless networks, such as 5G, enable beyond-line-of-sight operation in a shared airspace and applications such as instant asset delivery with drones gain traction. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics while airborne; iii) as location information is to be used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our real-world experiments show that MicNest is able to localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency

    A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

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    The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

    Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

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    Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.publishedVersio

    Détection de la stratigraphie en milieu de neige sÚche à l'aide d'un radar à onde continue modulé en fréquence (FMCW) de 24 GHz

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    ConsidĂ©rant l’augmentation en popularitĂ© des activitĂ©s hivernales dans l’arriĂšre-pays, il est important de s’assurer qu’elles puissent ĂȘtre pratiquĂ©e en toute sĂ©curitĂ©. Il devient alors impĂ©ratif de mieux comprendre le rĂŽle des interfaces de neige problĂ©matiques menant Ă  des instabilitĂ©s qui influence le danger d’avalanche. La cueillette des donnĂ©es gĂ©ophysiques de la neige en terrain avalancheux demeure toutefois restreinte due aux contraintes logistiques, temporelles et financiĂšres limitant l’accĂšs Ă  l’immense territoire. L’objectif de ce mĂ©moire est donc de dĂ©velopper une mĂ©thode automatisĂ©e et rapide de dĂ©tection des interfaces Ă  grand contraste de propriĂ©tĂ©s nivales pouvant potentiellement mener Ă  de l’instabilitĂ© Ă  l’aide d’un radar Ă  onde continue modulĂ©e en frĂ©quence (FMCW) de 24 GHz. Les vents forts, les Ă©vĂ©nements de pluie-sur-neige, les Ă©vĂšnements de gel-dĂ©gel hivernaux et les longues pĂ©riodes de froid de la pĂ©ninsule GaspĂ©sienne entraĂźnent souvent la formation d’un manteau neigeux complexe intĂ©ressant pour l’étude de ces interfaces instables. Ce projet de recherche se concentre sur le dĂ©veloppement d’une mĂ©thode empirique de dĂ©tection de ces interfaces Ă  l’aide de donnĂ©es in-situ recueillies dans le territoire des Chic-Chocs au QuĂ©bec. Les mesures radar ont suivi deux protocoles diffĂ©rents : 1) acquisition de donnĂ©es en mode mobile pour comprendre l’interaction et la sensibilitĂ© de l’onde radar avec la neige et ainsi optimiser les paramĂštres de l’instrument pour d’éventuelles Ă©tudes de variabilitĂ© spatiales, et 2) acquisition de donnĂ©es en mode fixe pour Ă©valuer le potentiel du dispositif radar Ă  Ă©tudier la variabilitĂ© temporelle de la stratigraphie du manteau neigeux et ainsi mieux comprendre la persistance des interfaces Ă  grands contrastes et le rĂŽle que joue la mĂ©tĂ©orologie dans leur dĂ©veloppement. Plus spĂ©cifiquement, le principe du radar est de quantifier le contraste diĂ©lectrique entre les diffĂ©rentes couches de neige. En Ă©tablissant un seuil sur l’amplitude radar et en connaissant la vitesse de propagation du signal dans diffĂ©rentes strates de neige, il est possible de corrĂ©ler la profondeur des pics d’amplitude avec les interfaces potentiellement instables. Les donnĂ©es de comparaison in-situ utilisĂ©es initialement pour bien comprendre la signature du signal radar proviennent de profils de neige manuels et d’un Snow Micro Penetrometer (SMP). Ces donnĂ©es ont aussi servi Ă  la validation des rĂ©sultats et Ă  Ă©tablir la performance du dispositif radar. Lors de la validation, les mesures radar ont dĂ©montrĂ© un bon potentiel pour l’étude de la variabilitĂ© spatiale et temporelle en dĂ©tectant 80% des interfaces identifiĂ©es manuellement, et ce, avec une erreur de positionnement vertical de 3 cm.Abstract : Considering the increased popularity for backcountry mountain recreation activities, problematic snowpack interfaces are currently of great interest given their impact on snow stability. As such, the identification of interface vertical locations in the snowpack and their spatial variability is essential for avalanche danger forecasting. The GaspĂ© Peninsula specific climate (strong winds, rain-on-snow events, winter thaw and prolonged very cold temperatures) often leads to a complex snowpack development, where the need of improved monitoring is important. The goal of this research is to asses an automated method to detect contrasted snow interfaces (i.e. contrasted layers) using a 24 GHz Frequency Modulated Continuous Wave (FMCW) portable radar. Based on different in-situ configurations (upward and downward looking), we compared the radar amplitude signals with in-situ snow geophysical measurements, including Snow Micro Penetrometer. Radar measurements have been done following two different protocols: 1) mobile radar looking-up and down in order to understand the radar-snow wave interactions and optimize its parameters for spatial variability assessment of contrasted snow layers and 2) fixed radar mounted on a tripod looking down to evaluate its potential to study snow stratigraphy temporal variability in one fixed location. Results show good agreements with compared validation data with 80% of manually identified interfaces detection and a vertical positioning error of 3 cm. The presented FMCW radar appears to have a good potential for spatial and temporal variability assessment of snowpack stratigraphy
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