Design and implementation of an SDR-based multi-frequency ground-based SAR system

Synthetic Aperture Radar (SAR) has proven a valuable tool in the monitoring of the Earth, either at a global or local scales. SAR is a coherent radar system able to image extended areas with high resolution, and finds applications in many areas such as forestry, agriculture, mining, structure inspection or security operations. Although space-borne SAR systems can image extended areas, their main limitation is the long revisit times, which are not suitable for applications where the target experiments rapid changes, in the scale of minutes to few days. GBSAR systems have proven useful to fill this revisit time gap by imaging relatively small areas continuously, with extensions usually smaller than a few square kilometers. Ground Based SAR (GBSAR) systems have been used extensively for the monitoring of slope instability, and are a common tool in the mining sector. The development of the GBSAR is relatively recent, and various developments have taken place since the 2000s, transitioning from the usage of Vector Network Analyzers (VNAs) to custom radar cores tailored for this application. This transition is accompanied by a reduction in cost, but at the same time is accompanied by a loss of operational flexibility. Specifically, most GBSAR sensors now operate at a single frequency, losing the value of the multi-band operation that VNAs provided. This work is motivated by the idea that it is worth to use the value of multi-frequency GBSAR measurements, while maintaining a limited system cost. In order to implement a GBSAR with these characteristics, it is realized that Software Defined Radio (SDR) devices are a good option for fast and flexible implementation of broadband transceivers. This thesis details the design and implementation process of an SDR-based Frequency Modulated Continuous Wave (FMCW) GBSAR system from the ground up, presenting the main issues related with the usage of the most common SDR analog architecture, the Zero-IF transceiver. The main problem is determined to be the behavior of spurs related to IQ imbalances of the analog transceiver with the FMCW demodulation process. Two effective techniques to overcome these issues, the Super Spatial Variant Apodization (SSVA) and the Short Time Fourier Transform (STFT) signal reconstruction techniques, are implemented and tested. The thesis also deals with the digital implementation of the signal generator and digital receiver, which are implemented on top of an RF Network-on-Chip (RFNoC) architecture in the SDR Field Programmable Gate Array (FPGA). Another important aspect of this work is the development of an radiofrequency front-end that extends the capabilities of the SDR, implementing filtering, amplification, leakage mitigation and up-conversion to X-band. Finally, a set of test campaigns is described, in which the operation of the system is verified and the value of multi-frequency GBSAR observations is shown.El radar d'obertura sintètica (SAR) ha demostrat ser una eina valuosa en el monitoratge de la Terra, sigui a escala global o local. El SAR és un sistema de radar coherent capaç d’obtenir imatges de zones extenses amb alta resolució i té aplicacions en moltes àrees com la silvicultura, l’agricultura, la mineria, la inspecció d’estructures o les operacions de seguretat. Tot i que els sistemes SAR embarcats en plataformes orbitals poden obtenir imatges d'àrees extenses, la seva principal limitació és el temps de revisita, que no són adequats per a aplicacions on l'objectiu experimenta canvis ràpids, en una escala de minuts a pocs dies. Els sistemes GBSAR han demostrat ser útils per omplir aquesta bretxa de temps, obtenint imatges d'àrees relativament petites de manera contínua, amb extensions generalment inferiors a uns pocs quilòmetres quadrats. Els sistemes SAR terrestres (GBSAR) s’han utilitzat àmpliament per al control de la inestabilitat de talussos i esllavissades i són una eina comuna al sector miner. El desenvolupament del GBSAR és relativament recent i s’han produït diversos desenvolupaments des de la dècada de 2000, passant de l’ús d’analitzadors de xarxes vectorials (VNA) a nuclis de radar personalitzats i adaptats a aquesta aplicació. Aquesta transició s’acompanya d’una reducció del cost, però al mateix temps d’una pèrdua de flexibilitat operativa. Concretament, la majoria dels sensors GBSAR funcionen a una única freqüència, perdent el valor de l’operació en múltiples bandes que proporcionaven els VNA. Aquesta tesi està motivada per la idea de recuperar el valor de les mesures GBSAR multifreqüència, mantenint un cost del sistema limitat. Per tal d’implementar un GBSAR amb aquestes característiques, s’adona que els dispositius de ràdio definida per software (SDR) són una bona opció per a la implementació ràpida i flexible dels transceptors de banda ampla. Aquesta tesi detalla el procés de disseny i implementació d’un sistema GBSAR d’ona contínua modulada en freqüència (FMCW) basat en la tecnologia SDR, presentant els principals problemes relacionats amb l’ús de l’arquitectura analògica de SDR més comuna, el transceptor Zero-IF. Es determina que el problema principal és el comportament dels espuris relacionats amb el balanç de les cadenes de fase i quadratura del transceptor analògic amb el procés de desmodulació FMCW. S’implementen i comproven dues tècniques efectives per minimitzar aquests problemes basades en la reconstrucció de la senyal contaminada per espuris: la tècnica anomenada Super Spatial Variant Apodization (SSVA) i una tècnica basada en la transformada de Fourier amb finestra (STFT). La tesi també tracta la implementació digital del generador de senyal i del receptor digital, que s’implementen sobre una arquitectura RF Network-on-Chip (RFNoC). Un altre aspecte important d’aquesta tesi és el desenvolupament d’un front-end de radiofreqüència que amplia les capacitats de la SDR, implementant filtratge, amplificació, millora de l'aïllament entre transmissió i recepció i conversió a banda X. Finalment, es descriu un conjunt de campanyes de prova en què es verifica el funcionament del sistema i es mostra el valor de les observacions GBSAR multifreqüència

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Electromagnetic ray-tracing for the investigation of multipath and vibration signatures in radar imagery

Synthetic Aperture Radar (SAR) imagery has been used extensively within UK Defence and Intelligence for many years. Despite this, the exploitation of SAR imagery is still challenging to the inexperienced imagery analyst as the non-literal image provided for exploitation requires careful consideration of the imaging geometry, the target being imaged and the physics of radar interactions with objects. It is therefore not surprising to note that in 2017 the most useful tool available to a radar imagery analyst is a contextual optical image of the same area. This body of work presents a way to address this by adopting recent advances in radar signal processing and computational geometry to develop a SAR simulator called SARCASTIC (SAR Ray-Caster for the Intelligence Community) that can rapidly render a scene with the precise collection geometry of an image being exploited. The work provides a detailed derivation of the simulator from first principals. It is then validated against a range of real-world SAR collection systems. The work shows that such a simulator can provide an analyst with the necessary tools to extract intelligence from a collection that is unavailable to a conventional imaging system. The thesis then describes a new technique that allows a vibrating target to be detected within a SAR collection. The simulator is used to predict a unique scattering signature - described as a one-sided paired echo. Finally an experiment is described that was performed by Cranfield University to specifications determined by SARCASTIC which show that the unique radar signature can actually occur within a SAR collection

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests

Natural and anthropogenic disturbances pose a significant threat to forest condition. Continuous, reliable and accurate forest monitoring systems are needed to provide earlywarning of potential declines in forest condition. To address that need, state-of-the-art simulationsmodelswere used to evaluate the utility of C-, L- and P-band synthetic aperture radar (SAR) sensors within an integrated Earth-Observation monitoring system for beech, oak and coniferous forests in Romania. The electromagnetic simulations showed differentiated sensitivity to vegetation water content, leaf area index, and forest disturbance depending on SAR wavelength and forest structure. C-band data was largely influenced by foliage volume and therefore may be useful for monitoring defoliation. Changes in water content modulated the C-band signal by b1 dBwhichmay be insufficient for a meaningful retrieval of drought effects on forest. Cband sensitivity to significant clear-cuts was rather low (1.5 dB). More subtle effects such as selective logging or thinning may not be easily detected using C- or L-band data with the longer P-band needed for retrieving small intensity forest disturbances. Overall, the simulations emphasize that additional effort is needed to overcome current limitations arising from the use of a single frequency, acquisition time and geometry by tapping the advantages of dense time series, and by combining acquisitions from active and passive sensors. The simulation results may be applicable to forests outside of Romania since the forests types used in the study have similar morphological characteristics to forests elsewhere in Europe.Romanian National Agency for Scientific Research and Innovation Authorit

Scalable computing for earth observation - Application on Sea Ice analysis

In recent years, Deep learning (DL) networks have shown considerable improvements and have become a preferred methodology in many different applications. These networks have outperformed other classical techniques, particularly in large data settings. In earth observation from the satellite field, for example, DL algorithms have demonstrated the ability to learn complicated nonlinear relationships in input data accurately. Thus, it contributed to advancement in this field. However, the training process of these networks has heavy computational overheads. The reason is two-fold: The sizable complexity of these networks and the high number of training samples needed to learn all parameters comprising these architectures. Although the quantity of training data enhances the accuracy of the trained models in general, the computational cost may restrict the amount of analysis that can be done. This issue is particularly critical in satellite remote sensing, where a myriad of satellites generate an enormous amount of data daily, and acquiring in-situ ground truth for building a large training dataset is a fundamental prerequisite. This dissertation considers various aspects of deep learning based sea ice monitoring from SAR data. In this application, labeling data is very costly and time-consuming. Also, in some cases, it is not even achievable due to challenges in establishing the required domain knowledge, specifically when it comes to monitoring Arctic Sea ice with Synthetic Aperture Radar (SAR), which is the application domain of this thesis. Because the Arctic is remote, has long dark seasons, and has a very dynamic weather system, the collection of reliable in-situ data is very demanding. In addition to the challenges of interpreting SAR data of sea ice, this issue makes SAR-based sea ice analysis with DL networks a complicated process. We propose novel DL methods to cope with the problems of scarce training data and address the computational cost of the training process. We analyze DL network capabilities based on self-designed architectures and learn strategies, such as transfer learning for sea ice classification. We also address the scarcity of training data by proposing a novel deep semi-supervised learning method based on SAR data for incorporating unlabeled data information into the training process. Finally, a new distributed DL method that can be used in a semi-supervised manner is proposed to address the computational complexity of deep neural network training