Time Reversal Compressive Sensing MIMO Radar Systems

Active radar systems transmit a probing signal and use the return backscatters received from the channel to determine properties of the channel. After detecting the presence of targets, the localization of targets is achieved by estimating relevant target parameters, including the range, Doppler's frequency, and azimuth associated with the targets. A major source of error in parameter estimation is the presence of clutter (undesired targets) that also reflects the probing signal back to the radar. To eliminate the fading effect introduced by backscatters originating from the clutter, the multiple input multiple output (MIMO) radar transmits a set of simultaneous uncorrelated probing signals from the transmit elements comprising the transmit array. A major problem with MIMO radars is the large amount of data generated when the recorded backscatters are discretized at the Nyquist sampling rate. This in turn necessitates the need of expensive, high speed analog-to-digital converter circuits. Compressive sensing (CS) has emerged as a new sampling paradigm for reconstructing sparse signals with relatively few observations and at a lower computational cost compared to other sparsity promoting approaching. Although compressive beamforming has the potential of high resolution estimates, the approach has several limitations arising mainly due to the difficulty in achieving complete incoherency and sparsity in the CS dictionary. This PhD thesis will apply the principle of time reversal (TR) to MIMO radars to improve the incoherency and sparsity of the compressive beamforming dictionary. The resulting CS TR MIMO radar is analytically studied and assessed for performance gains as compared to the conventional MIMO systems

Frequency Diverse Array Radar: Signal Characterization and Measurement Accuracy

Radar systems provide an important remote sensing capability, and are crucial to the layered sensing vision; a concept of operation that aims to apply the right number of the right types of sensors, in the right places, at the right times for superior battle space situational awareness. The layered sensing vision poses a range of technical challenges, including radar, that are yet to be addressed. To address the radar-specific design challenges, the research community responded with waveform diversity; a relatively new field of study which aims reduce the cost of remote sensing while improving performance. Early work suggests that the frequency diverse array radar may be able to perform several remote sensing missions simultaneously without sacrificing performance. With few techniques available for modeling and characterizing the frequency diverse array, this research aims to specify, validate and characterize a waveform diverse signal model that can be used to model a variety of traditional and contemporary radar configurations, including frequency diverse array radars. To meet the aim of the research, a generalized radar array signal model is specified. A representative hardware system is built to generate the arbitrary radar signals, then the measured and simulated signals are compared to validate the model. Using the generalized model, expressions for the average transmit signal power, angular resolution, and the ambiguity function are also derived. The range, velocity and direction-of-arrival measurement accuracies for a set of signal configurations are evaluated to determine whether the configuration improves fundamental measurement accuracy

How Well Sensing Integrates with Communications in MmWave Wi-Fi?

The development of integrated sensing and communication (ISAC) systems has recently gained interest for its ability to offer a variety of services including resources sharing and new applications, for example, localization, tracking, and health care related. While the sensing capabilities are offered through many technologies, rending to their wide deployments and the high frequency spectrum they provide and high range resolution, its accessibility through the Wi-Fi networks IEEE 802.11ad and 802.11ay has been getting the interest of research and industry. Even though there is a dedicated standardization body, namely the 802.11bf task group, working on enhancing the Wi-Fi sensing performance, investigations are needed to evaluate the effectiveness of various sensing techniques. In this project, we, in addition to surveying related literature, we evaluate the sensing performance of the millimeter wave (mmWave) Wi-Fi systems by simulating a scenario of a human target using Matlab simulation tools. In this analysis, we processed channel estimation data using the short time Fourier transform (STFT). Furthermore, using a channel variation threshold method, we evaluated the performance while reducing feedback. Our findings indicate that using STFT window overlap can provide good tracking results, and that the reduction in feedback measurements using 0.05 and 0.1 threshold levels reduces feedback measurements by 48% and 77%, respectively, without significantly degrading performance.Comment: arXiv admin note: substantial text overlap with arXiv:2207.04859 by other author

Low-complexity hardware and algorithm for joint communication and sensing

Joint Communication and Sensing (JCAS) is foreseen as one very distinctive feature of the emerging 6G systems providing, in addition to fast end reliable communication, the ability to obtain an accurate perception of the physical environment. In this paper, we propose a JCAS algorithm that exploits a novel beamforming architecture, which features a combination of wideband analog and narrowband digital beamforming. This allows accurate estimation of Time of Arrival (ToA), exploiting the large bandwidth and Angle of Arrival (AoA), exploiting the high-rank digital beamforming. In our proposal, we separately estimate the ToA and AoA. The association between ToA and AoA is solved by acquiring multiple non-coherent frames and adding up the signal from each frame such that a specific component is combined coherently before the AoA estimation. Consequently, this removes the need to use 2D and 3D joint estimation methods, thus significantly lowering complexity. The resolution performance of the method is compared with that of 2D MUltiple SIgnal Classification (2D-MUSIC) algorithm, using a fully-digital wideband beamforming architecture. The results show that the proposed method can achieve performance similar to a fully-digital high-bandwidth system, while requiring a fraction of the total aggregate sampling rate and having much lower complexity.Comment: 13 pages, 9 figures. Submitted to IEEE Transactions on Wireless Communication

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Contribution à l’étude des Systèmes Multi-Antennes pour les Télécommunications et les Radars

Habilitation à diriger des recherchesEtude des Systèmes Multi-Antennes pour les Télécommunications et les RadarsCe mémoire résume un peu plus de 10 années de recherche, depuis ma thèse (soutenue en décembre 2002), jusqu’à aujourd’hui au département OSA (Ondes et Systèmes Associés) du Laboratoire XLIM à l’université de Limoges, où je suis maître de conférences depuis septembre 2005.Mes activités de recherche s’intégraient initialement dans le domaine de l’électromagnétisme et des antennes (antennes multifonctions intégrées, réseaux d’antennes pour la formation de faisceau ou pour des applications de goniométrie) avec une connotation plus poussée sur les antennes miniatures. Par la suite, ce dernier axe de recherche s’est naturellement orienté vers l’étude des systèmes multi-antennes (MIMO), permettant notamment de lutter contre les évanouissements du canal radio, qui représentait un challenge important en terme d’intégration au sein d’un terminal mobile par exemple. Cependant, pour être pleinement couvert, ce domaine d’études nécessite de prendre en considération, non seulement les antennes, mais aussi le canal de propagation multitrajets, la mise en forme et les traitements numériques des signaux associés au système multi-antennes. C’est dans ce sens qu’une nouvelle équipe de recherche (« réseaux sans fil ») a été créée au sein du département OSA d’XLIM, et dont je suis responsable depuis 2006. Les axes de recherche abordés concernent donc de manière générale le vaste domaine des systèmes de transmissions multi-antennes pour les communications et les radars, ainsi que le sondage de canal. Ils s’appuient sur une forte partie expérimentale par la mise en œuvre de bancs de mesures permettant la caractérisation active d’algorithmes liés aux traitements multi-antennes (codages MIMO, formation de faisceau, imagerie radar, …), en environnement maîtrisé (chambre réverbérante à brassage de mode, chambre anéchoïde multicapteurs) et en environnement réel