ICI-aware parameter estimation for MIMO-OFDM radar via APES spatial filtering

We propose a novel three-stage delay-Doppler-angle estimation algorithm for a MIMO-OFDM radar in the presence of inter-carrier interference (ICI). First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. In the final stage, delay and Doppler of each target can be recovered from target-specific channel estimates over time and frequency. Simulation results illustrate the superior performance of the proposed algorithm in high-mobility scenarios

A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM-MIMO Radar

We address the problem of a new joint Doppler frequency shift (DFS) and direction of arrival (DOA) estimation for colocated TDM-MIMO radar that is a novel technology applied to autocruise and safety driving system in recent years. The signal model of colocated TDM-MIMO radar with few transmitter or receiver channels is depicted and “time varying steering vector” model is proved. Inspired by sparse representations theory, we present a new processing scheme for joint DFS and DOA estimation based on the new input signal model of colocated TDM-MIMO radar. An ultracomplete redundancy dictionary for angle-frequency space is founded in order to complete sparse representations of the input signal. The SVD-SR algorithm which stands for joint estimation based on sparse representations using SVD decomposition with OMP algorithm and the improved M-FOCUSS algorithm which combines the classical M-FOCUSS with joint sparse recovery spectrum are applied to the new signal model’s calculation to solve the multiple measurement vectors (MMV) problem. The improved M-FOCUSS algorithm can work more robust than SVD-SR and JS-SR algorithms in the aspects of coherent signals resolution and estimation accuracy. Finally, simulation experiments have shown that the proposed algorithms and schemes are feasible and can be further applied to practical application

Active Backscattering Positioning System Using Innovative Harmonic Oscillator Tags for Future Internet of Things: Theory and Experiments

RÉSUMÉ D'ici 2020, l'Internet des objets (IoT) permettra probablement de créer 25 milliards d'objets connectés, 44 ZB de données et de débloquer 11 000 milliards de dollars d’opportunités commerciales. Par conséquent, ce sujet a suscité d’énormes intérêts de recherche dans le monde académique entier. L'une des technologies clés pour l'IoT concerne le positionnement physique intérieur précis. Le principal objectif dans ce domaine est le développement d'un système de positionnement intérieur avec une grande précision, une haute résolution, un fonctionnement à plusieurs cibles, un faible coût, un faible encombrement et une faible consommation d'énergie. Le système de positionnement intérieur conventionnel basé sur les technologies de Wi-Fi ou d'identification par radiofréquence (RFID) ne peut répondre à ces exigences. Principalement parce que leur appareil et leur signal ne sont pas conçus spécialement pour atteindre les objectifs visés. Les chercheurs ont découvert qu'en mettant en oeuvre de différents types de modulation sur les étiquettes, le radar à onde continue (CW) et ses dérivés deviennent des solutions prometteuses. Les activités de recherche présentées dans cette thèse sont menées dans le but de développer des systèmes de positionnement en intérieur bidimensionnel (2-D) à plusieurs cibles basées sur des étiquettes actives à rétrodiffusion harmonique avec une technique à onde continue modulée en fréquence (FMCW). Les contributions de cette thèse peuvent être résumées comme suit: Tout d'abord, la conception d'un circuit actif harmonique, plus spécifiquement une classe d'oscillateurs harmoniques innovants utilisée comme composant central des étiquettes actives dans notre système, implique une méthodologie de conception de signal de grande taille et des installations de caractérisation. L’analyseur de réseau à grand signal (LSNA) est un instrument émergent basé sur les fondements théoriques du cadre de distorsion polyharmonique (PHD). Bien qu'ils soient disponibles dans le commerce depuis 2008, des organismes de normalisation et de recherche tels que l’Institut national des normes et de la technologie (NIST) des États-Unis travaillent toujours à la mise au point d'un standard largement reconnu permettant d'évaluer et de comparer leurs performances. Dans ce travail, un artefact de génération multi-harmonique pour la vérification LSNA est développé. C'est un dispositif actif capable de générer les 5 premières harmoniques d'un signal d'entrée avec une réponse ultra-stables en amplitude et en phase, quelle que soit la variation de l'impédance de la charge.----------ABSTRACT By 2020, the internet of things (IoT) will probably enable 25 billion connected objects, create 44 ZB data and unlock 11 trillion US dollar business opportunities. Therefore, this topic has been attracting tremendous research interests in the entire academic world. One of the key enabling technologies for IoT is concerned with accurate indoor physical positioning. The development of such an indoor positioning system with high accuracy, high resolution, multitarget operation, low cost, small footprint, and low power consumption is the major objective in this area. The conventional indoor positioning system based on WiFi or radiofrequency identification (RFID) technology cannot fulfill these requirements mainly because their device and signal are not purposely designed for achieving the targeted goals. Researchers have found that by implementing different types of modulation on the tags, continuous-wave (CW) radar and its derivatives become promising solutions. The research activities presented in this Ph.D. thesis are carried out towards the goal of developing multitarget two-dimensional (2-D) indoor positioning systems based on harmonic backscattering active tags together with a frequency-modulated continuous-wave (FMCW) technique. Research contributions of this thesis can be summarized as follows: First of all, the design of a harmonic active circuit, more specifically, a class of innovative harmonic oscillators used as the core component of active tags in our system, involves a large signal design methodology and characterization facilities. The large signal network analyzer (LSNA) is an emerging instrument based on the theoretical foundation for the Poly-Harmonic Distortion (PHD) framework. Although they have been commercially available since 2008, standard and research organizations such as the National Institute of Standards and Technology (NIST) of the US are still working towards a widely-recognized standard to evaluate and cross-reference their performances. In this work, a multi-harmonic generation artifact for LSNA verification is developed. It is an active device that can generate the first 5 harmonics of an input signal with ultra-stable amplitude and phase response regardless of the load impedance variation

A Miniaturized Low Power Millimeter Wave RFID tag for Spatial Localization and Detection

The work outlined in this thesis investigates the applicability of millimeter wave semi-passive backscatter nodes for use in the spatial localization and tracking of objects at short distances. A miniaturized semi-passive ultra-low power energy autonomous RFID tag operating in the 24 GHz ISM frequency band is developed. The spatial localization of the RFID tags is enabled by the use of a Frequency Modulated Continuous Wave (FMCW) Radar as the reader. The radar is used to resolve the modulated backscatter returned by the RFID tags when interrogated by a Continuous Wave from the reader.M.S

Software defined radar system

Software defined radar concept and simulation -- Signal processing methods of synthetic software defined radar -- Mixer-based synthetic software defined radar -- Six-port-based syunthetic software defined radar -- Performance study of synthetic software defined radar

Signal Processing for Large Arrays: Convolutional Beamspace, Hybrid Analog and Digital Processing, and Distributed Algorithms

The estimation of the directions of arrival (DOAs) of incoming waves for a passive antenna array has long been an important topic in array signal processing. Meanwhile, the estimation of the MIMO channel between a transmit antenna array and a receive antenna array is a key problem in wireless communications. In many recent works on these array processing tasks, people consider millimeter waves (mmWaves) due to their potential to offer more bandwidth than the already highly occupied lower-frequency bands. However, new challenges like strong path loss at the high frequencies of mmWaves arise. To compensate for the path loss, large arrays, or massive MIMO, are used to get large beamforming gain. It is practical due to the small sizes of mmWave antennas. When large arrays are used, it is important to develop efficient estimation algorithms with low computational and hardware complexity. The main contribution of this thesis is to propose low-complexity DOA and channel estimation methods that are especially effective for large arrays. To achieve low complexity, three main aspects are explored: beamspace methods, hybrid analog and digital processing, and distributed algorithms. First, a new beamspace method, convolutional beamspace (CBS), is proposed for DOA estimation based on passive arrays. In CBS, the array output is spatially filtered, followed by uniform decimation (downsampling) to achieve dimensionality reduction. No DOA ambiguity occurs since the filter output is represented only by the passband sources. CBS enjoys the advantages of classical beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. Moreover, unlike classical beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for uniform linear arrays without additional preparation since the Vandermonde structure is preserved under the CBS transformation. The method produces more accurate DOA estimates than classical beamspace, and for correlated sources, better estimates than element-space. The idea of hybrid analog and digital processing is then incorporated into CBS, leading to hybrid CBS for DOA estimation. In hybrid processing, an analog combiner is used to reduce the number of radio frequency (RF) chains and thus hardware complexity. Also for lowering hardware cost, the analog combiner is designed as a phase shifter network with unit-modulus entries. It is shown that any general (arbitrary coefficient) CBS filter can be implemented despite the unit-modulus constraints. Moreover, a new scheme of CBS is proposed based on nonuniform decimation and difference coarray method. This allows us to identify more sources than RF chains. The retained samples correspond to the sensor locations of a virtual sparse array, dilated by an integer factor, which results in larger coarray aperture and thus better estimation performance. Besides, with the use of random or deterministic filter delays that vary with snapshots, a new method is proposed to decorrelate sources for the coarray method to work. Next, a 2-dimensional (2-D) hybrid CBS method is developed for mmWave MIMO channel estimation. Since mmWave channel estimation problems can be formulated as 2-D direction-of-departure (DOD) and DOA estimation, benefits of CBS such as low complexity are applicable here. The receiver operation is again filtering followed by decimation. A key novelty is the use of a proper counterpart of CBS at the transmitter—expansion (upsampling) followed by filtering—to reduce RF chains. The expansion and decimation can be either uniform or nonuniform. The nonuniform scheme is used with 2-D coarray method and requires fewer RF chains to achieve the same estimation performance as the uniform scheme. A method based on the introduction of filter delays is also proposed to decorrelate path gains, which is crucial to the success of coarray methods. It is shown that given fixed pilot overhead, 2-D hybrid CBS can yield more accurate channel estimates than previous methods. Finally, distributed (decentralized) algorithms for array signal processing are studied. With the potential of reducing computation and communication complexity, distributed estimation of covariance, and distributed principal component analysis have been introduced and studied in the signal processing community in recent years. Applications in array processing have been also indicated in some detail. In this thesis, distributed algorithms are further developed for several well-known methods for DOA estimation and beamforming. New distributed algorithms are proposed for DOA estimation methods like root-MUSIC, total least squares ESPRIT, and FOCUSS. Other contributions include distributed design of the Capon beamformer from data, distributed implementation of the spatial smoothing method for coherent sources, and distributed realization of CBS. The proposed algorithms are fully distributed since average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a finite-time version of AC which converges to the exact solution in a finite number of iterations. This enables the proposed distributed algorithms to achieve the same performance as the centralized counterparts, as demonstrated by simulations.</p

Practical investigation of Butler matrix application for beamforming with circular antenna arrays

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Sparse Array Signal Processing: New Array Geometries, Parameter Estimation, and Theoretical Analysis

Array signal processing focuses on an array of sensors receiving the incoming waveforms in the environment, from which source information, such as directions of arrival (DOA), signal power, amplitude, polarization, and velocity, can be estimated. This topic finds ubiquitous applications in radar, astronomy, tomography, imaging, and communications. In these applications, sparse arrays have recently attracted considerable attention, since they are capable of resolving O(N2) uncorrelated source directions with N physical sensors. This is unlike the uniform linear arrays (ULA), which identify at most N-1 uncorrelated sources with N sensors. These sparse arrays include minimum redundancy arrays (MRA), nested arrays, and coprime arrays. All these arrays have an O(N2)-long central ULA segment in the difference coarray, which is defined as the set of differences between sensor locations. This O(N2) property makes it possible to resolve O(N2) uncorrelated sources, using only N physical sensors. The main contribution of this thesis is to provide a new direction for array geometry and performance analysis of sparse arrays in the presence of nonidealities. The first part of this thesis focuses on designing novel array geometries that are robust to effects of mutual coupling. It is known that, mutual coupling between sensors has an adverse effect on the estimation of DOA. While there are methods to counteract this through appropriate modeling and calibration, they are usually computationally expensive, and sensitive to model mismatch. On the other hand, sparse arrays, such as MRA, nested arrays, and coprime arrays, have reduced mutual coupling compared to ULA, but all of these have their own disadvantages. This thesis introduces a new array called the super nested array, which has many of the good properties of the nested array, and at the same time achieves reduced mutual coupling. Many theoretical properties are proved and simulations are included to demonstrate the superior performance of super nested arrays in the presence of mutual coupling. Two-dimensional planar sparse arrays with large difference coarrays have also been known for a long time. These include billboard arrays, open box arrays (OBA), and 2D nested arrays. However, all of them have considerable mutual coupling. This thesis proposes new planar sparse arrays with the same large difference coarrays as the OBA, but with reduced mutual coupling. The new arrays include half open box arrays (HOBA), half open box arrays with two layers (HOBA-2), and hourglass arrays. Among these, simulations show that hourglass arrays have the best estimation performance in presence of mutual coupling. The second part of this thesis analyzes the performance of sparse arrays from a theoretical perspective. We first study the Cramér-Rao bound (CRB) for sparse arrays, which poses a lower bound on the variances of unbiased DOA estimators. While there exist landmark papers on the study of the CRB in the context of array processing, the closed-form expressions available in the literature are not applicable in the context of sparse arrays for which the number of identifiable sources exceeds the number of sensors. This thesis derives a new expression for the CRB to fill this gap. Based on the proposed CRB expression, it is possible to prove the previously known experimental observation that, when there are more sources than sensors, the CRB stagnates to a constant value as the SNR tends to infinity. It is also possible to precisely specify the relation between the number of sensors and the number of uncorrelated sources such that these sources could be resolved. Recently, it has been shown that correlation subspaces, which reveal the structure of the covariance matrix, help to improve some existing DOA estimators. However, the bases, the dimension, and other theoretical properties of correlation subspaces remain to be investigated. This thesis proposes generalized correlation subspaces in one and multiple dimensions. This leads to new insights into correlation subspaces and DOA estimation with prior knowledge. First, it is shown that the bases and the dimension of correlation subspaces are fundamentally related to difference coarrays, which were previously found to be important in the study of sparse arrays. Furthermore, generalized correlation subspaces can handle certain forms of prior knowledge about source directions. These results allow one to derive a broad class of DOA estimators with improved performance. It is empirically known that the coarray structure is susceptible to sensor failures, and the reliability of sparse arrays remains a significant but challenging topic for investigation. This thesis advances a general theory for quantifying such robustness, by studying the effect of sensor failure on the difference coarray. We first present the (k-)essentialness property, which characterizes the combinations of the faulty sensors that shrink the difference coarray. Based on this, the notion of (k-)fragility is proposed to quantify the reliability of sparse arrays with faulty sensors, along with comprehensive studies of their properties. These novel concepts provide quite a few insights into the interplay between the array geometry and its robustness. For instance, for the same number of sensors, it can be proved that ULA is more robust than the coprime array, and the coprime array is more robust than the nested array. Rigorous development of these ideas leads to expressions for the probability of coarray failure, as a function of the probability of sensor failure. The thesis concludes with some remarks on future directions and open problems.</p