Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling

Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various applications, where perturbations appear both in the data vector as well as in the regression matrix. However, existing TLS approaches do not account for sparsity possibly present in the unknown vector of regression coefficients. On the other hand, sparsity is the key attribute exploited by modern compressive sampling and variable selection approaches to linear regression, which include noise in the data, but do not account for perturbations in the regression matrix. The present paper fills this gap by formulating and solving TLS optimization problems under sparsity constraints. Near-optimum and reduced-complexity suboptimum sparse (S-) TLS algorithms are developed to address the perturbed compressive sampling (and the related dictionary learning) challenge, when there is a mismatch between the true and adopted bases over which the unknown vector is sparse. The novel S-TLS schemes also allow for perturbations in the regression matrix of the least-absolute selection and shrinkage selection operator (Lasso), and endow TLS approaches with ability to cope with sparse, under-determined "errors-in-variables" models. Interesting generalizations can further exploit prior knowledge on the perturbations to obtain novel weighted and structured S-TLS solvers. Analysis and simulations demonstrate the practical impact of S-TLS in calibrating the mismatch effects of contemporary grid-based approaches to cognitive radio sensing, and robust direction-of-arrival estimation using antenna arrays.Comment: 30 pages, 10 figures, submitted to IEEE Transactions on Signal Processin

Adaptive Signal Processing Techniques and Realistic Propagation Modeling for Multiantenna Vital Sign Estimation

Tämän työn keskeisimpänä tavoitteena on ihmisen elintoimintojen tarkkailu ja estimointi käyttäen radiotaajuisia mittauksia ja adaptiivisia signaalinkäsittelymenetelmiä monen vastaanottimen kantoaaltotutkalla. Työssä esitellään erilaisia adaptiivisia menetelmiä, joiden avulla hengityksen ja sydämen värähtelyn aiheuttamaa micro-Doppler vaihemodulaatiota sisältävät eri vastaanottimien signaalit voidaan yhdistää. Työssä johdetaan lisäksi realistinen malli radiosignaalien etenemiselle ja heijastushäviöille, jota käytettiin moniantennitutkan simuloinnissa esiteltyjen menetelmien vertailemiseksi. Saatujen tulosten perusteella voidaan osoittaa, että adaptiiviset menetelmät parantavat langattoman elintoimintojen estimoinnin luotettavuutta, ja mahdollistavat monitoroinnin myös pienillä signaali-kohinasuhteen arvoilla.This thesis addresses the problem of vital sign estimation through the use of adaptive signal enhancement techniques with multiantenna continuous wave radar. The use of different adaptive processing techniques is proposed in a novel approach to combine signals from multiple receivers carrying the information of the cardiopulmonary micro-Doppler effect caused by breathing and heartbeat. The results are based on extensive simulations using a realistic signal propagation model derived in the thesis. It is shown that these techniques provide a significant increase in vital sign rate estimation accuracy, and enable monitoring at lower SNR conditions

Eigenvector-based multidimensional frequency estimation : identifiability, performance, and applications.

Multidimensional frequency estimation is a classic signal processing problem that has versatile applications in sensor array processing and wireless communications. Eigenvalue-based two-dimensional (2-D) and N -dimensional ( N -D) frequency estimation algorithms have been well documented, however, these algorithms suffer from limited identifiability and demanding computations. This dissertation develops a framework on eigenvector-based N -D frequency estimation, which contains several novel algorithms that estimate a structural matrix from eigenvectors and then resolve the N -D frequencies by dividing the elements of the structural matrix. Compared to the existing eigenvalue-based algorithms, these eigenvector-based algorithms can achieve automatic pairing without an extra frequency pairing step, and tins the computational complexity is reduced. The identifiability, performance, and complexity of these algorithms are also systematically studied. Based on this study, the most relaxed identifiability condition for the N -D frequency estimation problem is given and an effective approach using optimized weighting factors to improve the performance of frequency estimation is developed. These results are applied in wireless communication for time-varying multipath channel estimation and prediction, as well as for joint 2-D Direction-of-arrival (DOA) tracking of multiple moving targets

Experimental analysis of multidimensional radio channels

In this thesis new systems for radio channel measurements including space and polarization dimensions are developed for studying the radio propagation in wideband mobile communication systems. Multidimensional channel characterization is required for building channel models for new systems capable of exploiting the spatial nature of the channel. It also gives insight into the dominant propagation mechanisms in complex radio environments, where their prediction is difficult, such as urban and indoor environments. The measurement systems are based on the HUT/IDC wideband radio channel sounder, which was extended to enable real-time multiple output channel measurements at practical mobile speeds at frequencies up to 18 GHz. Two dual-polarized antenna arrays were constructed for 2 GHz, having suitable properties for characterizing the 3-D spatial radio channel at both ends of a mobile communication link. These implementations and their performance analysis are presented. The usefulness of the developed measurement systems is demonstrated by performing channel measurements at 2 GHz and analyzing the experimental data. Spatial channels of both the mobile and base stations are analyzed, as well as the double-directional channel that fully characterizes the propagation between two antennas. It is shown through sample results that spatial domain channel measurements can be used to gain knowledge on the dominant propagation mechanisms or verify the current assumptions. Also new statistical information about scatterer distribution at the mobile station in urban environment is presented based on extensive real-time measurements. The developed techniques and collected experimental data form a good basis for further comparison with existing deterministic propagation models and development of new spatial channel models.reviewe

Signal processing architectures for automotive high-resolution MIMO radar systems

To date, the digital signal processing for an automotive radar sensor has been handled in an efficient way by general purpose signal processors and microcontrollers. However, increasing resolution requirements for automated driving on the one hand, as well as rapidly growing numbers of manufactured sensors on the other hand, can provoke a paradigm change in the near future. The design and development of highly specialized hardware accelerators could become a viable option - at least for the most demanding processing steps with data rates of several gigabits per second. In this work, application-specific signal processing architectures for future high-resolution multiple-input and multiple-output (MIMO) radar sensors are designed, implemented, investigated and optimized. A focus is set on real-time performance such that even sophisticated algorithms can be computed sufficiently fast. The full processing chain from the received baseband signals to a list of detections is considered, comprising three major steps: Spectrum analysis, target detection and direction of arrival estimation. The developed architectures are further implemented on a field-programmable gate array (FPGA) and important measurements like resource consumption, power dissipation or data throughput are evaluated and compared with other examples from literature. A substantial dataset, based on more than 3600 different parametrizations and variants, has been established with the help of a model-based design space exploration and is provided as part of this work. Finally, an experimental radar sensor has been built and is used under real-world conditions to verify the effectiveness of the proposed signal processing architectures.Bisher wurde die digitale Signalverarbeitung für automobile Radarsensoren auf eine effiziente Art und Weise von universell verwendbaren Mikroprozessoren bewältigt. Jedoch können steigende Anforderungen an das Auflösungsvermögen für hochautomatisiertes Fahren einerseits, sowie schnell wachsende Stückzahlen produzierter Sensoren andererseits, einen Paradigmenwechsel in naher Zukunft bewirken. Die Entwicklung von hochgradig spezialisierten Hardwarebeschleunigern könnte sich als eine praktikable Alternative etablieren - zumindest für die anspruchsvollsten Rechenschritte mit Datenraten von mehreren Gigabits pro Sekunde. In dieser Arbeit werden anwendungsspezifische Signalverarbeitungsarchitekturen für zukünftige, hochauflösende, MIMO Radarsensoren entworfen, realisiert, untersucht und optimiert. Der Fokus liegt dabei stets auf der Echtzeitfähigkeit, sodass selbst anspruchsvolle Algorithmen in einer ausreichend kurzen Zeit berechnet werden können. Die komplette Signalverarbeitungskette, beginnend von den empfangenen Signalen im Basisband bis hin zu einer Liste von Detektion, wird in dieser Arbeit behandelt. Die Kette gliedert sich im Wesentlichen in drei größere Teilschritte: Spektralanalyse, Zieldetektion und Winkelschätzung. Des Weiteren werden die entwickelten Architekturen auf einem FPGA implementiert und wichtige Kennzahlen wie Ressourcenverbrauch, Stromverbrauch oder Datendurchsatz ausgewertet und mit anderen Beispielen aus der Literatur verglichen. Ein umfangreicher Datensatz, welcher mehr als 3600 verschiedene Parametrisierungen und Varianten beinhaltet, wurde mit Hilfe einer modellbasierten Entwurfsraumexploration erstellt und ist in dieser Arbeit enthalten. Schließlich wurde ein experimenteller Radarsensor aufgebaut und dazu benutzt, die entworfenen Signalverarbeitungsarchitekturen unter realen Umgebungsbedingungen zu verifizieren

Interferometric Synthetic Aperture Sonar Signal Processing for Autonomous Underwater Vehicles Operating Shallow Water

The goal of the research was to develop best practices for image signal processing method for InSAS systems for bathymetric height determination. Improvements over existing techniques comes from the fusion of Chirp-Scaling a phase preserving beamforming techniques to form a SAS image, an interferometric Vernier method to unwrap the phase; and confirming the direction of arrival with the MUltiple SIgnal Channel (MUSIC) estimation technique. The fusion of Chirp-Scaling, Vernier, and MUSIC lead to the stability in the bathymetric height measurement, and improvements in resolution. This method is computationally faster, and used less memory then existing techniques