1,895 research outputs found
Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications
No full textIn today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop a data reduction tool called sparse sensing, which consists of a deterministic and structured sensing function (guided by a sparse vector) that is optimally designed to achieve a desired inference performance with the reduced number of data samples. The first part of this thesis is dedicated to the development of sparse sensing mechanisms and convex programs to efficiently design sparse sensing functions. We design sparse sensing functions under the assumption that the data is not yet available and the model information is perfectly known. Sparse sensing offers a number of advantages over compressed sensing (a state-of-the-art data reduction method for sparse signal recovery). One of the major differences is that in sparse sensing the underlying signals need not be sparse. This allows us to consider general signal processing tasks (not just sparse signal recovery) under the proposed sparse sensing framework. Specifically, we focus on fundamental statistical inference tasks, like estimation, filtering, and detection. In essence, we present topics that transform classical (e.g., random or uniform) sensing methods to low-cost data acquisition mechanisms tailored for specific inference tasks. The developed framework can be applied to sensor selection, sensor placement, or sensor scheduling, for example. In the second part of this thesis, we focus on some applications related to distributed sampling using sensor networks. Sensor networks can be used as a spatial sampling device, that is, to faithfully represent distributed signals (e.g., a spatially varying phenomenon such as a temperature field). On top of that, the distributed signals can exist in space and time, where the temporal sampling is achieved using analog-to-digital converters, for example. Each sensor has an independent sample clock, and its stability essentially determines the alignment of the temporal sampling grid across the sensors. Due to imperfections in the oscillator, the sample clocks drift from each other, resulting in the misalignment of the temporal sampling grids. To overcome this issue, we devise a mechanism to distribute the sample clock wirelessly. Specifically, we perform wireless clock synchronization based on the time-of-flight measurements of broadcast messages. In addition, clock synchronization also plays a central role in other time-based sensor network applications such as localization. Localization is increasingly gaining popularity in many applications, especially for monitoring environments beyond human reach, e.g., using robots or drones with several sensor units mounted on it. Consequently we now have to localize more than one sensor or even localize the whole sensing platform. Therefore, we extend the classical localization paradigm to localize a (rigid) sensing platform by exploiting the knowledge of the sensor placement on the platform. In particular, we develop algorithms for rigid body localization, i.e., for estimating the position and orientation of the rigid platform using distance measurements. Given the central role of sensing and sensor networks, the results presented in this thesis impacts a wide range of applications.Microelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc
Compressive Sensing for Near-field Source Localization
No full textNear-field source localization is an important aspect in many diverse areas such as acoustics, seismology, to list a few. The planar wave assumption frequently used in far-field source localization is no longer valid when the sources are in the near field. Near-field sources can be localized by solving a joint direction-of-arrival and range estimation problem. The original near-field source localization problem is a multi-dimensional non-linear optimization problem which is computationally intractable. In this thesis we study address two important questions related to near-field source localization: (i) Sparse reconstruction techniques for joint DOA and range estimation using a grid-based model. (ii) Matching the sampling grid for off-grid sources. In the first part of this thesis, we use a grid-based model and by further leveraging the sparsity, we can solve the aforementioned problem efficiently using any of the off-the-shelf l1_-norm optimization solvers. When multiple snapshots are available, we can also exploit the cross-correlations among the symmetric sensors of the array and further reduce the complexity by solving two sparse reconstruction problems of lower dimensions instead of a single sparse reconstruction problem of a higher dimension. In the second part of this thesis, we account scenarios where the true source locations are not on the grid resulting in a grid mismatch. Using the first-order Taylor approximation, we model the grid mismatch as a perturbation around the sampling grid. Based on the grid mismatch model, we propose a bounded sparse and bounded joint sparse recovery algorithms to localize near-field sources.Electrical EngineeringTelecommunicationsElectrical Engineering, Mathematics and Computer Scienc
An Extensible Toolkit For Real-Time High-Performance Wideband Spectrum Sensing
No full textThis document describes the design process of a software toolkit to perform high-performance wideband spectrum sensing. A prominent application of this is Cognitive Radio, a technique that aims to make more efficient use of the available radio spectrum. An extensive theoretical analysis will be performed. Various non-uniform sampling techniques will be discussed, such as coprime and circular sparse sampling. An algorithm to reconstruct the PSD of sub-Nyquist sampled signals will be developed and a detection algorithm which uses this PSD will be proposed. This analysis will be utilised to implement an extensible software toolkit written in Python. The software architecture and various design patterns that were utilised to structure the toolkit will be described and its quality and performance will be analysed. The hardware used for data acquisition, a USRP N210, will be introduced. The work will be concluded with a conclusion and its discussion.Electrical EngineeringCircuits and SystemsElectrical Engineering, Mathematics and Computer Scienc
Sparse sensing for composite matched subspace detection
No full textIn this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub-spaces, we show how deterministic samplers can be designed under a Neyman-Pearson-like setting when the generalized likelihood ratio test is used. For a less stringent case than the worst case design, we introduce a submodular cost that obtains comparable results with its convex counterpart, while having a linear time heuristic for its near optimal maximization.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Subset Selection for Kernel-Based Signal Reconstruction
No full textIn this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the covariance matrix of the kernel regressors to establish metrics for the subset selection problem. Despite the NP-hard nature of the problem, we introduce efficient algorithms for finding approximate solutions for the proposed metrics. Finally, numerical experiments demonstrate the applicability of the proposed strategies.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Submodular Sparse Sensing for Gaussian Detection with Correlated Observations
No full textDetection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available sensors, referred to as sparse sensing, that meets both the budget and performance requirements is highly desirable. Unfortunately, the subset selection problem for detection under dependent observations is combinatorial in nature and suboptimal subset selection algorithms must be employed. In this work, different from the widely used convex relaxation of the problem, we leverage submodularity, the diminishing returns property, to provide practical near-optimal algorithms suitable for large-scale subset selection. This is achieved by means of low-complexity greedy algorithms, which incur a reduced computational complexity compared to their convex counterparts.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Wideband spectrum sensing techniques for wireless sensors
No full textThe limited availability of radio frequency spectrum demands for more efficient ways to utilize it in future wireless networks. Spectrum sharing radios are an interesting solution to the spectral scarcity problem, where the available resources are adaptively used across time and frequency without affecting other user's transmissions. In this context, sensing the spectrum for its occupancy is needed to increase the awareness among technologies that share the same spectrum. In a typical wireless sensor network, each node senses and transmits data constrained by a very low power budget. At the same time, they should be capable of finding a free frequency channel with minimal latency. A solution to this problem is to make radios capable of sensing multiple frequency bands, in the order of a few hundred MHz, all at once. The technical challenge lies in the design of low-complexity wideband spectrum sensing techniques that increase context awareness at the wireless node. In this thesis, we address this problem with two approaches. The first approach is based on Compressed Sampling (CS) theory, where a new perspective is taken, different to conventional methods that estimate the spectrum and perform detection on the reconstructed spectrum. Instead a direct detection is performed on the sub-Nyquist rate sampled wideband signal. In the second part of this thesis, an alternative approach to reduce the power at an architectural level is proposed, by avoiding the Nyquist rate wideband Analog-to-Digital Converter (ADC) and pushing the conventional digital processing to the analog domain.Telecommunication, Circuits and SystemsMicroelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc
SPARSEST NETWORK SUPPORT ESTIMATION: A SUBMODULAR APPROACH
No full textIn this work, we address the problem of identifying the underlying network structure of data. Different from other approaches, which are mainly based on convex relaxations of an integer problem, here we take a distinct route relying on algebraic properties of a matrix representation of the network. By describing what we call possible ambiguities on the network topology, we proceed to employ sub-modular analysis techniques for retrieving the network support, i.e., network edges. To achieve this we only make use of the network modes derived from the data. Numerical examples showcase the effectiveness of the proposed algorithm in recovering the support of sparse networks.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Graph Sampling with and Without Input Priors
No full textIn this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary graphs or arbitrary signals that may be represented using graphs, where we extend concepts from generalized sampling theory to the graph setting. To recover such signals from a given set of samples, we develop algorithms that incorporate prior knowledge on the original signal when available such as smoothness or subspace priors related to the underlying graph. For reconstructing arbitrary signals, we constrain the reconstruction to the graph, and provide a consistent reconstruction method, in which both the reconstructed signal and the input yield exactly the same measurements. Given a set of graph frequency domain samples, the sampling and interpolation operations may be efficiently implemented using linear shift-invariant graph filters.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Distributed Analytical Graph Identification
No full textAn analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
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