Download or read book Statistical Strategies for Efficient Signal Detection and Parameter Estimation in Wireless Sensor Networks written by Eric Ayeh and published by . This book was released on 2013 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Distributed Parameter Estimation in Wireless Sensor Networks written by Jongmin Lee (Electrical engineer) and published by . This book was released on 2017 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities. Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate to each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense. Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis. Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.

Download or read book Principles of Signal Detection and Parameter Estimation written by Bernard C. Levy and published by Springer Science & Business Media. This book was released on 2008-12-16 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive and current understanding of signal detection and estimation, including problems and solutions for each chapter. Signal detection plays an important role in fields such as radar, sonar, digital communications, image processing, and failure detection. The book explores both Gaussian detection and detection of Markov chains, presenting a unified treatment of coding and modulation topics. Addresses asymptotic of tests with the theory of large deviations, and robust detection. This text is appropriate for students of Electrical Engineering in graduate courses in Signal Detection and Estimation.

Download or read book Optimal Combining and Detection written by Jinho Choi and published by Cambridge University Press. This book was released on 2010-01-28 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: With signal combining and detection methods now representing a key application of signal processing in communication systems, this book provides a range of key techniques for receiver design when multiple received signals are available. Various optimal and suboptimal signal combining and detection techniques are explained in the context of multiple-input multiple-output (MIMO) systems, including successive interference cancellation (SIC) based detection and lattice reduction (LR) aided detection. The techniques are then analyzed using performance analysis tools. The fundamentals of statistical signal processing are also covered, with two chapters dedicated to important background material. With a carefully balanced blend of theoretical elements and applications, this book is ideal for both graduate students and practising engineers in wireless communications.

Download or read book System Design in Wireless Sensor Networks for Parameter Estimation and Dynamic Event Region Detection written by Tao Wu and published by . This book was released on 2013 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, the practical system design issues are studied for statistical inference in wireless sensor networks (WSNs). First, the problem of distributed estimation of an unknown parameter corrupted by noise is studied. Imperfect data transmission between local sensors and a fusion center is considered and modeled as a Rayleigh fading channel. The conventional maximum likelihood estimation usually involves high computational complexity and is not suitable for resource-constrained WSNs. Efficient estimators are designed for different receiver models and their efficiency is shown both theoretically and through experiments. The distributed parameter estimation performance also depends on the selection of local quantization thresholds. Therefore, different threshold schemes are investigated under the minimax criterion. Two quantizer structures (sinusoid function and raised cosine function) are proposed. Simulation results show that the simple sinusoid structure outperforms the intuitive uniform structure and the raised cosine structure achieves near optimal performance. The problem of dynamic event region detection in WSNs is studied next. To provide detection results at each time step, a distributed event region tracking algorithm is proposed. The system dynamics (modeled by a dynamic Markov random field) and information collected from neighbors are used to predict the underlying hypothesis at each sensor node and its local observation is used for update. The performance of the proposed algorithm is analyzed both theoretically and through simulations. Detecting and reconstructing critical dynamic event regions at a control center is an important application of bandwidth-limited WSNs. This problem is studied with emphasis on adaptive bandwidth allocation for sensor data transmission. To meet the stringent bandwidth and energy constraints in WSNs, only a few selected sensors are allowed to transmit compressed data to a control center. To reconstruct and track the field state map at each time step, a processing framework including sensor selection, local and central processing is proposed. Adaptive bandwidth allocation is obtained by solving a conditional entropy based optimization problem. The overall communication costs in terms of bandwidth and energy consumption of the proposed framework are evaluated by considering all possible overheads in a practical communication protocol.

Download or read book Wireless Sensor Networks written by Ananthram Swami and published by John Wiley & Sons. This book was released on 2007-11-12 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wireless sensor network (WSN) uses a number of autonomous devices to cooperatively monitor physical or environmental conditions via a wireless network. Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection. Technological advancements, coupled with lowering costs, suggest that wireless sensor networks will have a significant impact on 21st century life. The design of wireless sensor networks requires consideration for several disciplines such as distributed signal processing, communications and cross-layer design. Wireless Sensor Networks: Signal Processing and Communications focuses on the theoretical aspects of wireless sensor networks and offers readers signal processing and communication perspectives on the design of large-scale networks. It explains state-of-the-art design theories and techniques to readers and places emphasis on the fundamental properties of large-scale sensor networks. Wireless Sensor Networks: Signal Processing and Communications : Approaches WSNs from a new angle – distributed signal processing, communication algorithms and novel cross-layer design paradigms. Applies ideas and illustrations from classical theory to an emerging field of WSN applications. Presents important analytical tools for use in the design of application-specific WSNs. Wireless Sensor Networks will be of use to signal processing and communications researchers and practitioners in applying classical theory to network design. It identifies research directions for senior undergraduate and graduate students and offers a rich bibliography for further reading and investigation.

Download or read book Detection Algorithms for Wireless Communications written by Gianluigi Ferrari and published by John Wiley & Sons. This book was released on 2004-10-08 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a unified approach to detection for stochastic channels, with particular attention to wireless channels, this book illustrates how the three main criteria of sequence detection, symbol detection and graph-based detection, can all be described within a general framework.

Download or read book Principles of Signal Detection and Parameter Estimation written by Bernard C. Levy and published by Springer. This book was released on 2008-11-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive and current understanding of signal detection and estimation, including problems and solutions for each chapter. Signal detection plays an important role in fields such as radar, sonar, digital communications, image processing, and failure detection. The book explores both Gaussian detection and detection of Markov chains, presenting a unified treatment of coding and modulation topics. Addresses asymptotic of tests with the theory of large deviations, and robust detection. This text is appropriate for students of Electrical Engineering in graduate courses in Signal Detection and Estimation.

Download or read book Data Fusion in Wireless Sensor Networks written by Domenico Ciuonzo and published by Control, Robotics and Sensors. This book was released on 2019-05-03 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the advanced tools required to design state-of-the-art inference algorithms for inference in wireless sensor networks. Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in wireless sensor networks.

Download or read book Statistical Signal Processing in Sensor Networks written by Marco Guerriero and published by . This book was released on 2009 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Signal Processing written by Louis L. Scharf and published by Prentice Hall. This book was released on 1991 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements. This book presents the fundamental ideas in statistical signal processing along four distinct lines: mathematical and statistical preliminaries; decision theory; estimation theory; and time series analysis.

Download or read book Localization Algorithms and Strategies for Wireless Sensor Networks Monitoring and Surveillance Techniques for Target Tracking written by Mao, Guoqiang and published by IGI Global. This book was released on 2009-05-31 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.

Download or read book Parameter Estimation and Signal Detection Over Wireless Channels written by Ping Gao and published by . This book was released on 2006 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Theory of Signal Detection written by Carl W. Helstrom and published by Pergamon. This book was released on 1968 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Signals and filters; Noise; Hypothesis testing; Detection of a known signal; Detection of signals of unknown phase; Digital communications; Detection by multiple observations; The estimation of signal parameters; Detection of signals with unknown parameters; Signal resolution; Stochastic signals.

Download or read book Distributed Estimation in Sensor Networks with Modeling Uncertainty written by Qing Zhou and published by . This book was released on 2013 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major issue in distributed wireless sensor networks (WSNs) is the design of efficient distributed algorithms for network-wide dissemination of information acquired by individual sensors, where each sensor, by itself, is unable to access enough data for reliable decision making. Without a centralized fusion center, network-wide reliable inferencing can be accomplished by recovering meaningful global statistics at each sensor through iterative inter-sensor message passing. In this dissertation, we first consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a WSN, where each sensor receives a single snapshot of the field. An iterative distributed least-squares (DLS) algorithm is investigated with and without the consideration of node failures. In particular, without sensor node failures it is shown that every instantiation of the DLS algorithm converges, i.e., consensus is reached among the sensors, with the limiting agreement value being the centralized least-squares estimate. With node failures during the iterative exchange process, the convergence of the DLS algorithm is still guaranteed; however, an error exists be- tween the limiting agreement value and the centralized least-squares estimate. In order to reduce this error, a modified DLS scheme, the M-DLS, is provided. The M-DLS algorithm involves an additional weight compensation step, in which a sensor performs a one-time weight compensation procedure whenever it detects the failure of a neighbor. Through analytical arguments and simulations, it is shown that the M-DLS algorithm leads to a smaller error than the DLS algorithm, where the magnitude of the improvement dependents on the network topology. We then investigate the case when the observation or sensing mode is only partially known at the corresponding nodes, perhaps, due to their limited sensing capabilities or other unpredictable physical factors. Specifically, it is assumed that the observation validity at a node switches stochastically between two modes, with mode I corresponding to the desired signal plus noise observation mode (a valid observation), and mode II corresponding to pure noise with no signal information (an invalid observation). With no prior information on the local sensing modes (valid or invalid), we introduce a learning-based distributed estimation procedure, the mixed detection-estimation (MDE) algorithm, based on closed-loop interactions between the iterative distributed mode learning and the target estimation. The online learning (or sensing mode detection) step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE algorithm is established analytically, and the asymptotic performance analysis studies shows that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast with the estimation performance of a naive average consensus based distributed estimator (with no mode learning), whose estimation error blows up with an increasing SNR. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149566

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Download or read book Parameter Estimation Using Consensus Building Strategies with Application to Sensor Networks written by Kaushani Dasgupta and published by . This book was released on 2013 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: