Robust Adaptive Beamforming
Robust Adaptive Beamforming

Author: Jian Li

Publisher: John Wiley & Sons

Published: 2005-10-10

Total Pages: 422

ISBN-13: 0471733466

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The latest research and developments in robust adaptivebeamforming Recent work has made great strides toward devising robust adaptivebeamformers that vastly improve signal strength against backgroundnoise and directional interference. This dynamic technology hasdiverse applications, including radar, sonar, acoustics, astronomy,seismology, communications, and medical imaging. There are alsoexciting emerging applications such as smart antennas for wirelesscommunications, handheld ultrasound imaging systems, anddirectional hearing aids. Robust Adaptive Beamforming compiles the theories and work ofleading researchers investigating various approaches in onecomprehensive volume. Unlike previous efforts, these pioneeringstudies are based on theories that use an uncertainty set of thearray steering vector. The researchers define their theories,explain their methodologies, and present their conclusions. Methodspresented include: * Coupling the standard Capon beamformers with a spherical orellipsoidal uncertainty set of the array steering vector * Diagonal loading for finite sample size beamforming * Mean-squared error beamforming for signal estimation * Constant modulus beamforming * Robust wideband beamforming using a steered adaptive beamformerto adapt the weight vector within a generalized sidelobe cancellerformulation Robust Adaptive Beamforming provides a truly up-to-date resourceand reference for engineers, researchers, and graduate students inthis promising, rapidly expanding field.

Simplified Robust Adaptive Detection and Beamforming for Wireless Communications
Simplified Robust Adaptive Detection and Beamforming for Wireless Communications

Author: Ayman ElNashar

Publisher: John Wiley & Sons

Published: 2018-08-20

Total Pages: 424

ISBN-13: 1118938240

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This book presents an alternative and simplified approaches for the robust adaptive detection and beamforming in wireless communications. It adopts several systems models including DS/CDMA, OFDM/MIMO with antenna array, and general antenna arrays beamforming model. It presents and analyzes recently developed detection and beamforming algorithms with an emphasis on robustness. In addition, simplified and efficient robust adaptive detection and beamforming techniques are presented and compared with exiting techniques. Practical examples based on the above systems models are provided to exemplify the developed detectors and beamforming algorithms. Moreover, the developed techniques are implemented using MATLAB—and the relevant MATLAB scripts are provided to help the readers to develop and analyze the presented algorithms. em style="mso-bidi-font-style: normal;"Simplified Robust Adaptive Detection and Beamforming for Wireless Communications starts by introducing readers to adaptive signal processing and robust adaptive detection. It then goes on to cover Wireless Systems Models. The robust adaptive detectors and beamformers are implemented using the well-known algorithms including LMS, RLS, IQRD-RLS, RSD, BSCMA, CG, and SD. The robust detection and beamforming are derived based on the existing detectors/beamformers including MOE, PLIC, LCCMA, LCMV, MVDR, BSCMA, and MBER. The adopted cost functions include MSE, BER, CM, MV, and SINR/SNR.

Academic Press Library in Signal Processing
Academic Press Library in Signal Processing

Author: Mats Viberg

Publisher: Academic Press

Published: 2013-08-31

Total Pages: 1013

ISBN-13: 0124116213

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This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

Adaptive Filtering
Adaptive Filtering

Author: Wenping Cao

Publisher: BoD – Books on Demand

Published: 2021-10-20

Total Pages: 154

ISBN-13: 1839623772

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Active filters are key technologies in applications such as telecommunications, advanced control, smart grids, and green transport. This book provides an update of the latest technological progress in signal processing and adaptive filters, with a focus on Kalman filters and applications. It illustrates fundamentals and guides filter design for specific applications, primarily for graduate students, academics, and industrial engineers who are interested in the theoretical, experimental, and design aspects of active filter technologies.

Robust Adaptive Beamforming
Robust Adaptive Beamforming

Author:

Publisher:

Published: 1996

Total Pages: 76

ISBN-13:

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The minimum-variance distortionless-response (MVDR) beamforming method revolves around the computation of a set of sensor weights via a sample covariance matrix formed form field observations made at the sensors. Reducing or constraining the sensor weight amplitudes is an effective way of ameliorating problems with the method and can be optimally implemented in many cases by an extremely simple modification of the sample covariance matrix prior to computing the weights. This consists of writing the weights in vector form, where the constraint is on the value of the inner product vectors known as a quadratic function. This report features a reasonably extensive set of simulation results which attempt to examine the practical impact of the quadratically constrained MVDR approach. Infinite- and finite-time behaviour are discussed for various scenarios known to cause performance degradation for the standard MVDR adaptive beamformer. The transient behaviour of the method in highly dynamic fields is also briefly examined. Performance gains using the new method are demonstrated in terms of output signal-to-noise ratio.

Covariance Matrix Filtering for Adaptive Beamforming with Moving Interference
Covariance Matrix Filtering for Adaptive Beamforming with Moving Interference

Author:

Publisher:

Published: 2001

Total Pages: 0

ISBN-13:

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An approach is developed for adaptive beamforming for mobile sonars operating in an environment with moving interference from surface shipping. It is assumed that the sound source of each ship is drawn from an ensemble of Gaussian random noise, but each ship moves at constant speed along a deterministic course. An analytic expression for the ensemble mean covariance is obtained. In practice the location, course, speed, mean noise level, and transmission loss of each interferer are not known with sufficient precision to use the modeled ensemble mean as a basis for adaptive beamforming. The problem is thus to accurately estimate the ensemble mean based on data samples. The analytic ensemble mean is not stationary, and thus is not well estimated by the sample mean. The ensemble of covariance samples consists of rapidly varying random terms associated with the emitted noise and more slowly oscillating deterministic terms associated with the source and receiver motion. The non-stationary ensemble covariance mean can be estimated by filtering out the rapidly varying noise while retaining the slow oscillatory terms. Performance of the filters can be visualized and assessed in the epoch frequency domain, the Fourier transform of the covariance samples. In this domain, higher bearing rates show up at higher frequencies. The traditional sample mean estimator retains only the zero frequency bin corresponding to stationary interference. Techniques that can identify and include the appropriate non-zero frequency contributions are better non-stationary estimators than the sample mean. Several such techniques are offered and compared. Simulations are invaluable in evaluating the filter performance, since the ensemble mean can be precisely calculated analytically in the simulation and compared directly with the sample estimates. Simulations of adaptive beamformers using covariance filtering will be shown to yield improved robustness to shipping motion. (2 figures, 5 refs.).