Maximizing the Number of Spatial Nulls with Minimum Sensors

In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level

AN IMPROVED METHOD FOR THE IDENTIFICATION AND INVERSION OF MULTI-MODE RAYLEIGH SURFACE WAVE DISPERSION COLLECTED FROM NON-UNIFORM ARRAYS UTILIZING A MOVING SOURCE APPROACH

An improved method using a moving source approach is utilized in the analysis of Rayleigh surface waves for the accurate identification of higher mode propagation used in inversion. Two non invasive surface wave methods, Multi- station Analysis of Surface Waves (MASW) and Refraction Microtremor (ReMi) were used for the construction of composite dispersion curves representing the relationship of Rayleigh phase velocity (VR) with frequency. Multiple tests were executed with source offsets increasing with each successive test in order to account for near field effects and higher mode attenuation levels. The resulting dispersions were combined to form a composite dispersion which effectively maps all participating modes of propagation. The inversion was executed using a genetic algorithm (GA) which takes advantage of the Rayleigh forward problem. The results show good ability to identify intermediate high and low velocity layers and agree well with downhole results

Antenna Systems

This book offers an up-to-date and comprehensive review of modern antenna systems and their applications in the fields of contemporary wireless systems. It constitutes a useful resource of new material, including stochastic versus ray tracing wireless channel modeling for 5G and V2X applications and implantable devices. Chapters discuss modern metalens antennas in microwaves, terahertz, and optical domain. Moreover, the book presents new material on antenna arrays for 5G massive MIMO beamforming. Finally, it discusses new methods, devices, and technologies to enhance the performance of antenna systems

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide O(N^2) degrees of freedom using only physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input–minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Compressive Sensing Based Estimation of Direction of Arrival in Antenna Arrays

This thesis is concerned with the development of new compressive sensing (CS) techniques both in element space and beamspace for estimating the direction of arrival of various types of sources, including moving sources as well as fluctuating sources, using one-dimensional antenna arrays. The problem of estimating the angle of arrival of a plane electromagnetic wave is referred to as the direction of arrival (DOA) estimation problem. Such algorithms for estimating DOA in antenna arrays are often used in wireless communication network to increase their capacity and throughput. DOA techniques can be used to design and adapt the directivity of the array antennas. For example, an antenna array can be designed to detect a number of incoming signals and accept signals from certain directions only, while rejecting signals that are declared as interference. This spatio-temporal estimation and filtering capability can be exploited for multiplexing co-channel users and rejecting harmful co-channel interference that may occur because of jamming or multipath effects. In this study, three CS-based DOA estimation methods are proposed, one in the element space (ES), and the other two in the beamspace (BS). The proposed techniques do not require a priori knowledge of the number of sources to be estimated. Further, all these techniques are capable of handling both non-fluctuating and fluctuating source signals as well as moving signals. The virtual array concept is utilized in order to be able to identify more number of sources than the number of the sensors used. In element space, an extended version of the least absolute shrinkage and selection operator (LASSO) algorithm, the adaptable LASSO (A-LASSO), is presented. A-LASSO is utilized to solve the DOA problem in compressive sensing framework. It is shown through extensive simulations that the proposed algorithm outperforms the classical DOA estimation techniques as well as LASSO using a small number of snapshots. Furthermore, it is able to estimate coherent as well as spatially-close sources. This technique is then extended to the case of DOA estimation of the sources in unknown noise fields. In beamspace, two compressive sensing techniques are proposed for DOA estimation, one in full beamspace and the other in multiple beam beamspace. Both these techniques are able to estimate correlated source signals as well as spatially-close sources using a small number of snapshots. Furthermore, it is shown that the computational complexity of the two beamspace-based techniques is much less than that of the element-space based technique. It is shown through simulations that the performance of the DOA estimation techniques in multiple beam beamspace is superior to that of the other two techniques proposed in this thesis, in addition to having the lowest computational complexity. Finally, the feasibility for real-time implementation of the proposed CS-based DOA estimation techniques, both in the element-space and the beamspace, is examined. It is shown that the execution time of the proposed algorithms on Raspberry Pi board are compatible for real-time implementation