Efficient subspace fitting algorithms for diversely polarized arrays

Diversely polarized antenna arrays are widely used in RF ap-plications. The diverrity of rerponse provided b y such arrays can greatly improve direction finding performance over arrays sensi-tive to only one polarixation component. For d emitterr, direct implementation of a multidimensional estimation algorithm (e.g., maximum likelihood) requires a search f o r 3d parameters: d direc-tions of arrival (DOAs), and 2d polarization parameters. In this paper, we derive a more &cent solution based on noise subspace fitting (NSF). The NSF algorithm is decoupled into a two-step procedure, where the DOAs are estimated first, and then the p o-larization parametera are obtained b y solving a linear equation. The main advantage of this approach is that the search dimen-sion is reduced b y a factor of three. In addition, the algorithm can be shown to yield asymptotically minimum variance estimates provided no perfectlg coherent signals are present. 1

Simultaneous Source Localization and Polarization Estimation via Non-Orthogonal Joint Diagonalization with Vector-Sensors

Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods

Array interpolation methods with applications in wireless sensor networks and global positioning systems

Dissertação (mestrado)—Universidade de Brasília, 2013.Nas últimas três décadas o estudo de técnicas de processamento de sinais em arranjos de sensores tem recebido grande atenção. Uma grande quantidade de técnicas foi desenvolvida com diversas finalidades como a estimação da direção de chegada, a filtragem ou separação espacial dos sinais recebidos, a estimação do atraso de propagação, a estimação da frequência Doppler e a pré-codificação de sinais na transmissão para maximização da potência recebida por outro arranjo. Técnicas para estimação da direção de chegada são de particular interesse para sistemas de posicionamento baseado em ondas de rádio, como os sistemas de posicionamento global e para o mapeamento de sensores em redes de sensores. Uma particularidade dessas aplicações é a necessidade de uma estimação em tempo real ou computacionalmente eficiente. Técnicas de estimação da direção de chegada que atendem esses requisitos requerem uma estrutura muito específica do arranjo de antenas que, em geral, não pode ser obtida em implementações reais. Nesse trabalho é apresentado um conjunto de técnicas que permitem a interpolação de sinais recebidos em arranjos de geometria arbitrária para arranjos de geometria específica, de forma eficiente e robusta, para possibilitar a aplicação de técnicas eficientes para estimação da direção de chegada em arranjos de geometria arbitrária. Como aplicações das técnicas propostas são apresentados o mapeamento preciso em redes de sensores e posicionamento preciso em receptores de sistemas de posicionamento global. _______________________________________________________________________________________ ABSTRACTIn the last three decades the study of antenna array signal processing techniques has received significant attention. A large number of techniques have been developed with different purposes such as the estimation of the direction of arrival (DOA), filtering or spatial separation of received signals, estimation of time delay of arrival (TDOA), Doppler frequency estimation and precoding of transmitted signals to maximize the power received by a different array. DOA estimation techniques are of particular interest for positioning systems based on radio waves such as the global positioning system (GPS) and for sensor mapping in wireless sensor networks (WSNs). These applications have the particular requirement of demanding the estimations to be made in real time or with reduced computational complexity. DOA estimation techniques that fulfill these requirements demand very specific antenna array structures that cannot, in general, be obtained in real implementations. In this work a set of techniques is presented that allows the interpolation of signals received in arrays of arbitrary geometry into arrays of specific geometry efficiently and robustly to allow the application of efficient DOA estimation techniques in arrays of arbitrary geometry. As an application of the proposed techniques precise mapping for WSNs and precise positioning for GPS receivers is presented

Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation

Multi-mode antennas are an alternative to classical antenna arrays, and hence a promising emerging sensor technology for a vast variety of applications in the areas of array signal processing and digital communications. An unsolved problem is to describe the radiation pattern of multi-mode antennas in closed analytic form based on calibration measurements or on electromagnetic field (EMF) simulation data. As a solution, we investigate two modeling methods: One is based on the array interpolation technique (AIT), the other one on wavefield modeling (WM). Both methods are able to accurately interpolate quantized EMF data of a given multi-mode antenna, in our case a planar four-port antenna developed for the 6-8.5 GHz range. Since the modeling methods inherently depend on parameter sets, we investigate the influence of the parameter choice on the accuracy of both models. Furthermore, we evaluate the impact of modeling errors for coherent maximum-likelihood direction-of-arrival (DoA) estimation given different model parameters. Numerical results are presented for a single polarization component. Simulations reveal that the estimation bias introduced by model errors is subject to the chosen model parameters. Finally, we provide optimized sets of AIT and WM parameters for the multi-mode antenna under investigation. With these parameter sets, EMF data samples can be reproduced in interpolated form with high angular resolution

Statistical Nested Sensor Array Signal Processing

Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N 2 ) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods

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

High-resolution Direction-of-Arrival estimation

Direction of Arrival (DOA) estimation is considered one of the most crucial problems in array signal processing, with considerable research efforts for developing efficient and effective direction-finding algorithms, especially in the transportation industry, where the demand for an effective, real-time, and accurate DOA algorithm is increasing. However, challenges must be addressed before real-world deployment can be realised. Firstly, there is the requirement for fast computational time for real-time detection. Secondly, there is a demand for high-resolution and accurate DOA estimation. In this thesis, two state-of-the-art DOA estimation algorithms are proposed and evaluated to address the challenges. Firstly, a novel covariance matrix reconstruction approach for single snapshot DOA estimation (CbSS) was proposed. CbSS was developed by exploiting the relationship between the theoretical and sample covariance matrices to reduce estimation error for a single snapshot scenario. CbSS can resolve accurate DOAs without requiring lengthy peak searching computational time by computationally changing the received sample covariance matrix. Simulation results have verified that the CbSS technique yields the highest DOA estimation accuracy by up to 25.5% compared to existing methods such as root-MUSIC and the Partial Relaxation approach. Furthermore, CbSS presents negligible bias when compared to the existing techniques in a wide range of scenarios, such as in multiple uncorrelated and coherent signal source environments. Secondly, an adaptive diagonal-loading technique was proposed to improve DOA estimation accuracy without requiring a high computational load by integrating a modified novel and adaptive diagonal-loading method (DLT-DOA) to further improve estimation accuracy. An in-depth simulation performance analysis was conducted to address the challenges, with a comparison against existing state-of-the-art DOA estimation techniques such as EPUMA and MODEX. Simulation results verify that the DLT-DOA technique performs up to 8.5% higher DOA estimation performance in terms of estimation accuracy compared to existing methods with significantly lower computational time. On this basis, the two novel DOA estimation techniques are recommended for usage in real-world scenarios where fast computational time and high estimation accuracy are expected. Further research is needed to identify other factors that could further optimize the algorithms to meet different demands

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