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

Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

A pre-filtering maximum likelihood approach to multiple source direction estimation.

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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

Array processing techniques for direction of arrival estimation, communications, and localization in vehicular and wireless sensor networks

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Técnicas de processamentos de sinais para comunicações sem fio tem sido um tópico de interesse para pesquisas há mais de três décadas. De acordo com o padrão Release 9 desenvolvido pelo consorcio 3rd Generation Partnership Project (3GPP) sistemas utilizando múltiplas antenas foram adotados na quarta geração (4G) dos sistemas de comunicação sem fio, também conhecida em inglês como Long Term Evolution (LTE). Para a quinta geração (5G) dos sistemas de comunicação sem fio centenas de antenas devem ser incorporadas aos equipamentos, na arquitetura conhecida em inglês como massive multi-user Multiple Input Multiple Output (MIMO). A presença de múltiplas antenas provê benefícios como o ganho do arranjo, ganho de diversidade, ganho espacial e redução de interferência. Além disso, arranjos de antenas possibilitam a filtragem espacial e a estimação de parâmetros, ambos podem ser usados para se resolver problemas que antes não eram vistos pelo prisma de processamento de sinais. O objetivo dessa tese é superar a lacuna entre a teoria de processamento de sinais e as aplicações da mesma em problemas reais. Tradicionalmente, técnicas de processamento de sinais assumem a existência de um arranjo de antenas ideal. Portanto, para que tais técnicas sejam exploradas em aplicações reais, um conjunto robusto de métodos para interpolação do arranjo é fundamental. Estes métodos são desenvolvidos nesta tese. Além disso problemas no campo de redes de sensores e redes veiculares são tratados nesta tese utilizando-se uma perspectiva de processamento de sinais. Nessa tesa métodos inovadores de interpolação de arranjos são apresentados e sua performance é testada utilizando-se cenários reais. Conceitos de processamento de sinais são implementados no contexto de redes de sensores. Esses conceitos possibilitam um nível de sincronização suficiente para a aplicação de sistemas de múltiplas antenas distribuídos, o que resulta em uma rede com maior vida útil e melhor performance. Métodos de processamento de sinais em arranjos são propostos para resolver o problema de localização baseada em sinais de rádio em redes veiculares, com aplicações em segurança de estradas e proteção de pedestres. Esta tese foi escrita em língua inglesa, um sumário em língua portuguesa é apresentado ao final da mesma.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Array signal processing in wireless communication has been a topic of interest in research for over three decades. In the fourth generation (4G) of the wireless communication systems, also known as Long Term Evolution (LTE), multi antenna systems have been adopted according to the Release 9 of the 3rd Generation Partnership Project (3GPP). For the fifth generation (5G) of the wireless communication systems, hundreds of antennas should be incorporated to the devices in a massive multi-user Multiple Input Multiple Output (MIMO) architecture. The presence of multiple antennas provides array gain, diversity gain, spatial gain, and interference reduction. Furthermore, arrays enable spatial filtering and parameter estimation, which can be used to help solve problems that could not previously be addressed from a signal processing perspective. The aim of this thesis is to bridge some gaps between signal processing theory and real world applications. Array processing techniques traditionally assume an ideal array. Therefore, in order to exploit such techniques, a robust set of methods for array interpolation are fundamental and are developed in this work. Problems in the field of wireless sensor networks and vehicular networks are also addressed from an array signal processing perspective. In this dissertation, novel methods for array interpolation are presented and their performance in real world scenarios is evaluated. Signal processing concepts are implemented in the context of a wireless sensor network. These concepts provide a level of synchronization sufficient for distributed multi antenna communication to be applied, resulting in improved lifetime and improved overall network behaviour. Array signal processing methods are proposed to solve the problem of radio based localization in vehicular network scenarios with applications in road safety and pedestrian protection

Direction of Arrival Algorithm using GSU-minimization

A smart antenna is a digital wireless communications antenna system that takes advantage of diversity effect at the source (transmitter), the destination (receiver) or both. Diversity effect involves the transmission and/or reception of multiple radio frequency (RF) waves to increase data speed and reduce the error rate. A smart antenna enables a higher capacity in wireless networks by effectively reducing multipath and co-channel interference. This is achieved by focusing the radiation only in the desired direction and adjusting itself to changing traffic conditions or signal environments. Smart antennas employ a set of radiating elements arranged in the form of an array. The GSU-MUSIC algorithm for DOA estimation of smart antenna is similar to MUSIC and it uses iterative approach based on GSU minimization to find accurate values of the peaks. The GSU-MUSIC Algorithm overcomes the problems associated with previous techniques used for DOA estimation of smart antenna. DOI: 10.17762/ijritcc2321-8169.160412

Transformations for non-ideal uniform circular arrays operating in correlated signal environments

The Davies transformation is a method to transform the steering vector of a uniform circular array (UCA) to one with Vandermonde form. As such, it allows techniques such as spatial smoothing for direction-of-arrival (DOA) estimation in a correlated signal environment, developed originally for uniform linear arrays, to be applied to UCAs. However, the Davies transformation can be highly sensitive to perturbations of the underlying array model. This paper presents a method for deriving a more robust transformation using optimization techniques. The effectiveness of the method is illustrated through a number of DOA estimation examples