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<p>In this thesis, we develop methods for addressing the deficiencies of array processing with linear thinned arrays. Our methods are designed for array systems mounted on moving platforms and exploit synthetic aperture processing techniques. In particular, we use array motion to decrease the sidelobe levels and increase the degrees of freedom available from thinned array systems. In this work, we consider two application areas 1) passive SONAR and 2) ultrasound imaging. </p><p>Synthetic aperture processing is a methodology for exploiting array motion and has been successfully used in practice to increase array resolution. By spatially sampling along the path of the array virtual sensors can be realized and coherently fused to the existing array. The novel contribution of this work is our application of synthetic aperture processing. Here our goal is not to increase array resolution, instead we propose to use the synthetic aperture process to expand the spatial covariance and spatial frequency sensing capabilities of thinned array system.</p><p> </p><p>In the passive sensing case, we use a class of thinned arrays know as co-prime linear sensor arrays for source localization. The class of co-prime arrays provides roughly half the aperture worth of spatial covariances and with modest array motion can be extended to the full aperture of the array. The amount of motion required to produce a full set of spatial covariances is shown to be a function of the co-prime array parameters and is only a fraction of the total aperture of the array. The full set of spatial covariances can be used to form a spatial covariance matrix with dimension equal to that of a uniform array. With a spatial covariance matrix in hand one can perform signal processing tasks as if the array were fully populated. Three methods for spatial covariance matrix estimation are compared in different source localization scenarios. In the work presented here, we demonstrate the benefits of our approach for achieving reduced sidelobe levels and extending the source localization capabilities above the limits of the static co-prime array. </p><p>In the active sensing case, we develop a framework for incorporating motion using thinned arrays for ultrasound imaging. In this setting, array motion is used to augment the spatial frequency sensing capabilities of the thinned array system. Here we develop an augmentation strategy based on using quarter-wavelength array translations to fill-in missing spatial frequencies not measured by the static thinned array. The quarter-wavelength translation enables the thinned array system to sample missing spatial frequencies and increase the redundancy of other spatial frequencies sampled by the array. We compare the level of redundancy in sampling the spatial frequencies achieved by the thinned arrays post translation to different levels of sample redundancy derived from pruning the transmit/receive events of a uniform array. In this manner, we are able to examine how the level of spatial frequency redundancy afforded by different thinned arrays compare over the full redundancy range of the uniform array. While artificially pruning the uniform array does not necessarily create realizable arrays, it provides the means to compare image quality at different spatial frequency redundancy levels. In this work, we are able to conclude that images formed from thinned arrays using the translated synthetic aperture process are capable of approximating images formed from the corresponding uniform array. In particular, the systems considered in this work have approximately one-third of the active sensors when compared to the uniform array. </p><p>In both application areas, the use of thinned arrays offers a reduction in the cost to deploy and maintain a given array system. The feature that makes it possible to overcome the spatial sampling deficiencies of thinned array systems is motion and it is at the core of the performance gains in these applications.</p> / Dissertation

Radio astronomy, the survey and study of naturally occurring astronomical radio signals, is a challenging field in terms of engineering requirements. The typical astronomical signal of interest is incredibly faint, resulting in very low signal-to-noise ratios (SNRs) on the order of -30 dB or lower. To detect such signals, one must have an uncommonly low and stable receiver noise temperature, high gain through large aperture reflectors, and state-of-the-art signal processing algorithms. One must also be able to mitigate the effects of interference, the presence of which, even if extremely weak, can completely mask the faint astronomical signals of interest. To this end, this work presents the development of and results from a new broadband phased array feed (PAF) named the Focal L-Band Array for the Green Bank Telescope (FLAG). This instrument is able to form multiple simultaneous beams to survey a large patch of sky instantaneously, and has a minimum system noise temperature (Tsys) of 16.83 K. This PAF also has the potential to use spatial filtering techniques to place pattern nulls in the direction of interfering signals through the use of an orthogonal projection. This work will also present an improved method for computing an orthogonal projection operator, which is able to place a spatially broad null in the direction of a moving RFI source. A formal derivation of some detection and estimation theory properties for astronomical radio transients is also presented, which formalization is lacking within the astronomical community. This includes maximum-likelihood detectors and estimators and a Cramér Rao bound (CRB) analysis of astronomical transient parameters.

[pt] O problema de estimação de direção (DoA) de chegada é um importante tópico de pesquisa em áreas como radar, sonar, sismologia, vigilância eletrônica e comunicações sem fio. Este trabalho teve como principal resultado o desenvolvimento de um novo algoritmo que combina o método da máxima verossimilhança (ML) estocástica com o algoritmo ganancioso de busca ortogonal (OMP), comumente empregado em recuperação esparsa com Sensoriamento Compressivo (CS). Muito embora técnicas ML sejam consideradas ótimas em termos de erro médio quadrático, atingindo o limitante inferior de Cramér-Rao (CRLB), o menor limitante inferior para a variância do estimador, estas técnicas demandam de modo significante, às vezes inexequível, os recursos computacionais. Por outro lado, a partir de uma variante esparsa da equação de aquisição de dados, o problema de encontrar a solução mais esparsa possível de sistemas de equações subdeterminados com o algoritmo OMP tem sido empregado de modo satisfatório para encontrar as estimativas de direção, porém com muitas oportunidades de melhoria em casos com cenários sujeitos a condições severas. Por exemplo, cenários com acoplamento eletromagnético (EM), baixa razão sinal-ruído (SNR) e um número limitado de amostras temporais disponíveis. O estimador de direção proposto, baseado em coarranjo diferença, denominado OMP com Máxima Verossimilhança Baseado em Lista (LBML-OMP), apresentou uma melhora significativa no processo de estimação em comparação com técnicas tradicionais e modernas, tais como: OMP, Técnica de Limiar Iterativa (IHT) e Classificação de Múltiplos Sinais Espacialmente Suavizados (SS-MUSIC). A técnica proposta utiliza uma lista de candidatos gerada a partir da solução do algoritmo OMP original e decide pelo melhor a partir de uma busca limitada utilizando o estimador ML estocástico, o que justifica seu uso em cenários práticos. Para a amostragem dos sinais no ambiente, arranjos lineares não-uniformes clássicos e modernos foram empregados, tais como Arranjos Aninhados de Segunda Ordem (NAQ2), Arranjos Aninhados de Segunda Ordem Aperfeiçoados (SNAQ2), Arranjos de Redundância Mínima (MRA) e Arranjos Coprimos (CPA). Além disso, a estimação foi realizada considerando-se o efeito do acoplamento EM e ruído. Ainda, um novo modelo para estimação de direção em coarranjo diferença foi desenvolvido. Este modelo considera o número de amostras temporais finitas (não-assintótico) e mostrou melhora significativa quando do seu emprego no processo de estimação de direção de todos os algoritmos considerados, não apenas o LBML-OMP, evidenciando fontes secundárias de erro no modelo original estabelecido. De forma a complementar o trabalho, um algoritmo de atenuação de ruído chamado OMP aleatorizado (RandOMP) foi utilizado para aumentar a precisão da estimação em cenários com condições de ruído severas. Neste sentido, as contribuições deste trabalho estão relacionadas principalmente ao desenvolvimento de um novo algoritmo e um novo modelo de transformação em coarranjo diferença de modo a melhorar as estimativas de direção das fontes com arranjos lineares não-uniformes. Além disso, enfatiza-se o emprego de diferentes geometrias para as simulações, tornando-se evidente o impacto da posição dos sensores nas curvas de raiz quadrada do erro médio quadrático (RMSE). / [en] The Direction of Arrival (DoA) estimation or Direction Finding (DF) is a relevant topic for research in areas such as radar, sonar, seismology, electronic surveillance, and wireless communications. This thesis devises a new algorithm that combines a stochastic Maximum Likelihood (ML) method with the widely-known Orthogonal Matching Pursuit (OMP) greedy algorithm, commonly used in sparse recovery with Compressive Sensing (CS). Even though ML techniques are known to be optimal in the mean-squared error sense, achieving the Cramér-Rao Lower Bound (CRLB), the tighter lower bound on estimator variance, they demand a significant, sometimes infeasible, amount of computational resources. On the other hand, departing from a sparsified variant of the data acquisition equation, the problem of finding the sparsest solution of underdetermined systems of equations with OMP has been employed successfully to find the DoA estimates, but with many opportunities for improvement in cases of challenging scenarios. For instance, scenarios with electromagnetic (EM) coupling, low signal-to-noise ratio (SNR), and a limited number of available snapshots (time samples). The proposed difference coarray DoA estimator termed List-Based Maximum Likelihood OMP (LBML-OMP) has shown substantial improvements over traditional and modern techniques, such as OMP, Iterative Hard Thresholding (IHT), and Spatial Smoothing Multiple Signal Classification (SS-MUSIC). It uses a list of candidates generated from the OMP solution and decides for the best based on a limited search using the stochastic ML rule. Thus, it does not perform a grid search with the ML estimator, and this justifies its use in practical scenarios. For the sensing of space-time field, classic and modern non-uniform linear arrays are employed, such as 2-nd Order Nested Array (NAQ2), 2-nd Order Super Nested Array (SNAQ2), Minimum Redundancy Array (MRA), Minimum Hole Array (MHA), and Coprime Array (CPA). Additionally, the estimation is performed under the assumption of EM coupling and noise as disturbing side effects. Furthermore, a new model for difference coarray DoA estimation is developed. It accounts for the finite number of snapshots and has shown to increase the estimation accuracy for all the algorithms, not only LBML-OMP, evidencing secondary sources of error for the difference coarray transformation. To complement the work, a denoising algorithm called Randomized OMP (RandOMP) was applied to successfully increase the estimation accuracy for difference coarray estimators in scenarios with severe noisy conditions. The contributions of this work relate mainly to the development of a new algorithm and a new difference coarray transformation to improve the DoA estimation accuracy with non-uniform linear arrays. Also, it should be noticed the employment of different geometries for the numerical experiments, making evident the impact of the array sensors positions in the root mean square error (RMSE) curves.

The ever increasing diversity in communication systems has created a demand for constant improvements in receiver components. This thesis describes the design and characterisation of front-end receiver components for various challenging applications, including characterisation of low noise foundry processes, LNA design and multi-band antenna design. It also includes a new theoretical analysis of noise coupling in low noise phased array receivers.In LNA design much depends on the choice of the optimum active devices. A comprehensive survey of the performance of low noise transistors is therefore extremely beneficial. To this end a comparison of the DC, small-signal and noise behaviours of 10 state-of-the-art GaAs and InP based pHEMT and mHEMT low noise processes has been carried out. Their suitability in LNA designs has been determined, with emphasis on the SKA project. This work is part of the first known detailed investigation of this kind. Results indicate the superiority of mature GaAs-based pHEMT processes, and highlight problems associated with the studied mHEMT processes. Two of the more promising processes have then been used to design C-band and UHF-band MMIC LNAs. A new theoretical analysis of coupled noise between antenna elements of a low noise phased array receiver has been carried out. Results of the noise wave analysis, based on fundamental principles of noisy networks, suggest that the coupled noise contribution to system noise temperatures should be smaller than had previously been suggested for systems like the SKA. The principles are applicable to any phased array receiver. Finally, a multi-band antenna has been designed and fabricated for a severe operating environment, covering the three extremely crowded frequency bands, the 2.1 GHz UMTS, the 2.4 GHz ISM and the 5.8 GHz ISM bands. Measurements have demonstrated excellent performance, exceeding that of equivalent commercial antennas aimed at similar applications.