[pt] Processamento em arranjos de sensores é uma área com vasta aplicação, tanto civil quanto militar, por exemplo em sonar, radar, sismologia e comunicações sem fio. Por meio de processamento espacial e espácio-temporal é possível melhorar suas funcionalidades e explorar novas possibilidades. Esta área vem atraindo cada vez mais a atenção e os esfor¸cos da comunidade científica, especialmente agora, em que antenas phased-array se estabeleceram como uma tecnologia comercial e madura. Neste contexto, nós tratamos o problema de processamento com posto reduzido em processamento espacial (beamforming) e espácio-temporal de sinais radar e a nova área de radares com função dual de radar e comunicações (dualfunction radar-communications, DFRC), que pode ser resumida na incorporação de mensagens de comunicações nas transmissıes radar como uma tarefa secundária. Nesta tese, nós investigamos a aplicação de um novo esquema de reduções de posto baseado em interpolação e decimação em duas áreas distintas: processamento espacial e processamento espácio-temporal de sinais radar. Este algoritmo para redução de posto nunca havia sido testado nestes ambientes antes e apresentou resultados bastante expressivos. Nós também propomos simplificações para reduzir a complexidade computacional do algoritmo em bemforming. Quanto ao tópico de DFRC, nós propomos dois métodos originais para incorporar modulação de amplitude/fase aos lóbulos laterais do diagrama de irradiação do radar de forma robusta. Os métodos propostos são muito mais simples do que o estado-da-arte e apresentam desempenho superior em termos de robustez e aplicabilidade em operações de tempo-real. Nós ainda provemos várias outras análises, comparações e contribuições a esta nova área. / [en] Array processing is an area with many civilian and military applications, e.g. sonar, radar, seismology and wireless communications. By means of space and space-time processing it is possible to enhance their features and explore new possibilities. This area has been attracting increasingly more attention and gathering more efforts of the science community, especially now, that phased array antennas are established as a commercial and mature technology. Within this context, we address the problem of reduced rank processing in space and space-time radar signal processing and the new area of dual-function radar-communications (DFRC), which may be summarized as embedding communication messages into radar emissions as a secondary task for the radar. In this thesis, we investigate the application of a new joint interpolation and decimation rank reducing scheme in two different areas: beamforming and space-time radar processing. This rank reducing algorithm was never tested within these contexts before and shows impressive results. We also propose simplifications for decreasing the computational complexity of the algorithm in beamforming. In the topic of DFRC, we propose two original robust radar-embedded sidelobe phase/amplitude modulation methods which have simple closed form equations. The proposed methods are much simpler than the state of the art and have superior performance in terms of robustness and real-time applicability.

The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed.