Broadband adaptive beamforming with low complexity and frequency invariant response

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation.A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save based GSC beamforming structures have been explored. This system address the minimisation of the time domain MMSE, with a significant reduction in computational complexity when compared to time-domain implementations, and show a better convergence behaviour than the IFB beamformer. By studying the effects that the blocking matrix has on the adaptive process for the overlap-save beamformer, several modifications are carried out to enhance both the simplicity of the algorithm as well as its convergence speed. These modifications result in the GSC beamformer utilising a significantly lower computational complexity compare to the time domain approach while offering similar convergence characteristics.In certain applications, especially in the areas of acoustics, there is a need to maintain constant resolution across a wide operating spectrum that may extend across several octaves. To attain constant beamwidth is difficult, particularly if uniformly spaced linear sensor array are employed for beamforming, since spatial resolution is reciprocally proportional to both the array aperture and the frequency. A scaled aperture arrangement is introduced for the subband based GSC beamformer to achieve near uniform resolution across a wide spectrum, whereby an octave-invariant design is achieved. This structure can also be operated in conjunction with adaptive beamforming algorithms. Frequency dependent tapering of the sensor signals is proposed in combination with the overlap-save GSC structure in order to achieve an overall frequency-invariant characteristic. An adaptive version is proposed for frequency-invariant overlap-save GSC beamformer. Broadband adaptive beamforming algorithms based on the family of least mean squares (LMS) algorithms are known to exhibit slow convergence if the input signal is correlated. To improve the convergence of the GSC when based on LMS-type algorithms, we propose the use of a broadband eigenvalue decomposition (BEVD) to decorrelate the input of the adaptive algorithm in the spatial dimension, for which an increase in convergence speed can be demonstrated over other decorrelating measures, such as the Karhunen-Loeve transform. In order to address the remaining temporal correlation after BEVD processing, this approach is combined with subband decomposition through the use of oversampled filter banks. The resulting spatially and temporally decorrelated GSC beamformer provides further enhanced convergence speed over spatial or temporal decorrelation methods on their own

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Effect and Compensation of Timing Jitter in Through-Wall Human Indication via Impulse Through-Wall Radar

Impulse through-wall radar (TWR) is considered as one of preferred choices for through-wall human indication due to its good penetration and high range resolution. Large bandwidth available for impulse TWR results in high range resolution, but also brings an atypical adversity issue not substantial in narrowband radars — high timing jitter effect, caused by the non-ideal sampling clock at the receiver. The fact that impulse TWR employs very narrow pulses makes little jitter inaccuracy large enough to destroy the signal correlation property and then degrade clutter suppression performance. In this paper, we focus on the timing jitter impact on clutter suppression in through-wall human indication via impulse TWR. We setup a simple timing jitter model and propose a criterion namely average range profile (ARP) contrast is to evaluate the jitter level. To combat timing jitter, we also develop an effective compensation method based on local ARP contrast maximization. The proposed method can be implemented pulse by pulse followed by exponential average background subtraction algorithm to mitigate clutters. Through-wall experiments demonstrate that the proposed method can dramatically improve through-wall human indication performance

AGV RAD: AGV positioning system for ports using microwave doppler radar

Automation and intelligence have become an inevitable trend in the development of container terminals. The AGV (Automated Guided Vehicle) positioning is a primary problem to build the automated ports. Although the existing Ultra-High Frequency(UHF) RFID technology has good measurement accuracy and stability in the port AGV positioning, the exposed magnetic tags are easy to damage under the common heavy load, and its construction and maintenance cost is unbearable to most ports. Among the candidate technologies for the AGV positioning, microwave Doppler radar has a strong penetrating ability, and can work well in a complex environment (day and night, foggy, rainy). Therefore, the microwave Doppler radar-based AGV positioning system has attracted a lot of attention. In this thesis, a test system using the above technique was established, together with a NI myRIO real-time Wi-Fi compatible computation platform. Several computation algorithms were implemented to extract the accurate values of range and velocity. Wavelet denoising with the adapted threshold function was considered to filter noise contained in radar signals. In the frequency domain analysis, FFT and Chirp-Z Transform (CZT) joint algorithm was proposed to suppress the influence of fence effects and also improves real-time performance. In addition, 2D-FFT is used to calculate velocity of AGV. According to the port-like environment, the suitable AGV positioning algorithm and communication method based on microwave Doppler radars and NI myRIO-1900s also be proposed. The effectiveness of the proposed system was experimentally tested and several results are included in this thesis.Automação e inteligência artifical tornaram-se uma tendência inevitável no desenvolvimento dos terminais dos contentores. O posicionamento do VAG (Veículo Autónomo Guiado) é um dos problemas principais para construir as portas automatizadas. Embora a tecnologia RFID de frequência ultra-alta (UHF) existente tenha uma boa precisão e estabilidade de medição no posicionamento VAG dos portos, as etiquetas magnéticas expostas são fáceis de danificar sob a comum carga pesada e o seu habitual custo de construção e manutenção é insuportável para a maioria das portos. Entre as tecnologias para o posicionamento VAG, o radar Doppler de microondas possui uma forte capacidade de penetração e pode funcionar bem em ambientes complexos (dia, noite, nevoeiro e chuva). Portanto, o sistema de posicionamento VAG baseado em radar Doppler de microondas atraiu muita atenção. Nesta tese, foi estabelecido um sistema de teste usando a técnica acima mencionada, juntamente com uma plataforma de computação em tempo real, NI myRIO compatível com Wi-Fi. Vários algoritmos de computação foram envolvidos para extrair os valores precisos de distancia e velocidade. O “denoising” de wavelets com a função de limiar adaptado foi utilizado para filtrar o ruído nos sinais de radar. Na análise do domínio da frequência, o algoritmo conjunto FFT e Chirp-Z Transform (CZT) foi proposto para suprimir a influência dos efeitos de resolução e também melhorar o desempenho em tempo real. Além disso, o algoritmo 2D-FFT é usado para calcular a velocidade do VAG. De acordo com o ambiente dos portos, o algoritmo de posicionamento VAG e o método de comunicação adequado baseados em radares Doppler de microondas e NI myRIO-1900s também serão propostos. A eficiência do sistema proposto foi testada experimentalmente e vários resultados estão descritos nesta dissertação

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

The Future of Neutrino Mass Measurements: Terrestrial, Astrophysical, and Cosmological Measurements in the Next Decade. Highlights of the NuMass 2013 Workshop. Milano, Italy, February 4 - 7, 2013

The third Workshop of the NuMass series ("The Future of Neutrino Mass Measurements: Terrestrial, Astrophysical, and Cosmological Measurements in the Next Decade: NuMass 2013") was held at Dipartimento di Fisica "G. Occhialini, University of Milano-Bicocca in Milano, Italy, on 4-7 February 2013. The goal of this international workshop was to review the status and future of direct and indirect neutrino mass measurements in the laboratory as well as from astrophysical and cosmological observations. This paper collects most of the contributions presented during the Workshop

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed