Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

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

The Global Navigation System Scope (GNSScope): a toolbox for the end-to-end modelling simulation and analysis of GNSS

The thesis provides a detailed overview of the work carried out by the author over the course of the research for the award of the degree of Doctor of Philosophy at the University of Westminster, and the performance results of the novel techniques introduced into the literature. The outcome of the work is collectively referred to as the Global Navigation System Scope (GNSScope) Toolbox, offering a complete, fully reconfigurable platform for the end-to-end modeling, simulation and analysis of satellite navigation signals and systems, covering the signal acquisition, tracking, and range processing operations that take place in a generic Global Navigation Satellite System (GNSS) receiver, accompanied by a Graphical User Interface (GUI) providing access to all the techniques available in the toolbox. Designed and implemented entirely in the MATLAB mathematical programming environment using Software Defined Radio (SDR) receiver techniques, the toolbox offers a novel new acquisition algorithm capable of handling all Phase-Shift Keying (PSK) type modulations used on all frequency bands in currently available satellite navigation signals, including all sub-classes of the Binary Offset Carrier (BOC) modulated signals. In order to be able to process all these signals identified by the acquisition search, a novel tracking algorithm was also designed and implemented into the toolbox to track and decode all acquired satellite signals, including those currently intended to be used in future navigation systems, such as the Galileo test signals transmitted by the GIOVE satellites orbiting the Earth. In addition to the developed receiver toolbox, three novel algorithms were also designed to handle weak signals, multipath, and multiple access interference in GNSScope. The Mirrored Channel Mitigation Technique, based on the successive and parallel interference cancellation techniques, reduces the hardware complexity of the interference mitigation process by utilizing the local code and carrier replicas generated in the tracking channels, resulting in a reduction in hardware resources proportional to the number of received strong signals. The Trigonometric Interference Cancellation Technique, used in cross-correlation interference mitigation, exploits the underlying mathematical expressions to simplify the interference removal process, resulting in reduced complexity and execution times by reducing the number of operations by 25% per tracking channel. The Split Chip Summation Technique, based on the binary valued signal modulation compression technique, enhances the amount of information captured from compressing the signal to reveal specific filtering effects on the positive and negative polarity chips of the spreading code. Simulation case studies generated entirely using the GNSScope toolbox will be used throughout the thesis to demonstrate the effectiveness of the novel techniques developed over the course of the research, and the results will be compared to those obtained from other techniques reported in the literature

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces