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Low probability of intercept (LPI) is that property of an emitter that because of its low power, wide bandwidth, frequency variability, or other design attributes, makes it difficult to be detected or identified by means of passive intercept devices such as radar warning, electronic support and electronic intelligence receivers, In order to detect LPI radar waveforms new signal processing techniques are required This thesis first develops a MATLAB toolbox to generate important types of LPI waveforms based on frequency and phase modulation The power spectral density and the periodic ambiguity function are examined for each waveforms These signals are then used to test a novel signal processing technique that detects the waveforms parameters and classifies the intercepted signal in various degrees of noise, The technique is based on the use of parallel filter (sub-band) arrays and higher order statistics (third- order cumulant estimator) Each sub-band signal is treated individually and is followed by the third-order estimator in order to suppress any symmetrical noise that might be present, The significance of this technique is that it separates the LPI waveforms in small frequency bands, providing a detailed time-frequency description of the unknown signal, Finally, the resulting output matrix is processed by a feature extraction routine to detect the waveforms parameters Identification of the signal is based on the modulation parameters detected,
"This comprehensive book presents LPI radar design essentials, including ambiguity analysis of LPI waveforms, FMCW radar, and phase-shift and frequency-shift keying techniques. Moreover, you find details on new OTHR modulation schemes, noise radar, and spatial multiple-input multiple-output (MIMO) systems. The book explores autonomous non-linear classification signal processing algorithms for identifying LPI modulations. It also demonstrates four intercept receiver signal processing techniques for LPI radar detection that helps you determine which time-frequency, bi-frequency technique best suits any LPI modulation of interest."--Publisher.
This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.
The focus of the study is on combat systems operating at medium and low altitudes, which pose challenges different from intelligence, surveillance, and reconnaissance (ISR) platforms operating at high altitude. Medium- and low-altitude platforms are closer to jammers and signals intelligence receivers, so an adversary might more readily intercept or jam signals. The platforms' low observability can be compromised if they transmit large amounts of data, increasing their chance of detection. After discussing data requirements and threats and examining the current communications programs and shortfalls, the analysis finds that there is no one solution for all situations and platforms. A combination of options will be needed to ensure a reliable and robust communications link. These options will vary depending on altitude, range, data rate, and threat. Although communications does not appear to be a limiting factor for future ISR forces, programmatic action will be required to develop the necessary systems and the costs could be high.
Principles of Modern Radar: Basic Principles is a comprehensive text for courses in radar systems and technology, a professional training textbook for formal in-house courses and for new hires; a reference for ongoing study following a radar short course and a self-study and professional reference book.
Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.
Advances in digital signal processing algorithms and computer technology have combined to produce real-time systems with capabilities far beyond those of just few years ago. Nonlinear, adaptive methods for signal processing have emerged to provide better array gain performance, however, they lack the robustness of conventional algorithms. The chall
Based on the popular Artech House classic, Digital Communication Systems Engineering with Software-Defined Radio, this book provides a practical approach to quickly learning the software-defined radio (SDR) concepts needed for work in the field. This up-to-date volume guides readers on how to quickly prototype wireless designs using SDR for real-world testing and experimentation. This book explores advanced wireless communication techniques such as OFDM, LTE, WLA, and hardware targeting. Readers will gain an understanding of the core concepts behind wireless hardware, such as the radio frequency front-end, analog-to-digital and digital-to-analog converters, as well as various processing technologies. Moreover, this volume includes chapters on timing estimation, matched filtering, frame synchronization message decoding, and source coding. The orthogonal frequency division multiplexing is explained and details about HDL code generation and deployment are provided. The book concludes with coverage of the WLAN toolbox with OFDM beacon reception and the LTE toolbox with downlink reception. Multiple case studies are provided throughout the book. Both MATLAB and Simulink source code are included to assist readers with their projects in the field.
Discrete Signals and Inverse Problems examines fundamental concepts necessary to engineers and scientists working with discrete signal processing and inverse problem solving, and places emphasis on the clear understanding of algorithms within the context of application needs. Based on the original ‘Introduction to Discrete Signals and Inverse Problems in Civil Engineering’, this expanded and enriched version: combines discrete signal processing and inverse problem solving in one book covers the most versatile tools that are needed to process engineering and scientific data presents step-by-step ‘implementation procedures’ for the most relevant algorithms provides instructive figures, solved examples and insightful exercises Discrete Signals and Inverse Problems is essential reading for experimental researchers and practicing engineers in civil, mechanical and electrical engineering, non-destructive testing and instrumentation. This book is also an excellent reference for advanced undergraduate students and graduate students in engineering and science.