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Radar Resource Management (RRM) is vital for optimizing the performance of modern phased array radars, which are the primary sensor for aircraft, ships, and land platforms. Adaptive Radar Resource Management gives an introduction to radar resource management (RRM), presenting a clear overview of different approaches and techniques, making it very suitable for radar practitioners and researchers in industry and universities. Coverage includes: RRM’s role in optimizing the performance of modern phased array radars The advantages of adaptivity in implementing RRM The role that modelling and simulation plays in evaluating RRM performance Description of the simulation tool Adapt_MFR Detailed descriptions and performance results for specific adaptive RRM techniques The only book fully dedicated to adaptive RRM A comprehensive treatment of phased array radars and RRM, including task prioritization, radar scheduling, and adaptive track update rates Provides detailed knowledge of specific RRM techniques and their performance
Discover the technology for the next generation of radar systems Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems. The book begins with a chapter introducing the concept of Knowledge Based (KB) radar. The remaining nine chapters focus on current developments and recent applications of KB concepts to specific radar functions. Among the key topics explored are: Fundamentals of relevant KB techniques KB solutions as they apply to the general radar problem KBS applications for the constant false-alarm rate processor KB control for space-time adaptive processing KB techniques applied to existing radar systems Integrated end-to-end radar signals Data processing with overarching KB control All chapters are self-contained, enabling readers to focus on those topics of greatest interest. Each one begins with introductory remarks, moves on to detailed discussions and analysis, and ends with a list of references. Throughout the presentation, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability. Moreover, the authors forecast the impact of KB technology on future systems, including important civilian, military, and homeland defense applications. With chapters contributed by leading international researchers and pioneers in the field, this text is recommended for both students and professionals in radar and sonar detection, tracking, and classification and radar resource management.
This book provides a comprehensive and systematic framework for the design of adaptive architectures, which take advantage of the available a priori information to enhance the detection performance. Moreover, this framework also provides guidelines to develop decision schemes capable of estimating the target position within the range bin. To this end, the readers are driven step-by-step towards those aspects that have to be accounted for at the design stage, starting from the exploitation of system and/or environment information up to the use of target energy leakage (energy spillover), which allows inferring on the target position within the range cell under test.In addition to design issues, this book presents an extensive number of illustrative examples based upon both simulated and real-recorded data. Moreover, the performance analysis is enriched by considerations about the trade-off between performances and computational requirements.Finally, this book could be a valuable resource for PhD students, researchers, professors, and, more generally, engineers working on statistical signal processing and its applications to radar systems.
A multifunction radar is a modern system that is capable of performing multiple radar functions simultaneously, such as surveillance, target tracking, weather monitoring, etc. This class of radar, multifunction radar (MFR), requires a control function, resource manager, to balance the use of its finite resources among the multiple functions. Hence, the multifunction radar performance is limited by the resource manager intelligent behavior to allocate the system resources. This thesis addresses the challenge of using radar resource management (RRM) to provide the attention element of cognition for radar systems. A recursive form of the radar resource allocation problem is proposed that uses prior knowledge about the target to refine the radar parameters every time the radar revisiting the target. This approach enables the radar system to be more sensitive to the change in the environment and therefore adapt its parameters accordingly.
A preliminary simulation model of the search mode of a phased array radar has been developed. The basic components are a search strategy for the radar, an evasion policy for the target, and a simple radar environment. At present there is no attempt at adaptive modification of either component. A particular search strategy has been chosen. There is provision in the simulator to modify the procedure, however. The evasion policy of the target is one of changing course at random times with random deviations. It is planned to develop the tracking package next. The approach will be through a Kalman-Bucy filter.
This highly-anticipated second edition of the bestselling Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach, the first book on the subject, provides up-to-the-minute advances in the field of cognitive radar (CR). Adaptive waveform methods are discussed in detail, along with optimum resource allocation and radar scheduling. Chronicling the field of cognitive radar (CR), this cutting-edge resource provides an accessible introduction to the theory and applications of CR, and presents a comprehensive overview of the latest developments in this emerging area. It covers important breakthroughs in advanced radar systems, and offers new and powerful methods for combating difficult clutter environments. You find details on specific algorithmic and real-time high-performance embedded computing (HPEC) architectures. This practical book is supported with numerous examples that clarify key topics, and includes more than 370 equations.
Chronicling the new field of cognitive radar (CR), this cutting-edge resource provides an accessible introduction to the theory and applications of CR, and presents a comprehensive overview of the latest developments in this emerging area. The first book on the subject, Cognitive Radar covers important breakthroughs in advanced radar systems, and offers new and powerful methods for combating difficult clutter environments. You find details on specific algorithmic and real-time high-performance embedded computing (HPEC) architectures. This practical book is supported with numerous examples that clarify key topics, and includes more than 370 equations.
Cognitive or Fully Adaptive Radar (FAR) is an area of research that is inspired by biological systems and focuses on developing a radar system capable of autonomously adapting its characteristics to achieve a variety of different tasks such as improved environment sensing and spectral agility. The FAR framework implements a dynamic feedback loop (sense, learn, adapt) within a software defined radar (SDR) system and the environment that emulates a Perception Action Cycle (PAC). The implementation of the FAR framework on SDRs relies on solver-based optimization techniques for their action selection. However, with the increase of optimization complexity, there becomes a heavy impact on time to solution convergence, which limits real-time experimentation. Additionally, many "cognitive radars" lack a memory component resulting in repetitive optimization routines for similar/familiar perceptions. Using an existing model of the FAR framework, a neural network inspired refinement is made. Through the use of neural networks, a subset of machine learning, and other machine learning concepts, a substitution is made for the solver-based optimization component for the FAR framework applied to single target tracking. Static feedforward neural networks and dynamic neural networks were trained and implemented in both a simulation and experimentation environment. Performance comparisons between the neural network and the solver-based optimization approaches show that the static neural network based approach had faster runtimes which lead to more perceptions and sometimes better performance through lower resource consumption. A comparison between the simulation results of the static feed-forward neural network, the dynamic recurrent neural network, and the solver is also made. These comparisons further support the notion of neural networks being able to provide a memory component for cognitive radar through the incorporation of learning, moving toward truly cognitive radars. Additional research was also performed to further show the advantages of neural networks in radar applications of rapid waveform generation. The FAR framework is also extended from the single-target tracking FAR framework to a multiple target tracking implementation. The multi-target implementation of the FAR framework displays the benefits of adaptive radar techniques for multiple target environments where complexity is increased due to the increased number of targets present in the scene as well as the need to resolve all targets. Refinements and additions were made to the existing cost functions and detection/tracking frameworks due to the multiple target environment. Experimental and simulated results demonstrate the benefit of the FAR framework by enabling a robust adaptive algorithm that results in improved tracking and efficient resource management for a multiple target environment. In addition to this, the Hierarchical Fully Adaptive Radar (HFAR) framework was also applied to the problem of resource allocation for a system needing to perform multiple tasks. The Hierarchical Fully Adaptive Radar for Task Flexibility (HFAR-TF)/Autonomous Decision Making (ADM) work applies the HFAR framework to a system needing to engage in balancing multiple tasks: target tracking, classification and target intent discernment ("friend", "possible foe", and "foe"). The goal of this Ph.D. is to combine these objectives to form a basis for establishing a method of improving current cognitive radar systems. This is done by fusing machine learning concepts and fully adaptive radar theory, to enable real-time operation of truly cognitive radars, while also advancing adaptive radar concepts to new applications.
"This paper considers whether coordinated radar resource management (RRM), which exploits the sharing of tracking and detection data between radars, enhances performance compared to Independent RRM"--Abstract.
This book shows you how to adopt data-driven techniques for the problem of radar detection, both per se and in combination with model-based approaches. In particular, the focus is on space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. It is a handy, concise reference for many classic (model-based) adaptive radar detection schemes as well as the most popular machine learning techniques (including deep neural networks) and helps you identify suitable data-driven approaches for radar detection and the main related issues. You’ll learn how data-driven tools relate to, and can be coupled or hybridized with, traditional adaptive detection statistics; understand fundamental concepts, schemes, and algorithms from statistical learning, classification, and neural networks domains. The book also walks you through how these concepts and schemes have been adapted for the problem of radar detection in the literature and provides you with a methodological guide for the design, illustrating different possible strategies. You’ll be equipped to develop a unified view, under which you can exploit the new possibilities of the data-driven approach even using simulated data. This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.