Download or read book Parameter Optimization of a Radar Detection and Tracking System written by McLane, P. J and published by Kingston, Ont. : Queen's University, Department of Electrical Engineering. This book was released on 1981 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimization of Detection Tracking and Transmitter Parameters for a Radar Detection and Tracing System written by and published by . This book was released on 1982 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Radar Tracking Systems written by George Biernson and published by Wiley-Interscience. This book was released on 1990-04-03 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a state-of-the-art presentation of optimal radar tracking systems based on the sophisticated Altair radar, which uses Kalman filtering to perform optimal long-range tracking of ballistic missile warheads. This engineering example offers a means for explaining Kalman filter theory and many other technical issues critical to the design of a modern optimal radar tracking system, all in arelatively simple manner. Material includes discussion of feedback control, modulation and demodulation of signals, digital sampled-data systems, digital computer simulation, statistical analysis of random signals, detection and tracking processes in a radar system. This study of Altair features a considerable amount of detail concerning the operation of a complex electronic system, thereby presenting a study that is unusual in the unclassified literature.

Download or read book Knowledge Based Radar Detection Tracking and Classification written by Fulvio Gini and published by John Wiley & Sons. This book was released on 2008-06-09 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book MIMO Radar Signal Processing written by Jian Li and published by John Wiley & Sons. This book was released on 2008-10-10 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book to present a systematic and coherent picture of MIMO radars Due to its potential to improve target detection and discrimination capability, Multiple-Input and Multiple-Output (MIMO) radar has generated significant attention and widespread interest in academia, industry, government labs, and funding agencies. This important new work fills the need for a comprehensive treatment of this emerging field. Edited and authored by leading researchers in the field of MIMO radar research, this book introduces recent developments in the area of MIMO radar to stimulate new concepts, theories, and applications of the topic, and to foster further cross-fertilization of ideas with MIMO communications. Topical coverage includes: Adaptive MIMO radar Beampattern analysis and optimization for MIMO radar MIMO radar for target detection, parameter estimation, tracking,association, and recognition MIMO radar prototypes and measurements Space-time codes for MIMO radar Statistical MIMO radar Waveform design for MIMO radar Written in an easy-to-follow tutorial style, MIMO Radar Signal Processing serves as an excellent course book for graduate students and a valuable reference for researchers in academia and industry.

Download or read book Least Mean square error Adaptation of Parameters in Radar Detection and Tracking Systems written by McLane, P. J and published by Kingston, Ont. : Department of Electrical Engineering, Queen's University. This book was released on 1980 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Polarimetric Radar Signal Processing written by Augusto Aubry and published by SciTech Publishing. This book was released on 2023-02-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of some advanced techniques and technologies developed for polarimetric radars. It covers how the systems are designed to meet challenging performance requirements and also covers some of the most challenging application fields.

Download or read book Signal Processing in Radar Systems written by Vyacheslav Tuzlukov and published by CRC Press. This book was released on 2017-12-19 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential task in radar systems is to find an appropriate solution to the problems related to robust signal processing and the definition of signal parameters. Signal Processing in Radar Systems addresses robust signal processing problems in complex radar systems and digital signal processing subsystems. It also tackles the important issue of defining signal parameters. The book presents problems related to traditional methods of synthesis and analysis of the main digital signal processing operations. It also examines problems related to modern methods of robust signal processing in noise, with a focus on the generalized approach to signal processing in noise under coherent filtering. In addition, the book puts forth a new problem statement and new methods to solve problems of adaptation and control by functioning processes. Taking a systems approach to designing complex radar systems, it offers readers guidance in solving optimization problems. Organized into three parts, the book first discusses the main design principles of the modern robust digital signal processing algorithms used in complex radar systems. The second part covers the main principles of computer system design for these algorithms and provides real-world examples of systems. The third part deals with experimental measurements of the main statistical parameters of stochastic processes. It also defines their estimations for robust signal processing in complex radar systems. Written by an internationally recognized professor and expert in signal processing, this book summarizes investigations carried out over the past 30 years. It supplies practitioners, researchers, and students with general principles for designing the robust digital signal processing algorithms employed by complex radar systems.

Download or read book Signal Processing in Radar Systems written by Vyacheslav Tuzlukov and published by CRC Press. This book was released on 2012-09-24 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential task in radar systems is to find an appropriate solution to the problems related to robust signal processing and the definition of signal parameters. Signal Processing in Radar Systems addresses robust signal processing problems in complex radar systems and digital signal processing subsystems. It also tackles the important issue of defining signal parameters. The book presents problems related to traditional methods of synthesis and analysis of the main digital signal processing operations. It also examines problems related to modern methods of robust signal processing in noise, with a focus on the generalized approach to signal processing in noise under coherent filtering. In addition, the book puts forth a new problem statement and new methods to solve problems of adaptation and control by functioning processes. Taking a systems approach to designing complex radar systems, it offers readers guidance in solving optimization problems. Organized into three parts, the book first discusses the main design principles of the modern robust digital signal processing algorithms used in complex radar systems. The second part covers the main principles of computer system design for these algorithms and provides real-world examples of systems. The third part deals with experimental measurements of the main statistical parameters of stochastic processes. It also defines their estimations for robust signal processing in complex radar systems. Written by an internationally recognized professor and expert in signal processing, this book summarizes investigations carried out over the past 30 years. It supplies practitioners, researchers, and students with general principles for designing the robust digital signal processing algorithms employed by complex radar systems.

Download or read book Sparse Representations for Radar with MATLAB Examples written by Peter Knee and published by Springer Nature. This book was released on 2022-05-31 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the field of sparse representations is relatively new, research activities in academic and industrial research labs are already producing encouraging results. The sparse signal or parameter model motivated several researchers and practitioners to explore high complexity/wide bandwidth applications such as Digital TV, MRI processing, and certain defense applications. The potential signal processing advancements in this area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB® examples to emphasize the practical implementations both inside and outside the radar field. Table of Contents: Radar Systems: A Signal Processing Perspective / Introduction to Sparse Representations / Dimensionality Reduction / Radar Signal Processing Fundamentals / Sparse Representations in Radar

Download or read book Methods Techniques in Deep Learning written by Avik Santra and published by John Wiley & Sons. This book was released on 2022-12-13 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces multiple state-of-the-art deep learning architectures for mmwave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmwave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrate how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmwave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmwave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science and AI.

Download or read book Cognitive Radar Detection in Nonstationary Environments and Target Tracking written by Yijian Xiang and published by . This book was released on 2020 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, changes in the nonstationary environment can perturb the parameters of the clutter distribution or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which can rapidly recognize the nonstationarity, accurately learn the new characteristics of the environment, and adaptively update the detector. To achieve this cognition, we propose a unifying framework that integrates three functions: (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by using kernel density estimation (KDE) methods and similarity measures (iii) adaptive target detection by automatically modifying the likelihood-ratio test and the corresponding detection threshold. We also conduct extensive numerical experiments to show the merits of the proposed method compared to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case.For target tracking, with remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. Accordingly, we propose a general framework for single target tracking in cognitive networks of radars, including joint consideration of waveform design, path planning, and radar selection. We formulate the tracking procedure using the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE). This procedure includes two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, we use an illustrative example to introduce a specific scenario in 2-D space. Simulation results based on this scenario demonstrate that the proposed framework can accurately track the target under the management of a network of radars.

Download or read book Advancing Fully Adaptive Radar Concepts for Real time Parameter Adaptation and Decision Making written by Peter John-Baptiste (Jr) and published by . This book was released on 2020 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Harmony Search Algorithm written by Joong Hoon Kim and published by Springer. This book was released on 2015-08-08 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community. This volume, the proceedings of the 2nd International Conference on Harmony Search Algorithm 2015 (ICHSA 2015), brings together contributions describing the latest developments in the field of soft computing with a special focus on HSA techniques. It includes coverage of new methods that have potentially immense application in various fields. Contributed articles cover aspects of the following topics related to the Harmony Search Algorithm: analytical studies; improved, hybrid and multi-objective variants; parameter tuning; and large-scale applications. The book also contains papers discussing recent advances on the following topics: genetic algorithms; evolutionary strategies; the firefly algorithm and cuckoo search; particle swarm optimization and ant colony optimization; simulated annealing; and local search techniques. This book offers a valuable snapshot of the current status of the Harmony Search Algorithm and related techniques, and will be a useful reference for practising researchers and advanced students in computer science and engineering.

Download or read book Methods and Techniques in Deep Learning written by Avik Santra and published by John Wiley & Sons. This book was released on 2022-11-21 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

Download or read book Battelle Technical Review written by Battelle Memorial Institute and published by . This book was released on 1968 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Radar Resource Management written by Peter Moo and published by Academic Press. This book was released on 2015-07-23 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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