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A rigorous introduction to the theory and applications of state estimation and association, an important area in aerospace, electronics, and defense industries. Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications. Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.
Detailed descriptions of detection, direction-finding, and signal-estimation methods, using consistent formalisms and notation, emphasizing HF antenna array sensing applications. Adaptive antenna array technology encompasses many powerful interference suppression approaches that exploit spatial differences among signals reaching a radio receiver system. Today, worldwide propagation phenomenology occurring in the High Frequency (HF) radio regime has made such interference common. In this book, Jay Sklar, a longtime researcher at MIT Lincoln Laboratory, presents detailed descriptions of detection, direction-finding, and signal-estimation methods applicable at HF, using consistent formalisms and notation. Modern electronic system technology has made many of these techniques affordable and practical; the goal of the book is to offer practicing engineers a comprehensive and self-contained reference that will encourage more widespread application of these approaches. The book is based on the author's thirty years of managing MIT Lincoln Laboratory work on the application of adaptive antenna array technologies to the sensing of HF communication signals. After an overview of HF propagation phenomenology, communication signal formats, and HF receiver architectural approaches, Sklar describes the HF propagation environment in more detail; introduces important modulation approaches and signaling protocols used at HF; discusses HF receiver system architectural features; and addresses signal processor architecture and its implementation. He then presents the technical foundation for the book: the vector model for a signal received at an adaptive array antenna. He follows this with discussions of actual signal processing techniques for detection and direction finding, including specific direction-finding algorithms; geolocation techniques; and signal estimation.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
The development of deep space surveillance technology and its later application to near-Earth surveillance, covering work at Lincoln Laboratory from 1970 to 2000. In the 1950s, the United States and the Soviet Union raced to develop space-based intelligence gathering capability. The Soviets succeeded first, with SPUTNIK I in 1957. The United States began to monitor the growing Soviet space presence by developing technology for the detection and tracking of man-made resident space objects (RSOs) in near-Earth orbit. In 1972, the Soviet Union launched a satellite into deep space orbit, and the U.S. government called on MIT Lincoln Laboratory to develop deep space surveillance technology. This book describes these developments, as well as the later application of deep space surveillance technology to near-Earth surveillance, covering work at Lincoln Laboratory on space surveillance from 1970 to 2000. The contributors, all key participants in developing these technologies, discuss topics that include narrow beam, narrow bandwidth radar for deep surveillance; wide bandwidth radar for RSO monitoring; ground-based electro-optical deep space surveillance and its adaptation for space-based surveillance; radar as the means of real-time search and discovery techniques; methods of analyses of signature data from narrow bandwidth radars; and the collision hazard for satellites in geosynchronous orbit, stemming initially from the failure of TELSTAR 401. They also describe some unintended byproducts of this pioneering work, including the use of optical space surveillance techniques for near-Earth asteroid detection. Contributors Rick Abbott, Robert Bergemann, E.M. Gaposchkin, Israel Kupiec, Richard Lambour, Antonio F. Pensa, Eugene Rork, Jayant Sharma, Craig Solodyna, Ramaswamy Sridharan, J. Scott Stuart, George Zollinger
The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.
A guide to defense systems analysis by experts who have worked on systems that range from air defense to space defense. The Department of Defense and the military continually grapple with complex scientific, engineering, and technological problems. Defense systems analysis offers a way to reach a clearer understanding of how to approach and think about complex problems. It guides analysts in defining the question, capturing previous work in the area, assessing the principal issues, and understanding how they are linked. The goal of defense systems analysis is not necessarily to find a particular solution but to provide a roadmap to a solution, or an understanding of the relative value of alternative solutions. In this book, experts in the field—all of them with more than twenty years of experience—offer insights, advice, and concrete examples to guide practitioners in the art of defense systems analysis. The book describes general issues in systems analysis and analysis protocols in specific defense areas. It offers a useful overview of the process, a discussion of different venues, and practical advice running a study and reporting its results. It discusses red teaming (the search for vulnerabilities that might be exploited by an adversary) and its complement, blue teaming (the search for solutions to known shortcomings). It describes real-world defense systems analysis for both traditional and nontraditional areas, including air defense and ballistic missile defense systems, bioterrorism defense, space warfare, and interplanetary communications. Perspectives on Defense Systems Analysis is a very readable resource for analysts and engineers in industry, government, and research.
Why the United States lags behind other industrialized countries in sharing the benefits of innovation with workers and how we can remedy the problem. The United States has too many low-quality, low-wage jobs. Every country has its share, but those in the United States are especially poorly paid and often without benefits. Meanwhile, overall productivity increases steadily and new technology has transformed large parts of the economy, enhancing the skills and paychecks of higher paid knowledge workers. What’s wrong with this picture? Why have so many workers benefited so little from decades of growth? The Work of the Future shows that technology is neither the problem nor the solution. We can build better jobs if we create institutions that leverage technological innovation and also support workers though long cycles of technological transformation. Building on findings from the multiyear MIT Task Force on the Work of the Future, the book argues that we must foster institutional innovations that complement technological change. Skills programs that emphasize work-based and hybrid learning (in person and online), for example, empower workers to become and remain productive in a continuously evolving workplace. Industries fueled by new technology that augments workers can supply good jobs, and federal investment in R&D can help make these industries worker-friendly. We must act to ensure that the labor market of the future offers benefits, opportunity, and a measure of economic security to all.
This volume presents papers on the topics covered at the National Academy of Engineering's 2018 US Frontiers of Engineering Symposium. Every year the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and innovations in selected areas. The 2018 symposium was held September 5-7 and hosted by MIT Lincoln Laboratory in Lexington, Massachusetts. The intent of this book is to convey the excitement of this unique meeting and to highlight innovative developments in engineering research and technical work.
Tells the story of a laser technology that will have a big impact on society and the brilliant innovators responsible for its developmentLidar--a technology evolved from radar, but using laser light rather than microwaves--has found an astounding range of applications, none more prominent than its crucial role in enabling self-driving cars. This accessible introduction to a fascinating and increasingly vital technology focuses on the engaging human stories of lidar's innovators as they advance and adapt it to better understand air, water, ice and Earth - not to mention mapping Mars and Mercury, spotting incoming nuclear warheads, and avoiding pedestrians and cyclists on city streets.Award-winning science writer Todd Neff invites readers behind the scenes to meet some of the great innovators who have explored and expanded the uses of this amazing technology: people like MIT scientist Louis Smullin, whose lidar bounced light off the moon soon after the laser's invention; Allan Carswell, who plumbed the shallows of Lake Erie en route to developing the aerial lidar now essential for coastal mapping and hurricane damage assessment; Red Whittaker, the field robotics pioneer who was putting lidar on his autonomous contraptions as early as the 1980s; and David Hall, whose laser sombrero on a Toyota Tundra gave birth to modern automotive lidar.These are just some of the stories Neff tells before looking ahead to a future that could bring lidar to unpiloted air taxis, to the contaminated pipes of the U.S. nuclear weapons complex, and to satellites capable of pinpointing greenhouse gas sources from orbit. As the author makes clear, the sky is no limit with lidar, which promises to make our world safer, healthier, and vastly more interesting.