Ba-Ngu Vo – författare

A unique guide to the state of the art of tracking, classification, and sensor managementThis book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications.Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include: An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solvingA succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and trackingA state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification modelsNew structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and managementCoverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor managementInsight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR)With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.

Optimum envelope-constrained filter design is concerned with time-domain synthesis of a filter such that its response to a specific input signal stays within prescribed upper and lower bounds, while minimizing the impact of input noise on the filter output or the impact of the shaped signal on other systems depending on the application. In many practical applications, such as in TV channel equalization, digital transmission, and pulse compression applied to radar, sonar and detection, the soft least square approach, which attempts to match the output waveform with a specific desired pulse, is not the most suitable one. Instead, it becomes necessary to ensure that the response stays within the hard envelope constraints defined by a set of continuous inequality constraints. The main advantage of using the hard envelope-constrained filter formulation is that it admits a whole set of allowable outputs. From this set one can then choose the one which results in the minimization of a cost function appropriate to the application at hand. The signal shaping problems so formulated are semi-infinite optimization problems.This monograph presents in a unified manner results that have been generated over the past several years and are scattered in the research literature. The material covered in the monograph includes problem formulation, numerical optimization algorithms, filter robustness issues and practical examples of the application of envelope constrained filter design. Postgraduate students, researchers in optimization and telecommunications engineering, and applied mathematicians should find this work useful.

Explores advanced tracking, sensor management, and distributed fusion for modern engineering applicationsAdvances in statistical signal processing, sensor networks, and control theory have created a pressing need for a unified resource that synthesizes the latest developments in nonlinear filtering, multitarget tracking, sensor management, and distributed fusion. Advanced Techniques in Tracking and Sensor Management: Theory and Applications provides a carefully structured overview of the state-of-the-art algorithms, mathematical frameworks, and real-world applications that define the current landscape of tracking and sensor research.Unifying four decades of research, the book is divided into four parts, each focusing on a central domain: nonlinear filtering, multi-sensor and multitarget tracking, sensor management, and distributed fusion. Topics include particle filtering methods for constrained systems, random finite set–based multitarget tracking, advanced scheduling and resource allocation strategies for sensor management, and consensus-based techniques for distributed fusion. These areas are covered with both theoretical rigor and practical clarity, highlighting their application to radar systems, intelligence and surveillance operations, and space situational awareness. Each in-depth chapter, authored by leading international experts, balances mathematical exposition with illustrative applications to make complex concepts accessible to engineers, scientists, and graduate students.Both a textbook for advanced study and a reference for practitioners working at the forefront of defense, aerospace, and autonomous systems, Advanced Techniques in Tracking and Sensor Management: Features cutting-edge algorithms for space object tracking, space debris detection, and SLAM with finite set statisticsProvides rigorous treatments of Bayesian filtering and multitarget estimation methods under cluttered, and uncertain environmentsIntroduces new approaches to multi-agile satellite scheduling and robust power allocation for MIMO radar systemsExplores distributed state estimation and multi-object density fusion across sensor networksAdvanced Techniques in Tracking and Sensor Management: Theory and Applications is essential reading for graduate-level courses in statistical signal processing, target tracking, and sensor management within electrical and computer engineering programs. It is also a key reference for engineers and researchers working in aerospace, defense, autonomous systems, and surveillance industries.

Optimum envelope-constrained filter design is concerned with time-domain synthesis of a filter such that its response to a specific input signal stays within prescribed upper and lower bounds, while minimizing the impact of input noise on the filter output or the impact of the shaped signal on other systems depending on the application. In many practical applications, such as in TV channel equalization, digital transmission, and pulse compression applied to radar, sonar and detection, the soft least square approach, which attempts to match the output waveform with a specific desired pulse, is not the most suitable one. Instead, it becomes necessary to ensure that the response stays within the hard envelope constraints defined by a set of continuous inequality constraints. The main advantage of using the hard envelope-constrained filter formulation is that it admits a whole set of allowable outputs. From this set one can then choose the one which results in the minimization of a cost function appropriate to the application at hand. The signal shaping problems so formulated are semi-infinite optimization problems.This monograph presents in a unified manner results that have been generated over the past several years and are scattered in the research literature. The material covered in the monograph includes problem formulation, numerical optimization algorithms, filter robustness issues and practical examples of the application of envelope constrained filter design. Audience: Postgraduate students, researchers in optimization and telecommunications engineering, and applied mathematicians.

Focusing on autonomous robotic applications, this cutting-edge resource offers a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. Engineers and researchers may find detailed coverage of Simultaneous Localization and Map Building (SLAM) - an area referred to as the "Holy Grail" of autonomous robotics research.

The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.