Jitendra R. Raol

Intelligent Estimation: A Soft Computing Paradigm presents a unified treatment for a new paradigm of soft computing with AI-based approaches as applicable to system identification, parameter estimation, and filtering: Neuro-Fuzzy-GA Based System Identification, Parameter Estimation, and Filtering, known succinctly as Intelligent Estimation (IE). Offering a thorough understanding of soft computing-based estimation concepts and theory, the book discusses a modeling-control-system approach with numerous practical applications in solving mathematical modeling problems for industrial and aerospace engineering systems. It delves into theory, concepts and various ramifications of neural networks, fuzzy logic, and genetic algorithms for modeling, system identification, noise filtering, state estimation (including filtering), and parameter estimation. This book is intended for upper-level undergraduate and graduate engineering students studying soft computing, intelligent systems, and advanced control systems in industry applications. Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.

Situation Assessment in Aviation focuses on new aspects of soft computing technologies for the evaluation and assessment of situations in aviation scenarios. It considers technologies emerging from multisensory data fusion (MSDF), Bayesian networks (BN), and fuzzy logic (FL) to assist pilots in their decision-making. Studying MSDF, BN, and FL from the perspective of their applications to the problem of situation assessment, the book discusses the development of certain soft technologies that can be further used for devising more sophisticated technologies for a pilot's decision-making when performing certain tasks: airplane monitoring, pair formation, attack, and threat. It explains the concepts of situation awareness, data fusion, decision fusion, Bayesian networks, fuzzy logic type 1, and interval type 2 fuzzy logic. The book also presents a hybrid technique by using BN and FL and a unique approach to the problem of situation assessment, beyond visual range and air-to-air combat, by utilizing building blocks of artificial intelligence (AI) for the future development of more advanced automated systems, especially using commercial software. The book is intended for aerospace R&D engineers, systems engineers, aeronautical engineers, and aviation training professionals. It will also be useful for aerospace and electrical engineering students taking courses in Air Traffic Management, Aviation Management, Aviation Operations, and Aviation Safety Systems.

Flight Mechanics Modeling and Analysis comprehensively covers flight mechanics and flight dynamics using a systems approach. This book focuses on applied mathematics and control theory in its discussion of flight mechanics to build a strong foundation for solving design and control problems in the areas of flight simulation and flight data analysis. The second edition has been expanded to include two new chapters and coverage of aeroservoelastic topics and engineering mechanics, presenting more concepts of flight control and aircraft parameter estimation. This book is intended for senior undergraduate aerospace students taking Aircraft Mechanics, Flight Dynamics & Controls, and Flight Mechanics courses. It will also be of interest to research students and R&D project-scientists of the same disciplines. Including end-of-chapter exercises and illustrative examples with a MATLAB®-based approach, this book also includes a Solutions Manual and Figure Slides for adopting instructors. Features: Covers flight mechanics, flight simulation, flight testing, flight control, and aeroservoelasticity Features artificial neural network- and fuzzy logic-based aspects in modeling and analysis of flight mechanics systems: aircraft parameter estimation and reconfiguration of control Focuses on a systems-based approach Includes two new chapters, numerical simulation examples with MATLAB®-based implementations, and end-of-chapter exercises Includes a Solutions Manual and Figure Slides for adopting instructors

Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.This new edition offers updated chapters alongside four new chapters that are based on recent research carried out by the authors, including topics on machine learning techniques, target localization using a network of 2D ground radar, thermal imaging sensors for multi-target angle-only tracking, and multi-sensor data fusion for a single platform and team platforms. This book also covers major mathematical expressions, formulae and equations, and, where feasible, their derivations. It discusses signed distance function concepts, DF models and architectures, aspects and methods of types 1 and 2 fuzzy logics, and related practical applications. In addition, the authors cover soft computing paradigms that are finding increasing applications in multi-sensory DF approaches and applications.This text is geared toward researchers, scientists, teachers, and practicing engineers interested in and working in the multi-sensor data fusion area.

This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.Features:Provides comprehensive coverage of all issues related to parameter and state estimation.Discusses advanced topics related to Kalman filter, stability analysis, image centroid tracking and neural networks for parameter estimation.Explores convergence and stability results for the discussed methods.Reviews the estimation of parameters in linear/nonlinear models, and distributed fitting.Includes MATLAB®-based illustrative examples, and exercises.This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.

Mathematical Modelling of Aerospace Dynamic Systems with Practical Applications provides mathematical models for several aerospace dynamic systems: aircraft, rotorcraft, missiles, unmanned aerial vehicles (UAVs), mini air vehicles (MAVs), autonomous underwater vehicles (AUWVs), and satellite-coordinate systems.Presenting the use of mathematical models for analysis, prediction, and control of these systems, this book discusses numerous applications in aircraft/helicopter parameter estimation, guidance and navigation of these vehicles, underwater object search, aerial terrain mapping, and satellite orbit determination. It explains path planning with obstacle avoidance, object occlusion detection and tracking, and multisensory target tracking and sensor data fusion.This book is intended for senior undergraduate mechanical and aerospace engineering students taking courses in aerospace systems and dynamics, flight dynamics and control, and dynamical systems and estimation.Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.

Situation Assessment in Aviation focuses on new aspects of soft computing technologies for the evaluation and assessment of situations in aviation scenarios. It considers technologies emerging from multisensory data fusion (MSDF), Bayesian networks (BN), and fuzzy logic (FL) to assist pilots in their decision-making.Studying MSDF, BN, and FL from the perspective of their applications to the problem of situation assessment, the book discusses the development of certain soft technologies that can be further used for devising more sophisticated technologies for a pilot's decision-making when performing certain tasks: airplane monitoring, pair formation, attack, and threat. It explains the concepts of situation awareness, data fusion, decision fusion, Bayesian networks, fuzzy logic type 1, and interval type 2 fuzzy logic. The book also presents a hybrid technique by using BN and FL and a unique approach to the problem of situation assessment, beyond visual range and air-to-air combat, by utilizing building blocks of artificial intelligence (AI) for the future development of more advanced automated systems, especially using commercial software.The book is intended for aerospace R&D engineers, systems engineers, aeronautical engineers, and aviation training professionals. It will also be useful for aerospace and electrical engineering students taking courses in Air Traffic Management, Aviation Management, Aviation Operations, and Aviation Safety Systems.

Flight Mechanics Modeling and Analysis comprehensively covers flight mechanics and flight dynamics using a systems approach.This book focuses on applied mathematics and control theory in its discussion of flight mechanics to build a strong foundation for solving design and control problems in the areas of flight simulation and flight data analysis. The second edition has been expanded to include two new chapters and coverage of aeroservoelastic topics and engineering mechanics, presenting more concepts of flight control and aircraft parameter estimation.This book is intended for senior undergraduate aerospace students taking Aircraft Mechanics, Flight Dynamics & Controls, and Flight Mechanics courses. It will also be of interest to research students and R&D project-scientists of the same disciplines.Including end-of-chapter exercises and illustrative examples with a MATLAB®-based approach, this book also includes a Solutions Manual and Figure Slides for adopting instructors.Features:Covers flight mechanics, flight simulation, flight testing, flight control, and aeroservoelasticityFeatures artificial neural network- and fuzzy logic-based aspects in modeling and analysis of flight mechanics systems: aircraft parameter estimation and reconfiguration of controlFocuses on a systems-based approachIncludes two new chapters, numerical simulation examples with MATLAB®-based implementations, and end-of-chapter exercisesIncludes a Solutions Manual and Figure Slides for adopting instructors

Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available.

Fills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants. Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process.Focuses on Mathematical Tools That Use Data FusionThis text explores the use of statistical/probabilistic signal/image processing, filtering, component analysis, image algebra, decision making, and neuro-FL–GA paradigms in studying, developing and validating data fusion processes (DFP). It covers major mathematical expressions, and formulae and equations as well as, where feasible, their derivations. It also discusses SDF concepts, DF models and architectures, aspects and methods of type 1 and 2 fuzzy logics, and related practical applications. In addition, the author covers soft computing paradigms that are finding increasing applications in multisensory DF approaches and applications. This book: Explores the use of interval type 2 fuzzy logic and ANFIS in DFCovers the mathematical treatment of many types of filtering algorithms, target-tracking methods, and kinematic DF methodsPresents single and multi-sensor tracking and fusion mathematicsConsiders specific DF architectures in the context of decentralized systemsDiscusses information filtering, Bayesian approaches, several DF rules, image algebra and image fusion, decision fusion, and wireless sensor network (WSN) multimodality fusion Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance. This text is geared toward researchers, scientists, teachers and practicing engineers interested and working in the multisensor data fusion area.

Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are referenced throughout the text.

Fills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants. Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process.Focuses on Mathematical Tools That Use Data FusionThis text explores the use of statistical/probabilistic signal/image processing, filtering, component analysis, image algebra, decision making, and neuro-FL–GA paradigms in studying, developing and validating data fusion processes (DFP). It covers major mathematical expressions, and formulae and equations as well as, where feasible, their derivations. It also discusses SDF concepts, DF models and architectures, aspects and methods of type 1 and 2 fuzzy logics, and related practical applications. In addition, the author covers soft computing paradigms that are finding increasing applications in multisensory DF approaches and applications. This book: Explores the use of interval type 2 fuzzy logic and ANFIS in DFCovers the mathematical treatment of many types of filtering algorithms, target-tracking methods, and kinematic DF methodsPresents single and multi-sensor tracking and fusion mathematicsConsiders specific DF architectures in the context of decentralized systemsDiscusses information filtering, Bayesian approaches, several DF rules, image algebra and image fusion, decision fusion, and wireless sensor network (WSN) multimodality fusion Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance. This text is geared toward researchers, scientists, teachers and practicing engineers interested and working in the multisensor data fusion area.

Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering.After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems.Fusing sensors’ data can lead to numerous benefits in a system’s performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view.Select MATLAB programs are available for download on www.crcpress.com