This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.
The book collects selected papers presented at the 5th International Conference on Aerospace System Science and Engineering (ICASSE 2021), organized by Shanghai Jiao Tong University, China, hosted by Moscow Aviation Institute, Russia. It provides a forum for experts in aeronautics and astronautics to share new ideas and findings. ICASSE conference has been organized annually since 2017 and host in Shanghai, Moscow, and Toronto in turn, where the three regional editors of journal Aerospace Systems are located. This book presents high-quality contributions in the subject area of Aerospace System Science and Engineering, including topics such as: Trans-space vehicle systems design and integration, Air vehicle systems, Space vehicle systems, Near-space vehicle systems, Opto-electronic system, Aerospace robotics and unmanned system, Aerospace robotics and unmanned system, Communication, navigation and surveillance, Dynamics and control, Intelligent sensing and Information fusion, Aerodynamics and aircraft design, Aerospace propulsion, Avionics system, Air traffic management, Earth observation, Deep space exploration, Bionic micro-aircraft/spacecraft.
The new book presents a valuable selection of state-of-the-art technological advancements using the concepts of AI and machine learning, highlighting the use of predictive analytics of data to find timely solutions to real-time problems. It helps to identify applicable approaches in order to enhance, automate, and develop effective solutions to challenges in data science and artificial intelligence. The various novel approaches include applications in healthcare, natural language processing, and smart cities. As such, the book is divided into sections that address: Computational Intelligence in Image Processing Computational Intelligence in Healthcare Techniques for Natural Language Processing Computational Intelligence in Smart Cities The very diverse range of topics include AI and machine learning applications for In security: For using digital image processing for image fusion (face recognition, feature extraction, object detection as well tracking, moving object identification), for person re-identification for security purposes. In healthcare and medicine: For diagnosis and prediction of breast cancer, other cancers, diabetes, heart disease; for predicting susceptibility to COVID-19; for prediction of mood and anxiety disorders. In agriculture: For prediction of crop profit; for prediction of cropping patterns and recommendation for crop cultivation. In traffic science/smart cities: For understanding road scene images, for detection of traffic signs, for devising a fog-based intelligent traffic phase timing regulation system In language/speech/text: For automatic text summarization, for document indexing for unstructured data, for speech/accent recognition, for sound separation, for American Sign Language interpretation for nonsigners, for emotional recognition and analysis through speech, body postures with facial expressions, and other body movements (to improve the performance of virtual personal assistants / emotion recognition using speech, body postures with facial expressions and other body movements. This volume offers valuable information for researchers working in interdisciplinary or multidisciplinary areas of healthcare, image analysis, natural language processing, and smart cities. This includes academicians, people in industry, and students with engineering background with research interest in these areas. These peer-review chapters were selected from the International Conference on Computational Intelligence in Analytics and Information Systems (CIAIS- 2021), held in April 2021 at Manav Rachna University, India. Together with Volume 2: Advances in Digital Transformation, this 2-volume set offers an abundacne of valuable information on emerging technologies in computational intelligence in information systems focusing on data science and artificial intelliegence.
This book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations. Image processing technology has progressed significantly in recent years, and it has been commercialized worldwide to provide superior performance with enhanced computer/machine vision, video processing, and pattern recognition capabilities. Meanwhile, machine learning systems like CNN and CapsNet get popular to provide better model hierarchical relationships and attempts to more closely mimic biological neural organization. As machine learning systems prosper, image processing and machine learning techniques will be tightly intertwined and continuously promote each other in real-world settings. Adopting this trend, however, the image processing researchers are faced with few image reconstruction, analysis, and segmentation challenges. On the application side, the orientation of the image features and noise removal has become a huge burden.
Data Fusion is an interdisciplinary technology domain. This work focuses on the mature phase of data fusion, namely the detection and identification/classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications.
This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.
This book features the latest theoretical results and techniques in the field of guidance, navigation, and control (GNC) of vehicles and aircrafts. It covers a wide range of topics, including but not limited to, intelligent computing communication and control; new methods of navigation, estimation and tracking; control of multiple moving objects; manned and autonomous unmanned systems; guidance, navigation and control of miniature aircraft; and sensor systems for guidance, navigation and control etc. Presenting recent advances in the form of illustrations, tables, and text, it also provides detailed information of a number of the studies, to offer readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the development of GNC, making it a valuable resource for both beginners and researchers wanting to further their understanding of guidance, navigation, and control.
This book is a printed edition of the Special Issue "Advances in Multi-Sensor Information Fusion: Theory and Applications 2017" that was published in Sensors
A unique guide to the state of the art of tracking, classification, and sensor management This 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 solving A succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and tracking A state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management Coverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor management Insight 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.
This book constitutes the refereed proceedings of the 19th International Forum on Digital Multimedia Communication, IFTC 2022, held in Shanghai, China, December 8–9, 2022. The 40 full papers included in this book were carefully reviewed and selected from 112 submissions. They were organized in topical sections as follows: Computer Vision; Image Analysis; Quality Assessment; Video Processing; Machine Learning; and Big data.
