Informational Tracking

The essential guide to state-of-the art mobile positioning and tracking techniques—fully updated for new and emerging trends in the field Mobile Positioning and Tracking, Second Edition explores state-of-the-art mobile positioning solutions applied on top of current wireless communication networks. Application areas covered include positioning, data fusion and filtering, tracking, error mitigation, both conventional and cooperative positioning technologies and systems, and more. The authors fill the gap between positioning and communication systems, showing how features of wireless communications systems can be used for positioning purposes and how the retrieved location information can be used to enhance the performance of wireless networks. Unlike other books on the subject, Mobile Positioning and Tracking: From Conventional to Cooperative Techniques, 2nd Edition covers the entire positioning and tracking value chain, starting from the measurement of positioning signals, and offering valuable insights into the theoretical fundamentals behind these methods and how they relate to application areas such as location-based services, as well as related disciplines and professional concerns, including global business considerations and the changing laws and standards governing wireless communication networks. Fully updated and revised for the latest developments in the field, this Second Edition: Features new chapters on UWB positioning and tracking, indoor positioning in WLAN, and multi-tag positioning in RFID Explores an array of positioning and tracking systems based on satellite and terrestrial systems technologies and methods Introduces advanced and novel topics such as localisation in heterogeneous and cooperative scenarios Provides a bridge between research and industry with potential implementations of the solutions presented Mobile positioning and tracking is subject to continuous innovations and improvements. This important working resource helps busy industry professionals and practitioners—including software and service developers—stay on top of emerging trends in the field. It is also a valuable reference for advanced students in related disciplines studying positioning and mobile technologies.

Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time, the position of objects of interest seen through cameras. Starting from the general problem definition and a review of existing and emerging video tracking applications, the book discusses popular methods, such as those based on correlation and gradient-descent. Using practical examples, the reader is introduced to the advantages and limitations of deterministic approaches, and is then guided toward more advanced video tracking solutions, such as those based on the Bayes’ recursive framework and on Random Finite Sets. Key features: Discusses the design choices and implementation issues required to turn the underlying mathematical models into a real-world effective tracking systems. Provides block diagrams and simil-code implementation of the algorithms. Reviews methods to evaluate the performance of video trackers – this is identified as a major problem by end-users. The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications. The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programmes

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.

Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: Problems that apply theoretical material to real-world applications In-depth coverage of the Interacting Multiple Model (IMM) estimator Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.

Radiometric Tracking Techniques for Deep-Space Navigation focuses on a broad array of technologies and concepts developed over the last four decades to support radio navigation on interplanetary spacecraft. In addition to an overview of Earth-based radio navigation techniques, the book includes a simplified conceptual presentation of each radiometric measurement type, its information content, and the expected measeurement accuracy. The methods described for both aquiring and calibrating radiometric measurements also provide a robust system to support guidance and navigation for future robotic space exploration.

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.

Стабилизатор Qiimos-QL7C pro plus/ AI tracking - это высококачественный стабилизатор, специально разработанный для использования со смартфонами. Он обеспечивает стабильное и плавное видео, позволяя создавать профессиональные съемки с помощью смартфона.

Основные особенности и функции стабилизатора QL7C pro plus/ AI tracking включают:

1* Трехосевую стабилизацию: Устройство оснащено мощными бесщеточными моторами, которые компенсируют нежелательные движения и вибрации, обеспечивая стабильное изображение даже при активном движении

.

2* Режимы слежения AI для отслеживания лиц и управления жестами -это отдельный блок, который крепится на магнитах к стабилизатору. Благодаря функции отслеживания лиц Вы всегда останетесь в четком фокусе, не теряя качество изображения. А с помощью функции управления жестами Вы сможете контролировать фото и видео съемку парой движений.

.

3* Управление фокусом и экспозицией: С помощью приложения на телефоне можно контролировать фокусировку и яркость изображения, чтобы достичь наилучшего качества съемки.

4*Управление устройством осуществляется с помощью удобного джойстика. Стабилизатор способен поворачивать смартфон на 340° по горизонтали и на 325° по вертикали. Кроме того, стабилизатор поддерживает два режима съемки в одно нажатие на кнопку - вертикальный и горизонтальный.

5*приложение для ANDROID И IOS

С помощью мобильного приложения для Android и iOS Вы сможете удобно управлять настройками стабилизатора. Кроме того, приложение позволяет использовать дополнительные функции устройства, такие как автоматическое улучшение кадра, слежение за объектом и быстрое переключение между фото- и видеосъемкой.

6*Стабилизатор Qiimos-QL7C pro plus/ AI tracking : оснащен специальным креплением-зажимом, которое крепко фиксирует любой смартфон с диагональю от 4 до 7 дюймов. Вес смартфона может достигать 280 грамм

7*Стабилизатор Qiimos-QL7C pro plus/ AI tracking : совместим с большинством популярных мобильных телефонов на рынке, такими как iPhone 15/14/13/Pro Max/Pro/Plus, SE2, Samsung Galaxy S22/S21/S20/+/Ultra, Huawei P50/40. /30/20/Pro, Mate50/40/Pro и так далее.

8*Устройство оснащено аккумулятором емкостью 2000 мАч, который обеспечивает до 5 часов работы стабилизатора на одном заряде. Зарядка стедикама занимает всего 60- 80 минут. Долгий срок службы батареи: встроенная литий-ионная батарея обеспечивает длительное время работы, позволяя снимать длительные видео или проводить продолжительные фотосессии.

Стабилизатор Qiimos-QL7C pro plus/ AI tracking - это незаменимый инструмент для тех, кто хочет получать высококачественные и стабильные видео с помощью смартфона. Он легкий, портативный и легко настраивается, что делает его отличным выбором для любителей и профессионалов в области съемки на телефон.

Стабилизатор Qiimos-Q12 pro/ AI tracking - это высококачественный стабилизатор, специально разработанный для использования со смартфонами. Он обеспечивает стабильное и плавное видео, позволяя создавать профессиональные съемки с помощью смартфона.

Основные особенности и функции стабилизатора QL7C pro plus/ AI tracking включают:

1* Трехосевую стабилизацию: Устройство оснащено мощными бесщеточными моторами, которые компенсируют нежелательные движения и вибрации, обеспечивая стабильное изображение даже при активном движении

.

2* Режимы слежения AI для отслеживания лиц и управления жестами -это отдельный блок, который крепится на магнитах к стабилизатору. Благодаря функции отслеживания лиц Вы всегда останетесь в четком фокусе, не теряя качество изображения. А с помощью функции управления жестами Вы сможете контролировать фото и видео съемку парой движений.

.

3* Управление фокусом и экспозицией: С помощью приложения на телефоне можно контролировать фокусировку и яркость изображения, чтобы достичь наилучшего качества съемки.

4*Управление устройством осуществляется с помощью удобного джойстика. Стабилизатор способен поворачивать смартфон на 340° по горизонтали и на 325° по вертикали. Кроме того, стабилизатор поддерживает два режима съемки в одно нажатие на кнопку - вертикальный и горизонтальный.

5*приложение для ANDROID И IOS

С помощью мобильного приложения для Android и iOS Вы сможете удобно управлять настройками стабилизатора. Кроме того, приложение позволяет использовать дополнительные функции устройства, такие как автоматическое улучшение кадра, слежение за объектом и быстрое переключение между фото- и видеосъемкой.

6*Стабилизатор Qiimos-Q12 pro / AI tracking : оснащен специальным креплением-зажимом, которое крепко фиксирует любой смартфон с диагональю от 4 до 7 дюймов. Вес смартфона может достигать 280 грамм

7*Стабилизатор Qiimos-QL7C pro plus/ AI tracking : совместим с большинством популярных мобильных телефонов на рынке, такими как iPhone 15/14/13/Pro Max/Pro/Plus, SE2, Samsung Galaxy S22/S21/S20/+/Ultra, Huawei P50/40. /30/20/Pro, Mate50/40/Pro и так далее.

8*Устройство оснащено аккумулятором емкостью 2000 мАч, который обеспечивает до 5 часов работы стабилизатора на одном заряде. Зарядка стедикама занимает всего 60- 80 минут. Долгий срок службы батареи: встроенная литий-ионная батарея обеспечивает длительное время работы, позволяя снимать длительные видео или проводить продолжительные фотосессии.

Стабилизатор Qiimos-Q12 pro/ AI tracking - это незаменимый инструмент для тех, кто хочет получать высококачественные и стабильные видео с помощью смартфона. Он легкий, портативный и легко настраивается, что делает его отличным выбором для любителей и профессионалов в области съемки на телефон.

This book presents the most recent state of the art in mobile positioning and tracking techniques. This book discusses mobile positioning solutions applied on top of current wireless communication networks. In addition, the authors introduce advanced and novel topics such as localization in heterogeneous and cooperative networks, providing a unified treatment of the topic for researchers and industry professionals alike. Furthermore, the book focuses on application areas of positioning, basics of wireless communications for positioning, data fusion and filtering techniques, fundamentals of tracking, error mitigation techniques, positioning systems and technologies, and cooperative mobile positioning systems. Key Features: Covers the state of the art of satellite- and terrestrial-based positioning systems, spanning from outdoor to indoor environments and from wide area networks to short-range networks Discusses a whole range of topics related to mobile positioning: from fundamentals of positioning to the description of a wide spectrum of mobility models for tracking, from details on data fusion and filtering techniques to error mitigation techniques (including aspects of signal processing) Provides a solid bridge between research and industry envisaging a potential implementation of the presented solutions Fills the gap between positioning and communication systems, showing how features of communication systems can be used for positioning purposes and how the retrieved location information can be used to enhance the performance of wireless networks. Includes an accompanying website This book will be a valuable guide for advanced students studying related courses. Professionals and practitioners in the field of positioning and mobile technologies, and software and service developers will also find this book of interest.

Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space. Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening. Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory. The authors: Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target. Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.