This book captures the latest results and techniques for cooperative localization and navigation drawn from a broad array of disciplines. It provides the reader with a generic and comprehensive view of modeling, strategies, and state estimation methodologies in that fields. It discusses the most recent research and novel advances in that direction, exploring the design of algorithms and architectures, benefits, and challenging aspects, as well as a potential broad array of disciplines, including wireless communication, indoor localization, robotics, emergency rescue, motion analysis, etc.

The limitations of satellites create a large gap in assistive directional technologies, especially indoors. The methods and advances in alternate directional technologies is allowing for new systems to fill the gaps caused by the limitations of GPS systems. Positioning and Navigation in Complex Environments is a critical scholarly resource that examines the methodologies and advances in technologies that allow for indoor navigation. Featuring insight on a broad scope of topics, such as multipath mitigation, Global Navigation Satellite System (GNSS), and multi-sensor integration, this book is directed toward data scientists, engineers, government agencies, researchers, and graduate-level students.

This thesis introduces a new method to improve localization performance for teams of vehicles navigating cooperatively. When fusing measurements between multiple vehicles, the structure of the cooperative navigation network inherently introduces correlation between them, causing many traditional filter architectures to overconverge and become inconsistent. The algorithm presented in this thesis addresses this correlation and properly fuses measurements, allowing improved performance over other existing methods while still guaranteeing consistency. When restricted to linear, Gaussian systems, the covariance recovers 99% of the performance of an ideal centralized filter in some tests. Additionally, a proof is presented to guarantee that the algorithm is consistent under standard Kalman filter assumptions. The algorithm is also extended to apply to nonlinear systems, losing the guarantees of consistency (as with all Kalman filters) but achieving good performance in practice. This allowed the method to be tested in a laboratory experiment with real-world sensors. Finally, this thesis further extends the algorithm to apply to non-parametric particle filters, allowing for full cooperative Terrain-Relative Navigation (TRN) with multi-modal position estimates. This is demonstrated in simulation, where cooperative TRN is shown to provide a 63% reduction in localization error over standard single-vehicle TRN for one example mission, reducing the average error from 23.7m to 8.7m for a vehicle over flat terrain. The cooperative TRN algorithm is also demonstrated using field data from a team of Long-Range Autonomous Underwater Vehicles in Monterey Bay. In offline testing, the cooperative TRN method was able to correctly find the position of a vehicle when its own individual TRN filter was unable to converge. This demonstrates that the cooperative TRN algorithm is effective with real-world robotic systems, increasing localization accuracy and enabling new missions involving navigation in flat, unmapped, or changed terrain.

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

A comprehensive review of position location technology — from fundamental theory to advanced practical applications Positioning systems and location technologies have become significant components of modern life, used in a multitude of areas such as law enforcement and security, road safety and navigation, personnel and object tracking, and many more. Position location systems have greatly reduced societal vulnerabilities and enhanced the quality of life for billions of people around the globe — yet limited resources are available to researchers and students in this important field. The Handbook of Position Location: Theory, Practice, and Advances fills this gap, providing a comprehensive overview of both fundamental and cutting-edge techniques and introducing practical methods of advanced localization and positioning. Now in its second edition, this handbook offers broad and in-depth coverage of essential topics including Time of Arrival (TOA) and Direction of Arrival (DOA) based positioning, Received Signal Strength (RSS) based positioning, network localization, and others. Topics such as GPS, autonomous vehicle applications, and visible light localization are examined, while major revisions to chapters such as body area network positioning and digital signal processing for GNSS receivers reflect current and emerging advances in the field. This new edition: Presents new and revised chapters on topics including localization error evaluation, Kalman filtering, positioning in inhomogeneous media, and Global Positioning (GPS) in harsh environments Offers MATLAB examples to demonstrate fundamental algorithms for positioning and provides online access to all MATLAB code Allows practicing engineers and graduate students to keep pace with contemporary research and new technologies Contains numerous application-based examples including the application of localization to drone navigation, capsule endoscopy localization, and satellite navigation and localization Reviews unique applications of position location systems, including GNSS and RFID-based localization systems The Handbook of Position Location: Theory, Practice, and Advances is valuable resource for practicing engineers and researchers seeking to keep pace with current developments in the field, graduate students in need of clear and accurate course material, and university instructors teaching the fundamentals of wireless localization.

Since its introduction to the public in 1994, the Global Positioning System (GPS) has been used in every aspect of our lives. While GPS works just fine most of the times, such as navigating an unknown city using a portable GPS unit or soldiers using it in the battle field, scenarios arise where GPS fails as a reliable positioning and localization system; like inside buildings and tunnels. Cooperative localization using cellular phones would solve this problem since there is a much higher number of information sources (other surrounding cellular users) that are much closer to the user, whereas the GPS system only has a limited number of information sources which are thousands of kilometers away. Spoofing, although not popular in GPS, is a topic of a few research papers. With the introduction of cooperative localization, spoofing will be easier to implement and harder to defend against. Not much research has been done to study the effects of spoofing attacks on cooperative localization networks. In this dissertation, it is shown that reliable positioning and localization is possible using cooperative localization through cellular networks. In addition, it proves that processing sufficient information from a number of information transmitting users can mitigate spoofing attacks on any network. This can be done by filtering out the extreme location estimations resulting from the spoofing attacks. Since Kalman filter helps in predicting the movement of mobile users, using Kalman filter for mobile network users significantly helped reduce the error in estimating the user’s location.

This is the first book on the topic of all source positioning, navigation and timing (PNT) and how to solve the problem of PNT when the most widely-used measurement source available today, the GPS system, may be come unavailable, jammed or spoofed. Readers learn how to define the system architecture as well as the algorithms for GPS-denied and GPS-challenged PNT systems. In addition, the book provides comprehensive coverage of the individual technologies used, such as celestial navigation, vision-based navigation, terrain referenced navigation, gravity anomaly referenced navigation, signal of opportunity (SOO) based PNT, and collaborative PNT. Celestial Navigation is discussed, with stars and satellite used as reference, and star-tracker technology also included. Propagation based timing solutions are explored and the basic principles of oscillators and clocks presented. Initial alignment of strap-down navigation systems is explored, including initial alignment as a Kalman filter problem. Velocimeter/Dead reckoning based navigation and its impact on visual odometry is also explained. Covering both theoretical and practical issues, and packed with equations and models, this book is useful for both the engineering student as well as the advanced practitioner.

The IEEE International Conference on Autonomous Systems (ICAS) would be a premier international forum for the technological advances and research results in the fields of theoretical, experimental, and applied Autonomous Systems (AS) Since autonomous systems are a multidisciplinary field, research and applied frontiers in areas ranging from theory methodology to applications would be advanced by results first reported at ICAS sessions and events

Unavailability of GPS (global positioning system) for underwater navigation has created significant challenges for operation and localization of autonomous underwater vehicles (AUVs). This is more pronounced in dynamic ocean flows where significant background flows exist. In this thesis, a collaborative underwater localization hierarchy is introduced to improve the cooperative performance of a small AUV swarm by utilizing vehicles with bounded localization error as moving references in the presence of dominating background flows. Initially represented in probability theory, the problem is then decomposed into a cooperative localization problem and a dynamic simultaneous localization and mapping problem with moving features. To address the incomplete covariance updating issue, which arises when directly applying the extended Kalman filter in fully distributed systems, the modified extended Kalman filter (MEKF) is proposed and a MEKF based algorithm is discussed in detail. A particle filter based algorithm is implemented for comparative purposes due to its advantages in modeling multimodal non-Gaussian distributions. However, it is shown that the particle filter requires greater computational effort than the MEKF when the number of vehicles is small. Both proposed algorithms are verified in three-dimensional background flow simulations. The divergent behavior of localization error, which appears when using solely cooperative localization, is avoided through the implementation of either the MEKF algorithm or the particle algorithm. Significant decreases in localization error are subsequently observed.

Explore an insightful summary of the major self-contained aiding technologies for pedestrian navigation from established and emerging leaders in the field Pedestrian Inertial Navigation with Self-Contained Aiding delivers a comprehensive and broad treatment of self-contained aiding techniques in pedestrian inertial navigation. The book combines an introduction to the general concept of navigation and major navigation and aiding techniques with more specific discussions of topics central to the field, as well as an exploration of the future of the future of the field: Ultimate Navigation Chip (uNavChip) technology. The most commonly used implementation of pedestrian inertial navigation, strapdown inertial navigation, is discussed at length, as are the mechanization, implementation, error analysis, and adaptivity of zero-velocity update aided inertial navigation algorithms. The book demonstrates the implementation of ultrasonic sensors, ultra-wide band (UWB) sensors, and magnetic sensors. Ranging techniques are considered as well, including both foot-to-foot ranging and inter-agent ranging, and learning algorithms, navigation with signals of opportunity, and cooperative localization are discussed. Readers will also benefit from the inclusion of: A thorough introduction to the general concept of navigation as well as major navigation and aiding techniques An exploration of inertial navigation implementation, Inertial Measurement Units, and strapdown inertial navigation A discussion of error analysis in strapdown inertial navigation, as well as the motivation of aiding techniques for pedestrian inertial navigation A treatment of the zero-velocity update (ZUPT) aided inertial navigation algorithm, including its mechanization, implementation, error analysis, and adaptivity Perfect for students and researchers in the field who seek a broad understanding of the subject, Pedestrian Inertial Navigation with Self-Contained Aiding will also earn a place in the libraries of industrial researchers and industrial marketing analysts who need a self-contained summary of the foundational elements of the field.