Kegen Yu – författare

Presenting the latest advances in the field, Ground Based Wireless Positioning is one of the first books to cover non-GPS based technologies for wireless positioning. It serves as an indispensable reference for researchers and engineers specialising in the fields of localization and tracking, and wireless sensor networks.

This book focuses on non-GNSS positioning systems and approaches. Although it addresses both theoretical and practical aspects, the primary focus is on engineering practice. This is achieved by providing in-depth studies on a number of major topics such as tracking system architecture, link budget, system design, implementation, testing, and performance evaluation. It studies four positioning application cases in detail: covert vehicle tracking, horse racing, rowing, and tracking for field sports.

Its comprehensive and systematic treatment of practical issues in wireless positioning makes the book particularly suitable for readers who are interested in learning about practical wireless positioning solutions. It will also benefit researchers, engineers and graduate students in fields such as positioning and navigation, geospatial engineering and telecommunications.

This is the first authored English book completely focused on global navigation satellite system reflectometry (GNSS-R). It consists of two main parts: the fundamental theory; and major applications, which include ocean altimetry, sea surface wind speed retrieval, snow depth measurement, soil moisture measurement, tsunami detection and sea ice detection. Striking a healthy balance between theory and practice, and featuring in-depth studies and extensive experimental results, the book introduces beginners to the fundamentals, while preparing experienced researchers to pursue advanced investigations and applications in GNSS-R. 

This is the first book completely dedicated to positioning and navigation using machine learning methods. It deals with ground, aerial, and space positioning and navigation for pedestrians, vehicles, UAVs, and LEO satellites. Most of the major machine learning methods are utilized, including supervised learning, unsupervised learning, deep learning, and reinforcement learning. The book presents both fundamentals and in-depth studies as well as practical examples in positioning and navigation. Extensive data processing and experimental results are provided in the major chapters through conducting experimental campaigns or using in-situ measurements.