**Contents**



## **About the Editors**

**Tiancheng Li** (Professor) is currently a Professor at the School of Automation, Northwestern Polytechnical University (NPU), Xi'an, China. He received two bachelor's degrees from Harbin Engineering University, Harbin, China (2008), his first Ph.D. degree from London South Bank University, London, U.K. (2013), and his second doctoral degree from NPU (2015). He received the Excellent Doctoral Thesis Award of Shaanxi Province in 2017 and the Marie Sklodowska-Curie Individual Fellowship from the European Commission in 2016. He was a Postdoctoral Researcher with the University of Salamanca, Spain, from June 2014 to the Fall of 2018, and a Visiting Scholar with the Vienna University of Technology, Austria, in the Summer of 2017 and Fall of 2018. His research is focused on collaborative mobile robots, distributed-linear information fusion, and intelligent data-driven algorithms for target detection, tracking, and trajectory forecasting. He is an Associate Editor for three peer-reviewed journals *Frontiers of Information Technology and Electronic Engineering*, *Advances in Distributed Computing and Artificial Intelligence Journal*, and *Aero Weaponry*.

**Junkun Yan** (Professor, IEEE Senior Member) was born in Sichuan, China, in 1987. He received his B.S. and Ph.D. degrees in electronics engineering from Xidian University, Xi'an, China, in 2009 and 2015, respectively. He is currently an associate professor of the National Laboratory of Radar Signal Processing, Xidian University. His research interests include adaptive signal processing, target tracking, and cognitive radar. He has published more than 50 scientific articles in refereed journals, such as the *IEEE Transactions on Signal Processing*, *Information Fusion*, *IEEE Transactions on Vehicular Technology, Signal Processing*, *IEEE Transactions on Aerospace and Electronic Systems*, and *IEEE Sensors Journal*. He received the Excellent Doctoral thesis Award from the SHANNXI Institute of Electronics in 2017. He also received the Young Talent fund of the China Association for Science and Technology in 2020.

**Yue Cao** (Professor) received his Ph.D. degree from the Institute for Communication Systems (ICS) formerly known as the Centre for Communication Systems Research, at the University of Surrey, Guildford, UK in 2013. Further to his Ph.D. study, he was a research fellow at the University of Surrey, and academic faculty at Northumbria University, Lancaster University, UK, and Beihang University, China; he is currently a Professor at the School of Cyber Science and Engineering, Wuhan University, China. His research interests focus on Intelligent Transport Systems, including E-Mobility, V2X, Edge Computing.

**Javier Bajo** (Profesor) is a Full Profesor at the ETS Ingenieros Informaticos—Universidad ´ Politecnica de Madrid (UPM). He is currently the Coordinator of the Research Master in Artificial ´ Intelligence at UPM. Previously, he held the position of Associate Professor at the Universidad Pontificia de Salamanca (2003 to 2012) and Director of the Data Center in the same University (2010–2012). He obtained a Ph.D. in computer sciences from the Universidad de Salamanca (with honors) (2007) and a Master in E-Commerce from the same University (2006). He obtained a bachelor's in computer sciences at the Universidad de Valladolid (2001) and an MSc in computer sciences at the Universidad Pontificia de Salamanca (2003). His research efforts are focused on multi-agent systems, social computing, and ambient intelligence. He has participated in more than 50 research projects (funded by European Commission, National or Regional entities) and contracts with private companies, acting as the principal researcher in 11 of these projects. He is the co-author of more than 300 scientific publications (books, journal papers, and conference papers). Of these publications, 49 have been published in international journals that are indexed in the ISI JCR reference index. He has been the co-chairman of the organizing committee of more than 30 recognized international conferences (ACM SAC, IEEE FUSION, PAAMS, etc.).

## **Preface to "Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities"**

We are in a new era of intelligence and data. The rapid development of advanced sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, and smart sensing in harsh environments with poor a priori information. Relevant advances in artificial intelligence and machine learning are also rapidly adopted by industry and further fan the fire. Together with the classic least squares and linear fusion paradigms, these advances provide another unlimited means to utilize the pattern of data and probabilistic models for positioning and tracking, showing grea<sup>t</sup> promise as a means of restoring sensor capability over a range of challenging operating conditions. Consequently, research on advanced sensing and estimation approaches has burgeoned into two promising and intertwined directions. The first is intelligent systems and data-driven algorithms for information fusion, which have led to a variety of effective applications related to intelligent transportation, autonomous vehicles/robots, wearable computing, smart sensing, and the internet of things. For example, the least squares estimator plus a clustering algorithm based on appropriate target trajectory modeling can deal with the complicated problem of joint target detection and tracking in clutter with little a priori knowledge. The second is distributed systems and multi-agent frameworks, which are gaining considerable popularity due to their low power consumption and simple installation and high performance and strong reliability, compared with a centralized setting. To this end, arithmetic average fusion of multi-target densities is a notable ye<sup>t</sup> simple approach, which demonstrates outstanding robustness and tolerance to local failures. The advent in both directions provides rich observation at high frequencies but low financial costs, which facilitates novel perspectives based on data clustering, fusion, and learning to deal with noise, false alarms, and misdetection, given little a priori knowledge. As such, the sensors community has a grea<sup>t</sup> interest in novel information fusion, resource optimization, and data mining methods coupled with intelligent algorithms for substantial performance enhancement, especially for challenging scenarios that make traditional approaches inappropriate.

This book is a reprint of the Special Issue "*Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities*" which was published in the journal *Sensors*. This book consists of 14 papers contributed by scientists and technicians and aims to provide a cutting-edge coverage ofrecent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing.

> **Tiancheng Li, Junkun Yan, Yue Cao, Javier Bajo** *Editors*
