Abstract:

With the rapid growing number of smart sensors and deploying sensors on or around physical objects, the Internet of Things (IoT) seamlessly integrates a world of networked smart objects, makes their information be shared on a global scale, and provides an ability of intelligent computing and information processing, such as reporting status, position, and surrounding condition of each sensor node. Passive localization and tracking is a key problem, which has been already studied in various fields, including passive sonar, radar, seismic, mobile communications, wireless sensor networks. However, many solutions may not directly suit an IoT scenario where large quantities of sensor nodes that perform distributed sensing and collaborative information processing tasks are interconnected together over a wireless channel. Many challenges arise due to the limited bandwidth and energy resources. It is almost impossible to collect full network sampling data for accurate localization since any inter-sensor communication requires a large burden on sensor batteries. Typical metrics are measured at the local sensors including sample covariance matrices (SCM), time differences of arrival (TDOA), gain ratios of arrival (GROA), angles of arrival (AOA) and frequency differences of arrival (FDOA). There is no doubt that to estimate the source position as accurate as possible by utilizing the above mentioned metrics is full of challenges.

In this half-day tutorial, we introduce the fundamentals of typical passive source localization and target tracking methods, including least-squares, maximum likelihood, convex relax optimization. We discuss some state-of-the-art passive localization and tracking approaches for acoustic array sensor networks (AASN) and indoor positioning for IoT applications, for example, subband information fusion, auxiliary variables based algorithms, localization penalized maximum likelihood, and weighted leastsquares using AOA-GROA-TDOA. The audience will learn the basic of passive localization and tracking, and get familiar with the state of the art in passive localization and tracking systems for IoT applications.

Biographies

• Xiao-Ping Zhang

  received B.S. and Ph.D. degrees from Tsinghua University, in 1992 and 1996, respectively, both in Electronic Engineering. He holds an MBA in Finance, Economics and Entrepreneurship with Honors from the University of Chicago Booth School of Business, Chicago, IL. Since Fall 2000, he has been with the Department of Electrical and Computer Engineering, Ryerson University, where he is now Professor, Director of Communication and Signal Processing Applications Laboratory (CASPAL). He has been a Visiting Scientist at Research Laboratory of Electronics (RLE), Massachusetts Institute of Technology. His research interests include statistical signal processing and big data analytics, sensor networks and Internet of things, machine learning, and applications in bioinformatics, finance, and marketing. He is a frequent consultant for biotech companies and investment firms. He is cofounder and CEO for EidoSearch, an Ontario based company offering a content-based search and analysis engine for financial big data.

  Dr. Zhang is a registered Professional Engineer in Ontario, Canada, and a member of Beta Gamma Sigma Honor Society. He is the general co-chair for ICASSP2021. He is the general co-chair for 2017 GlobalSIP Symposium on Signal and Information Processing for Finance and Business. He is an elected member of ICME steering committee. He is the general chair for MMSP'15. He is the publicity chair for ICME'06 and program chair for ICIC'05 and ICIC'10. He served as guest editor for Multimedia Tools and Applications, and the International Journal of Semantic Computing. He is a tutorial speaker in ACMMM2011, ISCAS2013, ICIP2013, ICASSP2014, IJCNN2017. He is a Senior Area Editor for IEEE Transactions on Signal Processing. He is/was an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, and IEEE Signal Processing Letters.

• Ji-An Luo

  received the B.Sc. and M.Sc. degrees in control theory and control engineering from Hangzhou Dianzi University, Hangzhou, China, in 2005 and 2008 respectively. In 2013, he received the Ph.D. degree in control science and engineering at the State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China. From January to May 2010, he was with the Key Lab of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Science, Beijing. Since November 2010, he has been a visiting Ph.D. student in the Communications and Signal Processing Applications Lab (CASPAL), Ryerson University, Toronto. Currently, he is working at Hangzhou Dianzi University as an Assistant Professor. His research interests are source localization, internet of things, array signal processing, and statistical signal processing. He has published more than 20 papers in international conferences and journals.