Invited speaker
Pavel Loskot
ZJU-UIUC Institute, China
Speech Title: Estimation Problems in Array Signal Processing
Abstract: Spatial signals are generated by spatially distributed sources, which are then simultaneously observed in multiple spatial locations. The spatial dimension allows one to assume the spatial signals to be a special case of images, however, the former are also time-varying, and they are often modeled as Gaussian random processes. The example scenarios include radiowave propagation between the transmitting and receiving antennas in wireless communication systems, multi-channel EEG measurements, remote sensing in complex environments, and also multi-projections in general tomographic reconstructions. The signal processing problems appearing in all these scenarios always involve some form of inverse problems requiring to infer the parameters of the spatial signals in order to spatially resolve and combine the signals, spatially extrapolate the signals, and extract or reconstruct information about the sources. In this talk, we will focus on reviewing the key ideas and results in array signal processing where the spatial locations of the observers are known, and the task is to infer the key parameters of the incoming time-varying 3D random field such as the direction of arrival, amplitude and frequency shift, which are then used to design beamforming schemes for antenna arrays in wireless communications. We will show that the problem formulation and the solution can differ significantly when the signals can be considered to be narrowband and when they must be considered to be wideband.
Bio: Pavel Loskot joined the ZJU-UIUC Institute, Haining, China, in January 2021 as Associate Professor after 14 years being the Senior Lecturer at Swansea University in the UK. He obtained his PhD degree in Wireless Communications from the University of Alberta in Canada, and the MSc and BSc degrees in Radioelectronics and Biomedical Electronics, respectively, from the Czech Technical University of Prague in the Czech Republic. In the past 25 years, he was involved in numerous collaborative research and development projects, and also held a number of consultancy contracts with industry. Pavel Loskot is a Senior Member of the IEEE, a Fellow of the Higher Education Academy in the UK, and the Recognized Research Supervisor of the UK Council for Graduate Education. His current research interests focus on mathematical and probabilistic modeling, statistical signal processing and classical machine learning for multi-sensor data in biomedicine, computational molecular biology, and wireless communications.