IRS Tutorials

 During Microwave and Radar Week 2022 we would like to invite you on tutorial sessions organized by IRS and our distinguished guests.

Tutorial Session 1

Beyond 5G Integrated Sensing and Communications

Half-day (two slots) with a 10-minute break in between

Where & When:

Faculty Of ETI Building B, room NE 234
(please verify room with conf. program)
Thursday, September 15, 08:30-13:00

Kumar Vijay Mishra Dr. Ing.
United States Army Research Laboratory


M. R. Bhavani Shankar Dr. Ing.
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg


Today’s cellular networks are at a crossroads while moving from the current 4G cellular networks used for content delivery to the upcoming 5G networks that will provide services with low latency, high security, and high throughput. At the same time, a crunch in spectrum usage implies that such high data networks must coexist with the radar sensing systems of the future. In this tutorial, we present fundamental challenges in enabling a crucial tradeoff between sensing/radar and communications functionalities in beyond 5G (B5G) systems. In particular, the automotive sector has recently witnessed concerted and intense efforts towards realizing the joint radar-communications (JRC) systems for efficient utilization of limited electromagnetic spectrum at millimeter-wave (mm-Wave). This band is characterized by severe penetration losses, short coherence times, and the availability of wide bandwidth. While wide bandwidth is useful in attaining high vehicular communications data rates and high-resolution automotive radar, the losses must be compensated by using a large number of antennas at the transmitter and receiver. In this context, there is also recent research focus on joint multiple-input multiple-output (MIMO)-Radar-MIMO-Communications (MRMC) systems, where the antenna positions of radar and communications are shared with each other.

These synergistic approaches that exploit the interplay between state sensing and communication are both driving factors and opportunities for many current signal processing and information-theoretic techniques. For example, while there are still many open challenges at mm-Wave JRC, it is already a precursor to sub-mm-Wave or Terahertz (THz) JRC, where futuristic short-range THz communications would coexist with low-THF (.1-1THz) automotive and imaging radars. At present, THz band is witnessing developments such as ultra-massive-MIMO systems which employ thousands of antennas in a few cms of aperture. Imaging with low-THz automotive radar is currently being investigated. Joint sensing-communications is also a growing area for unmanned aerial vehicles (UAVs) such as drones. Building on the existing approaches, the tutorial focuses on highlighting emerging scenarios in collaborative and joint sensing and communications systems, particularly at mm-Wave and THz frequencies, highly dynamic vehicular environments, distributed radar-communications networks, and aerial channels, that would benefit from information exchange between the two systems. It presents the architectures, possible methodologies for mutually beneficial co-existence as separate entities or as a joint module and presents some recent results. The avenues discussed in the tutorial offer rich research potential while also enabling innovative plug-and-play methodologies for co-existence and co-design.

Tutorial Session 2

Deep Learning for Synthetic Aperture Radar Target Recognition and Image Interpretation


Quarter-Day (2 hours)


Where & When:

Faculty Of ETI Building B, room NE 234
(please verify room with conf. program)
Thursday, September 15, 14:00-16:00

Prof. Feng Xu
Fudan University


In the big data era of earth observation, deep learning and other data mining technologies become critical to successful end applications. Deep learning technology has revolutionized the computer vision areas, and is gradually being applied in radar remote sensing. Over the past several years, there has been exponentially increasing interests related to deep learning techniques applied to synthetic aperture radar (SAR) imagery. However, there are issues that are specific to SAR image interpretation such as limited training samples, sensitivity to observation configuration, or weak generalization ability. There are some techniques that can be used to mitigate these issues such as fusing electromagnetic physics laws with deep neural networks, using prior constraints of physical laws to realize few-shot learning capability, etc. This tutorial reports the recent progresses of the author and collaborators in this area.

The first part of the tutorial briefly introduces the theory of deep learning, including the principles of deep neural networks, the backpropagation algorithms, programming toolboxes, etc. The second part of the tutorial introduces many cases of application including SAR automatic target recognition, polarimetric classification, image segmentation, few-shot/zero-shot learning, target reconstruction, etc. In each case study, it will also introduce the used novel advanced deep learning method which are specially designed to tackle the challenges that are specific to SAR data, e.g. EM-simulation-aided zero-shot learning, adversarial auto-encoder networks for SAR image generation, differentiable SAR renderer for target reconstruction, physics-inspired neural networks for electromagnetic problems. Finally, it also discusses the future development of SAR intelligent interpretation and microwave vision technology.

Participants are expected to understand the basic theory for deep neural networks including convolutional neural network, backpropagation algorithm, etc., and learn the relevant skills for SAR image interpretation with deep learning. In the meantime, participants will get to know some recent progresses in the this area and understand how deep learning techniques can be adapted for the specific domain-relevant problems.


Tutorial Session 3

Surveillance of drones and birds with staring radar


Quarter-Day (2hrs)

Where & When:

Faculty Of ETI Building B, room NE 230
(please verify room with conf. program)
Thursday, September 15, 14:00-16:00


Mohammed Jahangir
Microwave Integrated Systems Laboratory (MISL), University of Birmingham

Michail Antoniu
University of Birmingham


In recent years the number of drones operating at low altitudes has increased immensely that has brought about the growth in the demand for radar that can provide round the clock surveillance for small airborne targets. Staring radar utilizes extended dwell on target in order to detect small drones but such systems are equally effective at detecting birds which can result in confuser targets. A deeper understanding at the signature level is central to the ability to develop effective discriminators that can distinguish drones from birds. At University of Birmingham (UoB) a dedicated facility of two networked staring radars has been set up to provide data to support machine learning techniques for classification of targets and mapping and characterising low to medium airspace in an urban setting.

This workshop will briefly outline the motivation for small target detection using a surveillance radar, introduce the basic radar principles of the staring radar and illustrate the working of the radar equation to derive a generic signal-to-noise ratio for a small target. This will be followed by a description of the staring radar testbed at UoB and the process set up for conducting control trials with drones and birds along with techniques developed for obtaining labelled data for opportune birds. Numerous examples will be provided of real radar measurements of drones and birds, along with a discussion of a number of machine learning techniques. The latter will also review modelling approaches for target signatures and future direction for classification. Finally, latest techniques developed for longer term monitoring of bird movement in low altitude airspace will be discussed to highlight the potential benefit to other applications such as aeroecology.

This workshop will enable the participants to gain an appreciation of the potential and limitation for detecting and classifying small airborne targets with a staring radar. They will benefit from gaining some insight into both the future direction for the emerging processing techniques and innovation in radar architecture for networked systems.


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