Tutorials will be run on the Monday 6th November and Friday 10 November and will cover a range of topics, with both introductory and advanced topics.
Tutorials can be booked as a part of your registration for the Radar Conference. Visit the registration page here.
Alternatively if you wish to book a tutorial only please click here.
The confirmed list of tutorials are:
Monday morning – 6 November 2023
Unleashing the Potential of Dual-Functional Radar Communications in Next-Generation Wireless Networks: A Tutorial on Advancement and Challenges – Kai Wu, Elias Aboutanios and Andrew Zhang
Introduction to Radar – Hugh Griffiths
Systematic Filter Design for Tracking Maneuvering Targets: Getting Guaranteed Performance Out Of your Sensors – Dale Blair
Detection, Estimation, and Resource Allocation in Distributed Radar Networks – Batu Chalise
Ultra Wideband Surveillance Radar – Mark Davis
Monday afternoon – 6 November 2023
Advanced Radar Detection and Applications – Scott Goldstien and Mike Picciolo
Introduction to Electronic Warfare – David Brown
Introduction to Over-the-Horizon Radar (OTHR) – Giuseppe Fabrizio
Friday morning – 10 November 2023
Design, Simulation, Analysis, and Testing of Radar Systems – Sumit Garg, Satish Thoklala, Ying Chen
Deep Learning for Advanced Radar Automatic Target Recognition (ATR) – Uttam Majumder
PCL radar – from theory to operational radar systems and future applications – Mateusz Malanowski, Steffen Lutz, Piotr Samczynski
Computational Methods in Radar Imaging – Petros Boufounos, Hassan Mansour
Book more than one tutorial and save on your second and third tutorial. Prices are calculated during registration.
All prices include GST and are in Australian dollars (AUD).
|IEEE Members early bird
|IEEE Member standard
|Non IEEE Members and day registrations early bird
|Non IEEE Members and day registrations standard
|Student IEEE members and Student non-members
Unleashing the Potential of Dual-Functional Radar Communications in Next-Generation Wireless Networks A Tutorial on Advancement and Challenges
Dual-functional radar communications (DFRC), also known as integrated sensing and communications (ISAC), is rapidly becoming a cornerstone of various next-generation wireless systems, including cellular networks, industrial IoT, and satellite networks. Recognizing the escalating academic and industrial interest in DFRC, we propose this timely and comprehensive tutorial that unravels the latest advancements in DFRC, while simultaneously addressing its ongoing challenges. Our tutorial commences with a detailed introduction to DFRC, emphasizing radar system-based designs. It then pivots to explore the intriguing designs of DFRC, especially those centered around frequency-hopping MIMO radars. Here, we spotlight how such radars can enhance data rate, reliability, and secrecy of embedded communications with minimal impact on existing radar systems. Subsequently, the tutorial explores another DFRC trend, the integration of radar sensing into ubiquitous communication systems. Seamlessly blending theoretical concepts with extensive analytical and experimental results, our tutorial aims to offer attendees a holistic view of the present state of DFRC and valuable insights into the potential avenues for future research and development in this exciting field.
Dr Kai Wu is a lecturer at the University of Technology Sydney (UTS). He has been working on the tutorial topic, I.e., dual-functional radar communications (DFRC), for over 3.5 years. He has developed numerous interesting designs in frequency hopping MIMO radar-based communications, facilitating high-rate, reliable and secure DFRC. Along with an overview of the design ideology and methodology, he will also share some recent experimental results on such DFRC systems, unveiling insights and remaining issues for ongoing DFRC designs and implementation. Dr Wu has been lecturing EEE-related subjects in UTS since 2019. He also has rich experience in delivering talks in international events, such as tutorials in ICC’2 and WCNC’20.
Introduction to Radar
The tutorial covers the basic principles of radar, for a wide range of different applications. It is based on an MSc module taught for many years at University College London, but with strong influence from the ‘Stimson’s Introduction to Airborne Radar (3rd edition)’ book. In that sense the style of the tutorial is to attempt to explain concepts pictorially rather than by pages of mathematics. The course is made up of twelve topics reflecting the important concepts in modern radar. These concepts will be illustrated with examples from practical modern radar systems.
Hugh Griffiths holds the THALES/Royal Academy Chair of RF Sensors in the Department of Electronic and Electrical Engineering at University College London, England, and is Chair of the Defence Science Expert Committee (DSEC) in the UK Ministry of Defence. His research interests include radar systems and signal processing (particularly bistatic radar and synthetic aperture radar), and antenna measurement techniques. He serves as Editor-in-Chief of the IET Radar, Sonar and Navigation journal. He has published over five hundred papers and technical articles in the fields of radar, antennas and sonar. He has received several awards and prizes, including the IEEE Picard Medal (2017), IET Achievement Medal (2017), the IEEE AES Mimno Award (2015), the IET A.F. Harvey Prize (2012) and the IEEE AES Nathanson Award (1996). He is a Fellow of the IET and a Fellow of the IEEE. In 2019 he was appointed OBE in the New Year’s Honours List, and in 2021 he was elected Fellow of the Royal Society.
Systematic Filter Design for Tracking Maneuvering Targets: Getting Guaranteed Performance Out of Your Sensors
Although the Kalman filter has been widely applied to target tracking applications since its introduction in the early 1960s, until recently, no systematic design methodology was available to predict tracking performance for maneuvering targets and optimize filter parameter selection. When tracking maneuvering targets with a Kalman filter, the selection of the process noise (e.g., acceleration errors) variance is complicated by the fact that the motion modeling errors are represented as white Gaussian, while target maneuvers are deterministic or highly correlated in time. In recent years, relationships between the maximum acceleration of the target and the variance of the process noise errors were developed to minimize the maximum mean squared error (MaxMSE) in position for multiple filter types. Lower bounds on the variance of the motion modeling errors were also expressed in terms of the maximum acceleration. This tutorial presents rigorous procedures for selecting the optimal process noise variance for the Kalman filter based on properties of the sensor and target motion model. Design methods are presented for the nearly constant velocity (NCV) Kalman filter with discrete white noise acceleration (DWNA), continuous white noise acceleration (CWNA), or exponentially-correlated acceleration errors (ECAE) and the nearly constant acceleration (NCA) Kalman filter with Discrete Wiener Process Acceleration (DWPA). Filter design for tracking maneuvering targets with linear frequency modulated (LFM) waveforms is also addressed and tracking with LFM waveforms is shown to be significantly better than tracking with an monotone waveform. The application of the design methods to radar tracking is addressed and numerous tracking examples are given. Guidelines on the inclusion of acceleration in your track filter are provided. In other words, guidelines on the use of an NCV Kalman filter versus an NCA Kalman filter are given. The design methods are applied to the Interacting Multiple Model (IMM) estimator and numerous radar tracking examples are used to illustrate the validity of the design methods. The benefit of tracking with LFM waveforms for mode estimation in the IMM estimator is also demonstrated via simulation examples.
Dr. Dale Blair received the BS and MS degrees in Electrical Engineering from Tennessee Technological University in 1985 and 1987, respectively. Currently, he is a Principal Research Engineer with the Georgia Tech Research Institute (GTRI) and GTRI Fellow. He received the Ph.D. degree in Electrical Engineering from the University of Virginia in 1998. He is senior member of the Sensors & Electromagnetic Applications Laboratory (SEAL) staff and supports the lab as a subject matter expert in multisensor-multitarget tracking, radar data processing, data fusion, modeling and simulation, and algorithm assessment. Prior to joining GTRI, he was with the Naval Surface Warfare Center, Dahlgren Division (NSWCDD) in Dahlgren, Virginia. While at NSWCDD, Dr. Blair led a project that demonstrated through a real-time tracking experiment that modern tracking algorithms can be utilized to reduce the radar time and energy required by a phased array radar to support surveillance tracking by as much as 60%. He joined GTRI in 1997. Dr. Blair is a recognized expert in the area of multitarget-multisensor tracking that includes optimal estimation, statistical signal processing, decision theory, radar resource allocation, radar signal processing, and radar systems modeling and simulation. In 2001, he was selected for the IEEE Fred Nathanson Award for Outstanding Young Radar Engineer for advancement of multitarget-multisensor tracking and radar resource allocation. In 2002, he was elected to the grade of IEEE Fellow for technical leadership and contributions to developing multitarget-multisensor tracking technology and applications. In 2008, Dr. Blair published his first paper on the design of Kalman filters for tracking maneuvering targets, a problem that had been open since the 1960s. His first paper addressed the design of nearly constant velocity (NCV) Kalman filters for tracking maneuvering targets. His publications since that time have enhanced the design methods for NCV Kalman filters and extended those methods to nearly constant acceleration Kalman filters and NCV Kalman filters with exponentially-correlated acceleration errors.
Detection, Estimation, and Resource Allocation in Distributed Radar Networks
The task of maintaining effectiveness of single platform-based radar systems, which employ sub-6 GHz radio frequency (RF) band, is becoming difficult due to ever increasing spectral congestion. Emerging radars need to enhance their spectrum utilization capabilities by providing resilience against interference. This can be achieved by deploying distributed radar networks, in which nodes collaboratively perform sensing and communications, such as dynamic spectrum sensing, resource allocations, detection, parameter estimation, and tracking. New algorithms, protocols, and experiments are required for enhancing distributed performance, with only limited information sharing, and minimum algorithmic and system design complexity. The effects of communications reliability and latency, imperfect synchronization, and cognitive capabilities on distributed radar performance need to be thoroughly understood. This tutorial provides a comprehensive overview of the research on the state-of-the art distributed radar networks, an in-depth understanding of new methodologies and solutions, and a summary of the key challenges for the implementation. The tutorial will cover following topics: a) motivation, b) distributed network architectures, c) overview of the related works, c) bandwidth and carrier frequency allocation algorithms, d) distributed detection, e) distributed estimation of target angles, delays, and Doppler, f) impact of reliable communications on distributed sensing, and g) implementation challenges and future research.
Batu K. Chalise received his M.Sc. and Ph.D. degrees in Electrical Engineering from the University of Duisburg-Essen, Germany. He is Associate Professor in the Department of Electrical and Computer Engineering (ECE), New York Institute of Technology. His research interests include optimization, signal processing, and machine learning for wireless, radar, and integrated radar sensing and communications systems. He has published 4 book chapters, and 87 peer reviewed articles in premier IEEE journals and IEEE flagship conferences. His research has been funded by the Air Force Research Lab (AFRL), Army Research Lab, and Naval Research Lab. He is the recipient of the AFRL Summer Faculty Research Fellowship (SFRF) in 2016 and 2020, and the recipient of the Office of Naval Research SFRF in 2020. Dr. Chalise is also the recipient of 2021 IEEE Region 1 Technological Innovation Award (Academic) for his contributions on joint radar and communications. He currently serves as an Editor for IEEE Transactions on Wireless Communications. He has been regularly serving as a Technical Program Committee (TPC) member, a Session Chair, and a reviewer for IEEE journals and conferences on signal processing, communications, and radar. He was the co-organizer of special sessions in IEEE Radar Conference in 2023 (San Antonio) and 2022 (New York City), and IEEE ICASSP, New Orleans, March 2017. In 2013, Dr. Chalise received an exemplary reviewer award from IEEE Communications Society. He is the Senior Member of IEEE and served as an Associate Editor of EURASIP Journal of Wireless Communications and Networking from 2013 to 2017.
Ultra Wideband Surveillance Radar
Description: Ultra Wide Band Surveillance Radar is an emerging technology for detecting and characterizing targets and cultural features for military and geosciences applica5ons. It is essential to have fine range and cross-range resolu5on to characterize objects near and under severe clutter. This lecture is divided into five parts.
• The Early History of Battlefield Surveillance Radar: Battlefield surveillance from manned and unmanned aircraft, along with early experiments in fixed and moving target detection and foliage penetration are covered.
• UWB Phased Array Antenna: Wideband waveforms place a significant demand on the ESA design to maintain gain and sidelobe characteristics. Design of ESA systems with time delay steering and digital beamforming will be illustrated.
• UWB Synthetic Aperture Radar (SAR): A brief descrip5on of key UWB surveillance SAR systems will be provided, along with illustrations of the SAR image and fixed object detection capability.
Mark E. Davis has over 45 years experience in Government and Industry in developing technology and systems for Radar and Electronic Systems. In 2008 he established medavis consulting as a Sole Proprietorship, to assist in review and development of advanced sensor systems, with customers in Government, Industry and Small Businesses. He held senior management positions at DARPA as Deputy Director Information Exploitation Office (2006-08), Technical Director for Air Force Research Laboratory Space Based Radar Technology (1998-2006) and Program Manager in DARPA Information Systems Office for Counter CC&D technologies (1995-1998). Dr Davis also had senior Engineering and Program Management positions with General Electric Aerospace, and General Dynamics Missile Systems. His interests are in Radar and microwave system design, phased array antennas and adaptive signal processing. Dr Davis is a Life Fellow of the IEEE, a Fellow of the Military Sensing Symposia, and Chair of the IEEE Radar Systems Panel. Within the IEEE Aerospace and Electronics Systems Society, he has been a member of the Board of Governors (2008-2013) holding positions of VP of Conferences (2010-2012) and VP of Finance (2013). Dr Davis is currently serving on the US Air Force Scientific Advisory Board, and is a member of the NASA review board on earth resource monitoring. He has received a PhD in Physics from The Ohio State University, and Bachelor and Masters Degrees in Electrical Engineering from Syracuse University. In addition to these technical duties, he has published over 75 journal and conference papers on Radar and Microwave Systems. More recently, he has authored a book Foliage Penetration Radar – Detection and Characterization of Objects under Trees published by Scitech Publishing in March 2011, and a Chapter on Principals of Modern Radar on FOPEN.
Advanced Radar Detection and Applications
We teach advanced radar detection from first principles and develop the concepts behind Space-Time Adaptive Processing (STAP) and advanced, yet practical, adaptive algorithms for realistic data environments. Detection theory is reviewed to provide the student with both the understanding of how STAP is derived, as well as to gain an appreciation for how the assumptions can be modified based on different signal and clutter models. Radar received data components are explained in detail and the mathematical models are derived so that the student can program their own MATLAB or other simulation code to represent target, jammer and clutter from a statistical framework and construct optimal and suboptimal radar detector structures. The course covers state-of-the-art STAP techniques that address many of the limitations of traditional STAP solutions, offering insight into future research trends.
Dr. Scott Goldstein is a Senior Vice President at Parsons and has spent a career at executive levels in government, industry and academia developing / applying cutting edge technology solutions across multiple application areas. He achieved the rank of Major General in the United States Air Force and has served as an executive in industry where he led organizations as well as served as a Chief Technology Officer, Chief Strategy Officer and Chief Scientist. He has performed fundamental research and development in Radar detection and estimation theory, Space Time Adaptive Processing and advanced systems concepts. He is a Fellow of the IEEE and a member of the IEEE Radar Systems Panel. He received the 2002 IEEE Fred Nathanson Radar Engineer of the Year Award and the 2019 IEEE Warren D. White Award for Excellence in Radar Engineering.
Dr. Mike Picciolo is Senior Technical Director at Anduril Industries, in the Growth and Electronic Warfare organizations. Previously, he was Director of Mission Engineering in the Engineering, Integration and Logistics Division at SAIC. Previously he served as Chief Technology Officer, NSS Division, at ENSCO. Prior, he was the Associate Chief Technologist for Dynetics and Chief Engineer of the Advanced Missions Solutions Group in Chantilly, VA. He has in-depth expertise in Radar, ISR systems, Space Time Adaptive Processing and conducts research in advanced technology development programs. Has deep domain expertise in SAR/GMTI radar, communications theory, waveform diversity, wireless communications, hyperspectral imagery, IMINT, SIGINT, and MASINT intelligence disciplines. He is a member of the IEEE Radar Systems Panel, received the 2007 IEEE Fred Nathanson Radar Engineer of the Year Award, the 2018 IEEE AESS Outstanding Organizational Leadership Award, and founded the IEEE Radar Summer School series.
Introduction to Electronic Warfare
An introduction to electronic warfare (EW) concepts and principles necessary for modern combat systems. The intent is familiarize the audience with EW concepts and achieve an understanding of how EW is used to interrupt radar processing chains. This talk covers a general discussion on the EW field, including applications outside radar specific uses and terminology widely used within the field. A historical development of the EW field will be presented to motivate importance and historical use. Basic EW techniques (e.g. noise, range/velocity techniques, etc.) with associated effects on nominal radars will be presented/discussed to ensure an understanding of the technical underpinnings of EW. Building on the basic techniques, a brief discussion on concepts in advanced EW systems and current research will be presented. The discussion will conclude by briefly presenting the revolutionary impact of cognitive and AI/ML processes on EW, which will serve as a lead in to the tutorial on Cognitive EW.
David Brown is a research engineer in the Defense & Intelligence Solutions Division at Southwest Research Institute (SwRI) where he is the lead engineer for advanced electronic warfare (EW) system research & development. His research interests are centered on applied cognitive EW, including methodologies to push AI/ML algorithms to the sensor edge, and smart data compression for congested data transport layers. Prior to joining SwRI, he held a variety of EW related research & development positions and was an adjunct professor at the Georgia Institute of Technology. In addition to engineering experience in EW, David developed experience in practical application of EW as a B-1B Electronic Warfare Officer (EWO). David received undergraduate and graduate training in electrical engineering from Georgia Tech as well as Master of Arts and Master of Divinity from Liberty University. David is a Distinguished Graduate of the Joint Electronic Warfare Officer School and is the recipient of the AOC EW Pioneer Award and RF Award. He served as the co-chair of the Sensor Open Systems Architecture (SOSA) Low Latency Subcommittee, which focused on EW specific concerns within open architecture systems. David is a senior member of the IEEE.
Introduction to Over-the-Horizon Radar (OTHR)
OTHR operates in the high frequency (HF) band (3-30 MHz) and exploits signal reflection from the ionosphere to detect and track targets at ranges well beyond the horizon (1000 to 3000 km). OTHR is attracting a resurgence of interest internationally due to the on-going requirement for persistent and cost-effective wide area surveillance as a key element of an integrated surveillance system. Australia has a strong heritage in OTHR that has resulted in the fielding of the Jindalee Operational Radar Network (JORN). Tutorial participants will gain an understanding of the fundamental principles of OTHR design and operation in the challenging HF environment to motivate and explain the architecture and capabilities of modern OTHR systems. A highlight of the tutorial is the prolific inclusion of experimental results to illustrate the practical application of techniques to real-world OTHR systems. The tutorial is intended for students, engineers, researchers, practitioners, managers and end-users with an interest in OTHR who have limited or no prior background in HF radar.
Giuseppe A. Fabrizio received his B.E. and Ph.D. degrees from the Department of Electrical and Electronic Engineering at The University of Adelaide. He has been with the Australian Defence Science and Technology Group (DSTG) since 1994 and has over 25 years of experience leading R&D in Over-the-Horizon Radar (OTHR) systems. As head of Signal Processing and Electronic Warfare for OTHR, he was responsible for the development and practical implementation of innovative and robust techniques that have enhanced the operational performance of the Jindalee Operational Radar Network (JORN). He was promoted to Group Leader for Microwave Radar Systems, where he led the realization of a real-time Active Electronically Scanned Array (AESA) radar system known as MARS and established DSTG’s Multifunction Aperture program to develop leap-ahead capabilities for broadband AESA under the Next Generation Technologies Fund. In his current role as Research Leader, he is responsible for all RF Sensing and EW capabilities in DSTG. Dr Fabrizio is a Fellow of the IEEE and is the principal author of over 60 peer-reviewed journal and conference publications. He is twice the recipient of the Barry Carlton Award for the best paper published in the IEEE Transactions on Aerospace and Electronic Systems (AES). He received the DSTG Science and Engineering Excellence award in 2007 for contributions to operational techniques in JORN and the IEEE Fred Nathanson Memorial Radar Award in 2011 for contributions to OTHR and signal processing. Dr Fabrizio has delivered many OTHR tutorials in the IEEE Radar Conference series. He currently serves an IEEE AES Society Distinguished Lecturer and is a past President of the IEEE AES Society Board of Governors. Dr Fabrizio is the author of the text “High Frequency Over-the-Horizon Radar – Fundamental Principles, Signal Processing and Practical Applications”, McGraw-Hill, NY, 2013.
Automotive Radar Principles and Challenges
Autonomous driving is one of the megatrends in the automotive industry, and a majority of car manufacturers are already introducing various levels of autonomy into commercially available vehicles. The main task of the sensing suite in autonomous vehicles is to provide the most reliable and dense information on the vehicular surroundings. Specifically, it is necessary to acquire information on drivable areas on the road and to port all objects above the road level as obstacles to be avoided. Thus, the sensors need to detect, localize, and classify a variety of typical objects, such as vehicles, pedestrians, poles, and guardrails. Comprehensive and accurate information on vehicle surroundings cannot be achieved by any single practical sensor. Therefore, all autonomous vehicles are typically equipped with multiple sensors of multiple modalities: radars, cameras, and lidars. Lidars are expensive and cameras are sensitive to illumination and weather conditions, have to be mounted behind an optically transparent surface, and do not provide direct range and velocity measurements. Radars are robust to adverse weather conditions, are insensitive to lighting variations, provide long and accurate range measurements, and can be packaged behind optically nontransparent fascia. The uniqueness of automotive radar scenarios mandates the formulation and derivation of new signal processing approaches beyond classical military radar concepts. The reformulation of vehicular radar tasks, along with new performance requirements, provides an opportunity to develop innovative signal processing methods. This Tutorial will first describe active safety and autonomous driving features and associated sensing challenges. Next it will overview technology trends and state advantages of available sensing modalities and describe automotive radar performance requirements. It will discuss propagation phenomena experienced by typical automotive radar and radar concepts that can address them. It will compare radar and LiDAR signal processing chains and emphasize their similarity, differences, and the associated processing challenges. Next this tutorial will focus on the radar processing chain: range, Doppler measurement estimation, beamforming, detection, range and angle-of-arrival migration, tracking and clustering. Discussing modern automotive radars, the tutorial will describe MIMO radar approach. Finally, the automotive radar applications and advanced topics, such as interference mitigation, and sensor fusion will be discussed.
Dr. Igal Bilik received B.Sc., M.Sc., and Ph.D. degrees in electrical and computer engineering from the Ben-Gurion University of the Negev, Beer Sheva, Israel, in 1997, 2003, and 2006, respectively. During 2006–2008, he was a postdoctoral research associate in the Department of Electrical and Computer Engineering at Duke University, Durham, NC. During 2008-2011, he has been an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts, Dartmouth. During 2011-2019, he was a Staff Researcher at GM Advanced Technical Center, Israel, leading automotive radar technology development. Between 2019 and 2020, he led Smart Sensing and Vision Group at GM R&D, responsible for developing state-of-art automotive radar, lidar, and computer vision technologies. Since Oct. 2020, Dr. Bilik has been an Assistant Professor in the School of Electrical and Computer Engineering at the Ben-Gurion University of the Negev. Since 2020, he has been a member of the IEEE AESS Radar Systems Panel and Chair of the Civilian Radar Committee. Dr. Bilik is an Acting Officer of the IEEE Vehicular Technology Chapter, Israel, and chairs the Autonomous and Connected Transportation Committee, Israeli Center for Smart Mobility Research. Dr. Bilik did Automotive Radar Tutorials in IEEE Radar Conferences 2020-2021, IEEE SAM Workshop 2022, Autosense 2022, and IEEE Radar Conference Summer School. He co-Chaired Automotive Radar Special Session in IEEE SAM, 2022, Co-chaired Automotive Radar Sessions at the IEEE Radar Conferences 2015- 2022, and chaired the “Automotive Vehicles Workshop” at The Prime Minister Smart Mobility Summit, 2021. Dr. Bilik has more than 180 patent inventions, authored more than 80 peer-reviewed academic publications, received the Best Student Paper Awards at IEEE RADAR 2005 and IEEE RADAR 2006 Conferences, Student Paper Award in the 2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, and the GM Product Excellence Recognition in 2017. Dr. Bilik is an Associate Editor in the IEEE Transactions of Aerospace Systems, IEEE Transactions on Radars, and IEEE Sensors.
Cognitive Electronic Warfare: An AI Approach
This tutorial will present an overview of how AI can be used in EW. They will describe opportunities for using AI in situation assessment and electronic support (ES), and decision-making techniques for electronic protect (EP), electronic attack (EA), and electronic battle management (EBM). We will present AI techniques from Situation Assessment, Decision Making, and Machine Learning, and discuss tradeoffs. We will describe approaches to the important issue of real-time in-mission machine learning, and evaluation approaches that demonstrate that a cognitive system that learns how to handle novel environments. The tutorial is intended to be a voice track to the 2021 book Cognitive EW: An AI Approach by Karen Haigh and Julia Andrusenko.
Dr. Karen Haigh is an expert and consultant in Cognitive EW and embedded AI. Her focus is on physical systems with limited communications and limited computation resources that must perform under fast hard- real-time requirements. In September 2021, her book “Cognitive Electronic Warfare: An Artificial Intelligence Approach”, was released by Artech House. She received her Ph.D. in from Carnegie Mellon University in Computer Science with a focus on AI and Robotics. Dr. Haigh is a Fellow of the IEEE for contributions to closed-loop control of embedded systems, and a Fellow of AAIA for outstanding achievements in the area of smart homes.
Design, Simulation, Analysis, and Testing of Radar systems
This tutorial introduces an overarching strategy for designing radars and countermeasures from the ground up, covering theoretical and practical aspects with MATLAB and Simulink demonstrations. We will start with selecting radar parameters, evaluating waveforms, and designing sensor arrays interactively. You can easily explore trade-offs with the radar range equation and link budget. We will further discuss creating realistic radar scenarios for airborne, ground-based, and shipborne platforms and targets. We will then discuss the inclusion of the Antenna and RF components in the radar system, signal processing, and data processing chain to model a multifunction phased array system.
In this tutorial you will learn how to:
- Interactively perform radar budget analysis and trade-off studies
- Create scenarios to design and test radar systems with varying fidelity including the effects of terrain and clutter
- Model and simulate radar algorithms such as waveform design, target detection, beamforming, and space-time adaptive processing
- Synthesize datasets to train AI models and improve design choices for Synthetic Aperture Radar (SAR) targets, micro doppler signatures, maritime clutter removal
- Deploy and test the models on processors, DSP, and FPGA hardware
Sumit Garg is a senior application engineer at MathWorks India specializing in design analysis and implementation of radar signal processing and data processing applications. He works closely with customers across domains to help them use MATLAB® and Simulink® in their workflows. He has ~11 years of industrial experience in the design and development of hardware and software applications in the radar domain. He has been a part of the complete lifecycle of projects pertaining to aerospace and defence applications. Prior to joining MathWorks, he worked for Bharat Electronics Limited (BEL) and Electronics and Radar Development Establishment (LRDE) as a senior engineer.
Satish Thokala is Aerospace and Defense industry manager at MathWorks®. His area of expertise is Avionics systems design for both military and civil aircrafts. He has ~20 years of experience in teaching, public and private aerospace establishments including Hindustan Aeronautics Limited and Collins Aerospace. In the current role, he is responsible to analyze technology adoption in the AeroDef industry and develop strategies to enhance capabilities of MATLAB® and Simulink® in end-to-end radar and communication system design. Early in the career, Satish designed and developed airborne communication radios. Satish led large engineering groups developing Avionics and autonomous systems. Satish delivered talks in 30+ national and international conferences.
Dr. Ying Chen is a Senior Application Engineer with 15 years’ experience in the discipline of signal processing, radar design, and wireless communications through works in both academic and industrial research institutions. Her research interests include RF front-end distortions, signal processing, and software-defined radio. Besides her research work, Ying has also contributed to various industrial prototyping and field-tested projects including satellite communication systems, passive and active radar systems.
Deep Learning for Advanced Radar Automatic Target Recognition (ATR)
The focus of this tutorial will be theory and implementation of modern artificial intelligence machine learning (AI/ML)/Deep Learning algorithms for radio frequency (Synthetic Aperture Radar, SAR) automatic target recognition (ATR). We will demonstrate hands on implementation of deep learning-based SAR ATR. For this tutorial, the author will use their recently published (July 2020) book by Artech House “Deep Learning for Radar and Communications Automatic Target Recognition“. This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. We have the video lectures, ppt slides, and a written textbook ready for the audience.
Dr. Uttam K. Majumder is a Senior Staff Scientist and Radar automation and artificial intelligence (A2I) lead for U.S. Department of Defense (May 2021- Present). His research interests include AI/ML for SAR target recognition, Radar Waveforms Design, SAR Signal and image processing, High Performance Computing, and SAR algorithms development for surveillance applications. From 2003-2021, he worked with AFRL ATR and Computation/Communications Divisions of Sensors and Information Directorates. He was a technical lead for several DARPA programs including RFMLS (Radio Frequency Machine Learning Systems), TRACE (Target Recognition and Adaption for Contested Environments), and HIVE (Hierarchical Identify, Verify, and Exploit). He led many in-house machine learning research programs. Dr. Majumder received several awards from AFRL including AFOSR STAR Team Research Award, Distinguished Research Achievement Award, and SPIE Rising Researchers Award. He is a senior member of IEEE and SPIE and a Distinguished Lecturer for IEEE AESS. Dr. Majumder earned Ph.D. in Electrical Engineering from Purdue University, West Lafayette, Indiana. Please check author’s website: Majumderfoundation.org
PCL radar – from theory to operational radar systems and future applications
The tutorial is focused on passive coherent location (PCL) radar, starting from the theory and finishing on operational systems. In the first part of the tutorial, the basics of passive radar will be presented. These include a review of possible illuminators of opportunity, bi/multi-static geometry, a typical signal processing chain.
In the second part of the tutorial, passive radar demonstrators’ designing will be presented. This will include the review of different hardware solutions that can be used for passive radar demonstrators and guidelines for demonstrator design. Examples of deployed demonstrators will be presented. Results of measurements using different systems will be shown and discussed.
The third part of the tutorial will be focused on the design and development of operational PCL systems. Architecture design considerations will be discussed. The deployment requirements and limitations will be presented, including mission planning tools. The different passive radar applications will be presented, including civilian and military ones.
In the last part, the concept of deployable multiband passive/active radar (DMPAR) will be studied, and the results of passive radar systems operating on their own as well as in cooperation with active radars, will be shown. Future applications of passive radar will be discussed, including the new frontiers in modern passive radars relating to passive radar using new wideband illuminators of opportunity, such as WiFi, 5G/6G, DVB-S and STARLINK, and the required signal processing techniques. In the end, the tutorial will be summarized, showing potential ways of further development in modern passive radars.
Prof. Mateusz Malanowski received his M.Sc., Ph.D. and D.Sc. degrees in Electrical Engineering from the Warsaw University of Technology, Warsaw, Poland, in 2004, 2009 and 2013 respectively. In 2022 he received Full Professor title from the President of Poland.He was a Research Scientist with FGAN (Forschungsgesellschaft fuer Angewandte Naturwissenschaften), Germany, and an Engineer with Orpal, Poland. Currently, he is a Professor at the Warsaw University of Technology.Prof. Malanowski is the author/coauthor of over 180 scientific papers. He is also an author of “Signal Processing for Passive Bistatic Radar” book, published by Artech House.His research interests are radar signal processing, target tracking, passive coherent location, synthetic aperture radar and noise radar. For the last 15 years he has been involved in numerous national and international projects, focusing on passive radar, synthetic aperture radar and noise radar. He has been a member of several NATO Science and Technology Organization groups. Prof. Malanowski is currently managing a project, whose aim is to develop first Polish, and one of the first in the world, operational military (TRL9) passive radar system.
Dr Steffen Lutz received his Master-degree in Systems Engineering in 2011 respectively. From 2011 till 2015 he was working on the field of mm-wave MIMO radar system design, mm-wave antenna design and RF measurement techniques. In 2015 he received his Ph.D. in electrical engineering from the University of Erlangen-Nuremberg and joined Airbus Defence and Space as radar system engineer in the area of MIMO short range and passive radar. Since 2017 he is the lead engineer for the Hensoldt Sensors Passive Radar program including the product development up to TRL8 and the R&T activities. His research interests include system design for active and passive radars, sensor signal array and signal processing and future radar concepts for air surveillance and ground based air defence. He has authored and co-authored more than 30 technical reviewed paper and patents. In 2021 he received with his team the NATO SET Panel Excellence Award for the contributions to the at the NATO APART-GAS trials as part of the work in NATO SET 258.
Prof. Piotr Samczynski received his B.Sc. and M.Sc. degrees in electronics and Ph.D. and D.Sc. degrees in telecommunications, all from the Warsaw University of Technology (WUT), Warsaw, Poland in 2004, 2005, 2010 and 2013 respectively. Since 2018, he has been the Associate Professor at the WUT; and since 2014 – a member of the WUT’s Faculty of Electronics and Information Technology Council. Prior to this, he was Assistant Profesor at WUT (2018-2010), a research assistant at the Przemyslowy Instytut Telekomunikacji S.A. (PIT S.A.) (2010-2005), and the head of PIT’s Radar Signal Processing Department (2010-2009). He is the Founder of XY-Sensing Ltd., where from 2018, he is held the CEO position.Prof. Samczynski’s research interests are in the areas of radar signal processing, passive radar, synthetic aperture radar, and digital signal processing. He is the author of over 200 scientific papers. Piotr Samczynski was involved in several projects for the European Research Agency (EDA), Polish National Centre for Research and Development (NCBiR), and Polish Ministry of Science and Higher Education (MiNSW), including the projects on SAR, ISAR, and passive radars. For his work, he was honored in 2020 with the Bronze Cross of Merit awarded by the President of the Republic of Poland. Since 2009, he has been a member of several research task groups under the NATO Science and Technology Organization (STO). He supports the research work and significantly contributed to numerous Sensors and Electronics Technology (SET) activities, particularly those related to the fields of radar signal processing, modern passive and active radars architectures, and noise radars. In 2018-2022 he was a Chair of the NATO SET-258 research task group (RTG) on Deployable Multiband Passive/Active Radar (DMPAR) deployment and assessment in military scenarios. Since 2023, he has been a Chair of the NATO SET-320 RTG on New Frontiers in Modern Passive Radar. In recognition of his pivotal role in enhancing the SET Programme of Work, Dr. Piotr Samczynski was presented in 2020 with the NATO STO SET Panel Early Career Award.Prof. Samczynski is an IEEE member since 2003 and IEEE Senior member since 2016. He is a member of IEEE AES, SP, and GRS Societies, and during 2017-2019 Prof. Samczynski was a Chair of the Polish Chapter of the IEEE Signal Processing Society. Since 2019, he is handling the position of Vice-Chairman (AES) of the IEEE Poland APS/AESS/MTTS Joint Chapter. He received IEEE Fred Nathanson Memorial Award for outstanding contribution to the field of passive radar imaging, including systems design, experimentation, and algorithm development, in 2017.
Computational Methods in Radar Imaging
Recent advances in inverse problems and learning have shifted the design paradigm for sensing systems. Computational methods are now an integral part of the design toolbox, using algorithms to address hardware limitations. A very promising application has been in radar imaging, which is becoming increasingly important in applications including robotics, autonomous driving, and medical imaging, among others. This tutorial will present a general inverse problem and learning framework for array processing systems, which describes both the acquisition hardware and the scene being acquired. Under this framework we can exploit knowledge, learned or designed, on the scene, the system, and the nature of a variety of errors that might occur. The result is significant improvements in the reconstruction accuracy. Furthermore, we consider the design of the system itself in the context of the inverse problem, leading to designs that are more efficient, more accurate, or less expensive, depending on the application.
Petros T Boufounos (S’02–M’06–SM’13) received the S.B. degree in economics in 2000, the S.B. and M.Eng. degrees in electrical engineering and computer science (EECS) in 2002, and the Sc.D. degree in EECS in 2006, all from the Massachusetts Institute of Technology, Cambridge, MA, USA. Between September 2006 and December 2008, he was a Postdoctoral Associate with the Digital Signal Processing Group, Rice University, Houston, TX, USA. He joined Mitsubishi Electric Research Laboratories, Cambridge, MA, USA, in January 2009, where he is currently a Senior Principal Research Scientist and the Computational Sensing Senior Team Leader. He is also a visiting scholar with the Electrical and Computer Engineering Department, Rice University. His immediate research focus includes signal acquisition and processing, inverse problems, frame theory, quantization, and data representations. He is also interested in how signal acquisition interacts with other fields that use sensing extensively, such as machine learning, robotics, and dynamical system theory. Dr. Boufounos was as an Area Editor and a Senior Area Editor for the IEEE signal processing letters. He has been a part of the SigPort editorial board and is currently a member of the IEEE Signal Processing Society Theory and Methods technical committee and an SPS Distinguished Lecturer for 2019-2020.
Hassan Mansour (S’99–M’09–SM’17) received the B.E. degree in computer and communications engineering from the American University of Beirut, Beirut, Lebanon, in 2003, and the M.A.Sc. degree in electrical and computer engineering and the Ph.D. degree in electrical and computer engineering from The University of British Columbia, Vancouver, BC, Canada, in 2005 and 2009, respectively. Between January 2010 and January 2013, he was a Postdoctoral Research Fellow with the Department of Computer Science, the Mathematics Department, and the Department of Earth, Ocean, and Atmospheric Sciences, The University of British Columbia. He is a Senior Principal Research Scientist and Computational Sensing Team Leader with Mitsubishi Electric Research Laboratories, Cambridge, MA, USA. His research interests are in inverse problems, machine learning, compressed sensing, sparse signal reconstruction, image enhancement, and scalable video compression and transmission. His current research is focused on the design of efficient acquisition schemes and reconstruction algorithms for natural images, radar sensing, video analytics, and seismic imaging. Dr. Mansour is a member of the IEEE Signal Processing Society Computational Imaging Technical Committee. He was an Associate Editor for the IEEE Transactions on Signal Processing between 2018 and 2022.