Tutorials

Synthetic Aperture Radar (SAR) is a radar imaging mode that maps radar reflectivity of the ground. This is an important earth resource monitoring and analysis tool in the civilian and government communities, and an important intelligence, surveillance, and reconnaissance (ISR) tool for the military and intelligence communities. This tutorial intends to provide an introduction to the physical concepts, processing, performance, features, and exploitation modes that make SAR work, and make it useful. Although mathematics will be shown in some parts of the presentation, the lecture will focus on the qualitative significance of the mathematics rather than dry derivations. Basic data models will be developed, and several image processing algorithms will be illustrated and compared. The radar equation for SAR will be explored in some detail to illustrate how SAR operating parameters can be traded for performance as measured by the Signal-to-Noise Ratio (SNR) for a target, and equivalently the Noise-Equivalent Reflectivity (NER). The unique nature of range-Doppler images will be discussed, including geometric distortions due to range-Doppler imaging such as wavefront curvature effects and layover. In addition, examples of SAR image dependence on wavelength, polarization, and atmospheric effects will be illustrated. Post-image-formation processing of SAR images will be exemplified, including autofocus, speckle reduction, and dynamic range compression for image display. Basic SAR image-quality metrics will be presented. Finally, a number of SAR image exploitation techniques and modes will be illustrated. Liberal use of example SAR images and other data products will be used to illustrate the concepts discussed.

This tutorial is focused on passive radar and illustrates the amazing solutions that can be adopted in order to increase their reliability and hence widen the range of applications.

The tutorial starts from the basic concepts by discussing the possible illuminators of opportunity, the impact of the geometry, as well as the passive radar equation. A typical signal processing scheme is introduced and effective solutions are illustrated for the signal processing techniques to be implemented at each stage.

Ground based passive radar systems are first investigated, including the demonstrator and operational system design and mission planning. Therefore, advanced methods are illustrated to enhance the performance of the passive radar sensor by exploiting long integration times or polarization/frequency/spatial diversity. Then the discussion moves to passive radar onboard moving platforms, which enables SAR and GMTI modes. The principle of operation, the signal models, and the signal processing techniques are illustrated with reference to both approaches.  Target imaging with passive ISAR is also considered along with the required techniques and main challenges.

In addition to the theoretical aspects, the tutorial provides the attendees with an insight into the real‐world applications of passive radar. A wide range of applications is covered moving from air traffic control up to indoor surveillance and several experimental results are reported exploiting different illuminators. Walking through these results gives the chance to describe in more detail technical aspects and signal processing techniques as well as to understand the current limitations and future perspectives of passive radar sensing.

An introduction to electronic warfare (EW) concepts and principles necessary for modern combat systems.  The intent is to 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 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 (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, including Digital RF Memory (DRFM) technology and testing of advanced of EW systems.  Open EW architectures will be discussed to present how open architecture enables advanced capability and rapid technology updates.  The discussion will conclude by briefly presenting advanced EW research, including the revolutionary impact of cognitive and AI/ML processes on EW.

The intended audience is engineers (students through experts) who are interested in gaining an understanding of Electronic Warfare systems that are used to counter radars.  This is introductory material, so it is accessible to all with a basic understanding of RF and radar concepts.  An additional outcome is the fostering of a collaborative environment between radar and EW professionals, which have traditionally been separated.  An implicit outcome is the exposure of the typically secretive EW field to a wider talent pool.

The micro-Doppler analysis is the study of the time-varying Doppler frequencies from multiple moving scattering centers of targets. Over the past few years, the potential of micro-Doppler signature analysis has been showcased in different areas of radar signal processing, such as improved target detection, characterization and tracking, in a variety of applications including condition monitoring, urban and airspace surveillance, healthcare, automotive, and manufacturing. Combined with the recent advances in machine learning and artificial intelligence, micro-Doppler analysis is a great tool to perform automatic target recognition.

This tutorial is broadly divided into two parts. In the first part, the fundamentals of the phenomenology of micro-Doppler signatures and related signal processing will be introduced with reference to the canonical cases of rigid bodies, then extended to non-rigid bodies. In the second part, different applications of radar micro-Doppler signature analysis will be discussed with reference to a common classification framework, either using more conventional extracted features or neural networks. These advanced applications will include micro-Doppler for UAVs classification, micro-Doppler-based ballistic threats discrimination, micro-Doppler in Industry 4.0 and AgriTech, hand gesture recognition and vital sign monitoring, and human activities classification targeting continuous actions in a sequence. An overview of the main techniques and of some of the open datasets available in the literature that can support research in this direction will also be provided.

The electromagnetic spectrum (EMS) is a precious resource that connects and protects our societies across the globe. Historically this resource was accessed by expensive, purpose-built radio-frequency (RF) systems that operated in well-defined, static frequency allocations. Recent advances in digital radio technology (e.g. software-defined radios, low-cost/high-sample rate analog-to-digital converters, etc.) have made wide swaths of spectrum easily accessible by low-cost, commercially available systems. This new accessibility has resulted in a heated competition between commercial telecommunications, civil infrastructure, scientific research, and defense interests for access to the finite, limited EMS. Consequently, the spectrum has become increasingly congested with no end in sight for the increasing, insatiable demand by competing users. To mitigate this congestion, it is vital that future users of the spectrum do so in an efficient manner. As radar and communication systems pose the greatest demand on spectrum access, their future designs must make use of all degrees-of-freedom (DoFs): time, frequency, space, coding and polarization.

This tutorial will provide a first-principles examination of the design goals and metrics of both radar and communications. We will explore the motivation and history of spectrum access and examine the practical requirements for utilizing the available DoFs. Specific examples of coexistence and co-design techniques will be explored based on the DoF(s) they use to enable efficient spectrum access. For the co-design problem two distinct families of techniques will be framed and explored in detail: radar-embedded communications via coding diversity and multi-beam emissions from digital arrays. Implications of hardware constraints on these techniques will be illustrated. To narrow the focus radar detection will be the primary radar application.

Phased array communications and radar systems are finding increased use in a variety of applications. This places a greater importance on training engineers on these concepts and in rapidly prototyping new phased array designs. However, both those imperatives have historically been difficult and expensive. But in this hands-on workshop, we will use the CN0566 8 Channel Software Defined Phased Array (the “Phaser”, from Analog Devices) to understand and experience these concepts. Fifteen identical lab stations will be setup to for the participants to build, program, and interact with their own phased array system. We will start at the beginning – with phased array fundamentals. Then we will methodically work through these concepts, step by step, until we have a complete operational beamformer. Each new concept will have a short lecture describing the theory and math, followed by the participants using the Phaser hardware to directly explore the lecture topic. The key topics we will cover are:

- Steering angle and beam formation:  You will directly experiment with the impact of phase shifting, steering angle, and number of elements on the beam pattern.
- Antenna sidelobes:  Where do they come from and how do we mitigate them? You will dynamically apply different tapering profiles and observe the impact to sidelobe suppression, beamwidth, and gain.
- Grating Lobes:  Where does the half lambda spacing rule come from, and what happens if we violate it? 
- Virtual Arrays:  Reduce the antenna beamwidth by using switched transmitters. 
- Monopulse Tracking:  Use digitized subarrays to model and track the position of a target.
- Null Steering:  Apply beamforming techniques to place a null at a jammer’s location, while preserving the beamwidth and gain in the desired direction.  

Relevant links for the workshop:
https://www.analog.com/cn0566
https://wiki.analog.com/phaser
http://pysdr.org/

Today, more than 50 spaceborne SAR systems are systematically monitoring the Earth’s surface. SAR is unique in its imaging capability: It provides high-resolution imaging independent from daylight, cloud cover and weather conditions for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2D and 3D mapping, change detection, 4D mapping (space and time), disaster monitoring, security-related applications up to planetary exploration. Therefore, it is predestined to monitor dynamic processes on the Earth’s surface in a reliable, continuous and global way. In the past few years a new era has started for spaceborne SAR systems with the number of satellites fast increasing due to NewSpace SAR initiatives. These small satellites complement the large full-fledged SAR systems with global coverage, building a network of satellites which is able to provide sub-daily coverage in a reliable way. Looking ahead, future spaceborne SAR systems with advanced imaging modes and digital beamforming will have an imaging performance one order of magnitude superior than that of current systems. Innovative multistatic SAR concepts will allow for new information products opening the door for a wealth of novel applications.

This four-hour tutorial provides a wide overview on spaceborne Synthetic Aperture Radar (SAR) systems and is suitable for engineers, graduate and PhD students as well as practitioners which have already a basic knowledge on radar systems. The course’s contents will be as follows: SAR basics, SAR theory, SAR signal processing, image properties, SAR imaging modes, SAR system concept and design, spaceborne SAR missions, interferometry, polarimetry, tomography, applications, advanced SAR technologies (e.g., digital beamforming), innovative SAR concepts and techniques (e.g., multistatic SAR) and future developments.

The purpose of this tutorial is to provide a serious exposition of the state-of-the-art of passive radar and its development in the context of target detection and imaging. The tutorial will focus on developing the grounding of advanced principles and concepts that are, and will be, of high relevance to the field. After an introduction the tutorial will move on to develop advanced topics, where new frontiers in passive radar for metropolitan area applications based on modern mobile communication standards and for short area applications will be discussed including different standards comparison regarding the sensing applications. By the conclusion of the tutorial, participants will have acquired a deep appreciation of core advanced topics relating to passive radar using new wideband illuminators of opportunity, such as 5G/6G, WiFi, DVB-S and Fixed Satellite Services (FSS, such as STARLINK and OneWeb), and the required signal processing techniques. The tutorial will include different standards comparison, challenges, opportunities and limitations analyzes with focus on modern applications for using wideband IoOs in passive radars, e.g., target detection, classification, SAR/ISAR imaging that participants would not have accrued through self-study of recently published literature. The challenges, opportunities and limitations analyses will be presented to the audience supporting by numerous examples of experimental results. Worked examples with interactive participation will ensure a lively tutorial for the full duration.

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. 

SAR/ISAR images have been largely used for earth observation, surveillance, classification and recognition of targets of interest. The effectiveness of such systems may be limited by a number of factors, such as poor resolution, shadowing effects, interference, etc. Moreover, both SAR and ISAR images are to be considered as two-dimensional maps of the real three-dimensional object. Therefore, a single sensor may produce only a two-dimensional image where its image projection plane (IPP) is defined by the system-target geometry. Such a mapping typically creates a problem for the image interpretation, as the target image is only a projection of it onto a plane. In addition to this, monostatic SAR/ISAR imaging systems are typically quite vulnerable to intentional jammers as the sensor can be easily detected and located by an electronic counter-measure (ECM) system. Bistatic SAR/ISAR systems can overcome such a problem as the receiver can act covertly due to the fact that it is not easily detectable by an ECM system, whereas multistatic SAR/ISAR may push forward the system limits both in terms of resolution and image interpretation and add to the system resilience.

The surge in the ability to design and manufacture large arrays at higher frequencies (10 GHz-60 GHz), coupled with increased reconfigurability in array parameters, is fueling a renewed interest in adaptive array techniques. Concurrently, advancements in high-speed converters and signal processors are sparking a growing fascination with realizing digital beamforming techniques. These techniques aim to enhance Signal-to-Interference-plus-Noise Ratio (SINR), throughput, Error Vector Magnitude (EVM), Bit Error Rate (BER), and more. Comprehending the foundational concepts behind these advancements necessitates a profound grasp of linear algebra, optimization theory, array processing, RF design, and signal processing. As a consequence, entering this field presents a steep learning curve, posing substantial yet rewarding challenges even for seasoned experts.

In this workshop, we aim to demystify the intricacies surrounding adaptive beamforming techniques and offer participants a hands-on experience with bleeding-edge beamforming systems. The workshop will guide participants through the underlying mathematics, fostering an intuitive understanding of how the math translates into practical applications. Moreover, it will showcase what the implemented math looks like on a real array. Participants can anticipate leaving the workshop with a heightened understanding of key adaptive beamforming techniques, along with the confidence to implement these techniques on real antenna array systems. While the workshop does not have any prerequisites, familiarity with linear algebra and antenna theory would give the participants a competitive edge.  

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 a 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.
 

Inverse Synthetic Aperture Radar (ISAR) is a well-known technique to obtain high-resolution radar images of non-cooperative targets. ISAR images have been largely used to classify and recognise targets and ISAR technology is nowadays employed and integrated in modern radar systems. Nevertheless, despite decades of research and development work in ISAR imaging, two-dimensional (2D) ISAR images present some intrinsic drawbacks that limit the effectiveness of their use for target classification and recognition. Some of these limitations come from the unpredictability and uncontrollability of the image projection, which transforms three-dimensional (3D) targets in 2D images. One very effective way of overcoming this problem is to form 3D ISAR images instead of 2D ones.

This tutorial will present a unique walkthrough 3D ISAR imaging, including concepts, algorithms, systems and real data examples, which will provide the attendants the necessary tools for a full understanding of this new technology. 

Active Electronically Scanned Arrays (AESAs): Fundamentals and Applications delivers a foundational treatment of AESAs ideal for engineering students and professionals. An overview is provided of the primary subsystems of an AESA. Detailed explanations are provided on the impact of AESAs on mission applications including Radar, Electronic Attack (EA), Electronic Support Measures (ESM), SIGINT and Communications.

A review of AESA fundamentals is covered including topics such as grating lobes, scan loss, instantaneous bandwidth, and 1D, 2D, and conformal array analysis. Comprehensive explorations of key design concepts and fundamentals are provided for subsystems inclusive of antenna array elements, transmit/receive modules, and beamformer including their purpose, functions, and practical design considerations. Performance results for various AESA architectures often found in industry, including analog, subarray, and digital beamforming AES architecture, are discussed. Key advantage of elemental digital beamforming in contrast with analog and subarray beamforming is also provided with an extension to adaptive array nulling for operation in the presence of jamming. With a focus on practical knowledge and applications, this tutorial offers an accessible overview of technology critical to the implementation of collision avoidance in cars, air surveillance radar, communication antennas, and defense technologies. This course is ideal for professionals working with AESAsfor Radar, EW, SIGINT or Communication systems

This tutorial has been motivated by a) the rising demand for multi-functional RF systems to provide adaptivity and serve multiple tasks in an energy-, hardware-, space-, spectrum- efficient way, b) the recent interest in Dual-Functional Radar-Communication (DFRC) systems featuring in numerous IEEE workshops and special issues across the IEEE AESS and ComSoc societies, and an IEEE Emerging Technology Initiative on Integrated Sensing and Communications. In line with this research agenda, there has been increasing innovation in agile multi-function RF systems that have the capability the meet the requirements for a system that needs to perform both sensing and communications. At the same time, DFRC has drawn significant attention in the wireless cellular domain, not just from academic researchers, but also from major industrial companies. Recently, Ericsson, Nokia, NTT DOCOMO, Intel, and Huawei have all suggested that sensing will play an important role in their 6G white papers and Wi-Fi 7 visions. In particular, in July 2023 wireless sensing was endorsed as one of six key 6G usage scenarios by the United Nations’ International Telecommunication Union.

Accordingly, we aim to provide an overview, in a uniform manner, of the recent advances and new insights in multi-function RF systems and how they can deliver DFRC concepts. This tutorial proposal identifies the following objectives:

• Establish a unified view of radar and communication functionalities in promoting a paradigm shift to move beyond the competitive Radar vs Communications co-existence. The previously competing radar and communication operations can be jointly optimized to improve the efficiency and to reduce the costs, via the shared use of a single hardware platform and a joint signal processing framework;
• Highlight the key building blocks of a multifunctional RF system, the array of emerging applications it will be expected to support, and the fundamental functionalities it will need to offer;
• Stimulate a discussion and promote research to further ways of delivering multi-function RF systems in view of emerging applications such as smart cities, urban security, and intelligent transportation.

Due to an explosive growth of wireless devices and networks, the sub-6 GHz radio frequency (RF) band used by many radar applications is highly congested, which is one of the major bottlenecks for enabling effective operations and functionality of the conventional single platform-based radar systems. Emerging radar systems need to enhance their spectrum utilization capabilities by becoming resilient against interference. This goal can be achieved with a deployment of a radar network, in which nodes collaboratively perform distributed radar sensing and communications, such as dynamic spectrum sensing, resource (bandwidth, carrier frequency, waveform) allocation, detection, parameter estimation, and tracking. New algorithms, protocols, and experiments are required for distributed radar networks to achieve either optimal or close to centralized network performance, with only limited information sharing, and minimum algorithmic and system design complexity. The effects of communications link reliability and latency, imperfect synchronization, and cognitive capabilities on distributed radar sensing performance need to be thoroughly understood.

The objective of this tutorial is to provide participants with a comprehensive overview of the research on the state-of-the-art distributed radars, an in-depth understanding of new methodologies and solutions, and a summary, highlighting the prospects and key challenges on the implementation of distributed radar networks. The tutorial will cover following aspects of network radar: a) motivation and overview of the related work, b) network architecture, c) bandwidth and carrier frequency allocation algorithms for both radar sensing and communications, d) radar waveform optimization, e) distributed detection and target parameter estimation, f) impact of reliable communications on distributed radar sensing, and g) future research directions to overcome implementation challenges.

In the tutorial, the concept of continuous-wave radar emitting noise or pseudo-noise waveform will be presented. Noise waveforms have significant advantages over classical radar waveforms, as they do not have range nor Doppler ambiguities and can be used in dense electromagnetic environments without significant interferences with other devices using the same spectrum.

In the tutorial, the basics of noise radar will be presented. Problems typical for noise radar, such as the masking effect, will be identified, and solutions to those problems will be analyzed. Pulse noise radar will also be presented and compared with classical deterministic pulse radar. The possibilities of target identification using micro-Doppler, SAR, and ISAR imaging will be discussed. The waveform design for noise radar will be shown, including sidelobe reduction and spectrum shaping. Operation of the noise radar in MIMO configuration, both using co-located and spatially separated antennas, will be analyzed.

In the tutorial, numerous real-life result examples will be shown. Possible applications of noise radar will be analyzed.