Tutorials

The tutorial begins with a concise historical overview of Active Electronically Scanned Array (AESA) development, tracing its evolution from early electronically steered concepts of the 1960s to modern fully elemental digital architectures. Key distinctions among mechanically scanned arrays (MSAs), passive electronically scanned arrays (PESAs), and AESAs are examined, highlighting the technological drivers that enabled each progression. The discussion then summarizes the operational advantages that AESAs bring to diverse mission areas—including radar, electronic attack and support (EA/ESM), SIGINT, and communications. Using the radar range equation as a foundation, the tutorial illustrates how array size, element count, transmit/receive module (TRM) architecture, and beamforming approaches influence end-to-end system performance. The section concludes with an introduction to fully elemental digital arrays, emphasizing their enhanced capabilities and the significant performance and architectural benefits they enable.

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. The tutorial proposed herein is intended 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. Liberal use of example SAR images and other data products will be used to illustrate the concepts discussed. The presentation will be given as four distinct modules, each based on (but enhanced from) presentations developed and given by the presenter in numerous non-
public forums to government, military, industry, and academic groups.

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.

Antennas are an integral and important part of almost any radar system. Most common types of radar antennas include reflector antennas, phased arrays as well as conformal arrays. In the field of antenna engineering, theoretical analysis is of paramount importance in understanding the basics of the antenna radiation characteristics. While the basic concept of antennas is well known, closed form, exact analytical solutions to many antenna problems are not practical and impossible in many cases. Advances in
electromagnetic (EM) simulations have significantly impacted the antenna design process by providing exact solutions by solving Maxwell’s equations using numerical methods. It is a common practice now in academia and industry to use various commercially available EM simulation tools for antenna design process. In this tutorial, we will introduce basics of antenna modeling and simulation process with pros and cons of various numerical methods, such as Method of Moments (MoM), Multilevel Fast Multipole Method (MLFMM), Finite Element Method (FEM), Finite Difference Time Domain (FDTD), Physical Optics (PO), Ray Lunching Geometrical Optics (RL-GO), and Uniform Theory of Diffraction (UTD). We will then discuss modeling and simulation of various antenna types, starting from simple configurations such as dipoles
and loops and eventually leading to more complicated and practical designs such as microstrip patches, antenna arrays and high-gain reflector antennas.

The tutorial focuses on passive coherent location (PCL) radar, starting from the theory and concluding with an overview of new frontiers in modern passive radars. In the first part of the tutorial, the fundamentals of passive radar will be presented. These include a review of possible illuminators of opportunity and features of different signals from the point of view of radar detection. The bi/multi-static geometry will be presented, and the passive radar equation will be analyzed. Next, a typical signal processing chain, consisting of clutter filtering, cross-ambiguity function calculation, detection, bistatic tracking, and Cartesian tracking, will be described. In the second part of the tutorial, the design of passive radar demonstrators will be presented. This will include a 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 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 (from GSM to 5G/6G, WiFi, among others). The challenges, opportunities and limitations analyses will be presented to the audience, supported by numerous examples of
experimental results. In addition to constructing the theoretical narrative fundamental to the advanced pillar topics, a comprehensive set of results will illustrate concepts and aid understanding. In the last part, different passive radar applications will be presented, including civilian and military ones. Some results of using passive radar systems operating on their own as well as in cooperation with active radars will be shown. In the final part of the tutorial, the passive radar applications and potential
exploitation of new illuminators of opportunity from space will be discussed. Future applications of passive radar will be discussed.

Millimeter-wave radar is now everywhere—automotive safety systems, presence detection for smart buildings, contactless vital sign monitoring and even drone surveillance. But whether you're
developing a smart home sensor or a counter-UAS system, success depends on understanding the fundamentals: the physics of the waveform, the hardware constraints, and the signal processing chain—not just consuming vendor-provided point clouds. This tutorial provides that foundation through live demonstrations on real hardware. Using a 60 GHz 4×4 MIMO development platform (AKM AK5818, 57–64 GHz bandwidth), we follow the complete processing pipeline from chirp transmission through tracked targets. The tutorial uses a purpose-built software interface that abstracts hardware complexity, letting attendees focus on the radar system and signal processing. The tutorial is structured around the end-to-end radar DSP chain: starting with the raw data cube populated by IF samples, we progress through range processing, Doppler processing, and angle processing to generate detected target lists, and finally extend to point cloud generation and multi-frame tracking. Each processing stage is demonstrated live. When we discuss the beat frequency relationship, we show the IF signal spectrum from a real target. When we explain Range-Doppler maps, we display them in real-time with a small drone as the moving target. When we describe angle estimation, we compare classical beamforming with subspace methods on measured data. This “see it working” approach builds intuition that equations alone cannot provide. Attendees will leave with a clear mental model of the complete FMCW MIMO processing chain—from raw ADC samples to tracked point clouds—grounded in real measurements they've seen generated.

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. 

Radar detection of Small Targets (low observables) and low RCS (Radar Cross section) targets (e.g small boats, periscope, drones etc) submerged in Sea clutter, has always been a challenge in modern times. But there is always a critical requirement to detect intruders (slow moving small boats) entering the territorial waters, UAVs (Unmanned Aviation Vehicles) over land and sea involved in EW assignments, detection of submarine periscopes exposed just above the water surface etc. Also, recently birds flying low at critical heights above the ground, have become potential aviation safety hazards too, during aircraft takeoff and landing periods. These targets (birds) have significantly low RCS with weak reflected/scattered power, masked by strong correlated sea clutter (at high sea states) /inherent ground clutter returns. Hence, extracting such weaker, unpredictable and unstable target returns amidst strong clutter background require efficient, reliable and robust Small Target detection methods and techniques.

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.) has 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. Therefore, RF systems can no longer depend on static resource allocations (i.e. user separation via frequency or time division). As radar and communication systems pose the greatest demand on spectrum access, their future designs must make use of all degrees-of-freedom (DoF): time, frequency, space, coding and polarization. Technologies for efficient radar-communications spectral access can be grouped into two broad categories: co-design and coexistence.

Coexistence is a key focus of emerging shared and unlicensed frequency bands (e.g. the Citizens Broadband Radio Service (CBRS) band in the United States), where radar and communications systems must share a set band while incurring limited interference to one another. Coexistence techniques provide efficient spectrum access for spatially distributed users, who may or may not coordinate their access to the shared bands.

The second spectrum access technique is co-design. Very often a single platform or system will have multiple datalinks and/or radar systems on-board. Further, advances in arbitrary waveform generation and digital-at-every-element technology have enabled waveform agility and multiple-input multiple-output radar technology, introducing the capability to shape spectrum access along the coding, spatial and polarization DoFs. Therefore, co-designed radar/communications techniques can reduce the number of RF systems on a platform or system, and a single software-defined RF device can perform both radar and communications. Dual-function waveforms allow for improved spectral efficiency by viewing spectral access from a user perspective rather than from a functional perspective.

Successful co-existence and co-design of radar and communication systems both rely on fundamental understanding of the design goals, constraints, and performance metrics of both types of systems. Further, these competing design constraints are not related to each other in a mathematically tractable fashion. Practitioners of one technology are often not fully aware of the restrictions and requirements of
the competing technology. Finally, there is often a disconnect between the theoretical design of spectrum access techniques (i.e. radar, communications, and dual-function waveforms) and the practical implementation of such techniques (e.g. amplifier non-linearities and out-of-band emissions).

Therefore, to bridge the divide between radar and communications engineers 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 example, the problem space of coexistence of radar and commercial communications will be explored in detail – from problem setup, to system requirements, to demonstrations of real-time processing. 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.

The purpose of this tutorial is to provide a serious exposition of the state-of-the-art of passive radar imaging and its development in the context of using synthetic aperture radar (SAR) and Inverse SAR in passive radars. 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 to the SAR and ISAR techniques, which will give the principles and fundamentals for the course attendance without background in radar imaging, the tutorial will move on to develop advanced topics on passive SAR/ISAR imaging using different illuminators of opportunity (IO) and using different geometries with airborne and ground vehicles’ passive radar using ground-based terrestrial Illuminators of opportunity, as well stationary radar receivers using space-based illuminators. This four-hour tutorial will provide a comprehensive technical overview of current techniques and practices associated with bistatic radar on mobile platforms and ground-based passive radars dedicated to SAR/ISAR imaging, advancing beyond the rudimentary development of long-established theories.
After an introduction to fundamental imaging principles, geometry, and applications, the tutorial will move on to develop advanced topics in passive radar imaging. Passive bistatic radar on mobile platforms will receive particular emphasis, given its civilian and military relevance. The tutorial will focus particularly on advanced topics such as ground surface imaging with airborne passive radar in SAR mode using ground-based terrestrial Illuminators of opportunity, such as DVB-T and 5G. In the next part of the tutorial, the topic of ground surface imaging with a stationary passive radar receiver in SAR mode using space-based Illuminators of opportunity, such as STARLINK and a space-based SAR constellation, will be addressed. Instead of the SAR imaging techniques, the tutorial will also cover target imaging with passive radar in ISAR mode using ground-based terrestrial (DVB-T) and space-based (DVB-S) Illuminators of opportunity. In the final part of the tutorial, the outlook on potential future developments in passive SAR/ISAR imaging and applications will be provided, concluding the tutorial and providing future applications for passive radar imaging technology.
Over the past few years, the tutors have contributed to the open literature on the emerging domain of Passive Radar Imaging using SAR and ISAR techniques, including novel simulations and measurements.  In addition to constructing the theoretical narrative that underpins the advanced pillar topics, a comprehensive set of results will illustrate key concepts and aid understanding.
Auxiliary advanced topics related to the two pillars will also receive coverage in the tutorial.
 

Digital modulation is emerging as a new paradigm for MIMO Continuous Wave radars that, as of today, mostly use the venerable Frequency Modulated Continuous Wave (FMCW) waveform. Phase-modulated continuous-wave (PMCW) and Orthogonal Frequency-Division Multiplex (OFDM) radars enjoy several system level advantages with a moderate increase in complexity that tends to reduce over time with CMOS scaling. We will show that for large scale MIMO radars, the use of Digitally Modulated Radars (DMR) helps to detect, without ambiguity, high speed moving obstacles while this is more difficult with FMCW. In addition, digital modulations are interesting candidates for Join Communication and Sensing (JCAS) applications. This tutorial covers several aspects of MIMO PMCW and OFDM radars including waveform design, signal processing and analog front-end non-idealities compensation.
In this tutorial, we will show how to design the waveform to improve the robustness to phase noise, IQ imbalance and transceiver non-linearity. We will show that the OFDM waveform can be designed with low PAPR to limit the impact of power amplifier compression. This improved robustness to front-end impairments helps to reduce the front-end analog design requirements for DMR.
While the literature contains several of solutions to improve the radar performances by calibration techniques, the proposed approach is to modify any waveform to make it robust by design to front-end non-idealities, without the need of additional signal processing algorithm on-board. Therefore, the radar dynamic range can be significantly improved without requiring the need for costly calibration processing and complex analog front-end design.
Finally, DMR are known to be very sensitive to the Tx-to-Rx leakage signal, which cannot be removed by a high-pass filter as in FMCW radars. Therefore, we will describe a mixed-signal technique developed especially to attenuate this signal. Combined with the different waveform design techniques described in this tutorial, the impact of the Tx leakage signal can become negligible.

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 the
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 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 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 the MIMO radar approach. Finally, the automotive radar applications and advanced topics, such as interference mitigation and
sensor fusion, will be discussed.

Nature presents examples of active sensing which are unique, sophisticated and incredibly fascinating. Bats, for example, use echolocation to sense the environment actively and, over a period of 50 million years, they have evolved an echolocation system with unique in-built sensing mechanisms that are often the envy of synthetic systems. These allow them to hunt for insects, forage for fruit or flower nectar and navigate in complex environments. Similarly, modern radar systems rely on active sensing to support a variety of tasks that include detection and classification of targets, accurate localization and tracking, autonomous navigation and collision avoidance. The basic principles of bat echolocation largely coincide with those of synthetic radar systems. Bats emit ultrasound echolocation calls and then listen for target echoes. However, there are also some key differences to explore which could inspire improvements for synthetic sensors. The first part of this tutorial covers the theory of waveform diversity and design to provide the attendees with the background information and tools required to fully appreciate and understand bat echolocation and the analysis of echolocation calls. This part includes the theory of narrowband and wideband waveforms, the introduction of the Ambiguity Function and theory showing how this plays a key role in the analysis of waveform performance. The second part covers echolocation by bats. An introduction on the topic is followed by examples of real echolocation calls which are analyzed to identify similarities and differences with respect to radar waveforms. This part includes the case-study of insect- and nectar-feeding bats and the presentation of recent radar bio-inspired research. Finally, this part covers some fascinating examples of the biosonar arms race between insects and bats with comparisons against Radar Electronic Warfare. 

Nonlinear radar has emerged as a powerful technique for detecting, localizing, and identifying targets equipped with nonlinear elements such as diodes, transistors, and nonlinear transmission lines. It has transitioned from a niche concept into a rapidly growing research direction with strong industrial and scientific relevance. Nonlinear systems exploiting frequency conversion in the target, enabling high clutter suppression, robust target discrimination, and new modalities for sensing and tracking in challenging environments. This tutorial provides a comprehensive introduction to nonlinear radar principles, system architectures, signal models, nonlinear target behavior, and application use-cases. We begin with core theory of nonlinear scattering mechanisms illustrated by intuitive circuit- and EM-level interpretations. Building on this foundation, we discuss practical hardware implementations including CW, FMCW, and pulsed architectures, and explore design trade-offs in antennas, waveform design, and diode-based transponders. The tutorial concludes with
demonstrated applications ranging from precision localization and RFID augmentation to ecological monitoring with miniaturized harmonic tags. Attendees will gain a unified overview of the field, bridging theory and practical design considerations relevant to modern radar engineers.

Space Domain Awareness (SDA) provides a persistent, accurate view of space to enable tactical and strategic operations. Sensors collect data to detect, track, and characterize objects from debris to active satellites. Radars are critical due to their ability to operate continuously in all weather and lighting conditions. However, observing distant space objects requires powerful, costly radar systems with large apertures. 
This tutorial highlights radar-based detection, tracking, and characterization of space objects, focusing on cost reduction through asset reuse. Two innovative solutions are explored. The first, long-baseline bistatic radar (LBBR) for tracking geostationary (GEO) satellites repurposes existing radar systems as transmitters and radio telescopes as receivers. System implementation, signal processing, and experimental data from NATO SET-293 RTG will be discussed. Second, passive radar for Low Earth Orbit (LEO) surveillance utilizes non-cooperative terrestrial transmitters as illuminators, making it cost-effective by relying on radio, TV, or active radar sources. Researchers led by Konrad Jedrzejewski at the Warsaw University of Technology and the Space Research Centre of the Polish Academy of Sciences have utilized LOFAR’s 60-meter-wide antenna arrays to carry out experiments. Their advanced signal processing techniques and experimental observations of satellites have proven passive radar’s effectiveness for space surveillance, as will be shown in this tutorial.

Integrated sensing and communications (ISAC, a.k.a. joint communications and sensing, or joint communications and radar, et al.) is expected to be one of the six distinguishing features of 6G wireless net- works, due to its substantial improvement of spectral and power efficiencies [1–4]. An ISAC transceiver emits electromagnetic (EM) wave modulated by communication data. The forward propagation of the EM wave delivers communication messages to corresponding communication receiver(s). Meanwhile, the backward propagation of the EM wave upon significant reflectors brings back the environmental information (such as range, angle, velocity and reflectivity), thus accomplishing the task of radar sensing. Since the same waveform is used for both communications and sensing, spectrum efficiency is greatly improved, which benefits various cyber physical systems (CPSs) for which simultaneous communications and sensing is the norm rather than an exception. Applications of ISAC include, but not limited to, smart furniture, health monitoring, smart factory, vehicular networks, urban air mobility (UAM), unmanned aerial vehicles (UAVs), environment monitoring, public safety, et al. The marriage of communications and radar sensing is further justified by their compatibilities of signal structures, frequency bands, hardware architectures, et al.