Virtual Distinguished Lecturer Program

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The IEEE AESS Virtual Distinguished Lecturer Webinar Series allows us to continue to provide AESS members with our respected and reputable Distinguished Lecturer program. Registration is completely free.

Virtual Distinguished Lecturer Program

Due to pandemic and travel restrictions, the traditional format of the Distinguished Lecturer Program (DLP) is not possible. The Virtual Distinguished Lecturer Program (VDLP) allows us to continue to serve the AESS participants and the aerospace and electronic systems community the opportunity to hear from our respected Distinguished Lecturers.

Registration is free for all webinars. If you are unable to attend the "live" virtual events, the presentations will be available after the event.

 

2022 Virtual DL Webinar Schedule

2022 IEEE Aerospace & Electronic Systems Society Virtual Distinguished Lecturer Webinar Series

Date/Time (ET/GMT) Title Presenter Registration Recordings

WED. 15 JUN 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Electronic Attack and Antenna Based Countermeasures

Antonio De Maio

🌐 Zoom

📺 Webinar

WED. 13 JUL 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Bayesian Quantum Mechanics Fred Daum 🌐 Zoom

📺 Webinar

WED. 27 JUL 2022
12:00 PM  (ET)
4:00 PM  (GMT)

An Introduction to Non-linear State Estimation and Target Tracking Based on Tensor Decompositions Felix Govaers 🌐 Zoom

📺 Webinar

TUE. 9 AUG 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Information Transfer Across Adjacent Cameras in a Network Yaakov Bar-Shalom 🌐 Zoom

📺 Webinar

TUE. 23 AUG 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Distributed Detection and Data Fusion Peter Willett 🌐 Zoom

📺 Webinar

WED. 7 SEPT 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Tracking Maneuvering Targets in a World of Netted Sensors Dale Blair 🌐 Zoom

📺 Webinar

WED. 5 OCT 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Dual-Function Radar Communication System With Communication and Radar Performance Tradeoff Athina Petropulu 🌐 Zoom

WED. 19 OCT 2022
9:00 AM  (ET)
1:00 PM  (GMT)

Radar Tomographic Imaging – Achieving High Resolution With Spatial Diversity X. Hongbo Sun 🌐 Zoom
WED. 9 Nov 2022
12:00 PM  (ET)
4:00 PM  (GMT)
Radar Technology and Sustainability: How to Conjugate Innovation and Social Duties Alfonso Farina 🌐 Zoom
WED. 14 Nov 2022
4:00 AM  (ET)
7:30 PM  (GMT+10.5)
Simulation of Radar Sea Clutter Luke Rosenberg 🌐 Zoom

WED. 16 NOV 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Reinforcement Learning: Snake Oil or Good Idea? Fred Daum 🌐 Zoom

WED. 30 NOV 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Gravity-Modeling Considerations in High-Integrity Inertial Systems Michael Braasch 🌐 Zoom

WED. 14 DEC 2022
12:00 PM  (ET)
4:00 PM  (GMT)

Cognitive Radars Sabrina Greco 🌐 Zoom

Electronic Attack and Antenna Based Countermeasures

15 June 2022 at 12:00 pm ET/ 4:00 pm GMT

This webinar is focused on Electronic Attack (EA) realized via a jamming system to reduce the effectiveness of a radar. In this respect, jamming geometrical configurations are introduced and discussed together with non-coherent and coherent jamming categories. Hence the concepts of masking and deception are explained with emphasis on some common jamming synthesis techniques. The last part of the webinar presents antena-based Electronic Protection (EP) strategies whose goal is to reduce the effectiveness of an opponent’s EA capability. Examples are provided including sidelobe blanking, sidelobe cancellation, mainlobe cancellation, narrow beamwidth, monopulse angle measurement, and low cross-polarization antenna.


 

Bayesian Quantum Mechanics

13 July 2022 at 12:00 pm ET/ 4:00 pm GMT

Bayesian quantum mechanics is important in practical applications, such as designing quantum communication, quantum navigation, quantum metrology, quantum computers, and maybe even quantum radars. In contrast, textbook quantum mechanics does not model macroscopic measurement errors, and it does not model any kind of real physical measurement, despite much talk about “measurements” in physics textbooks. We explain the correct models and algorithms for measurements and filtering of quantum mechanical systems. We recall the amazing story of the Schrödinger equation and the meaning of its solution. We give a Bayesian generalization of the boring old Schrödinger equation that works for practical applications. We explore the scope of future research in Bayesian quantum mechanics. This talk is for normal engineers who do not have quantum mechanics for breakfast.


 

Term Date
-
Type
Distinguished Lecturer

An Introduction to Non-linear State Estimation and Target Tracking Based on Tensor Decompositions

27 July 2022 at 12:00 pm ET/ 4:00 pm GMT

The increasing trend towards connected sensors (“internet of things” and “ubiquitous computing”) derives a demand for powerful non-linear estimation methodologies. Conventionally, algorithmic solutions in the field of Bayesian data fusion and target tracking are based on either a Gaussian (mixture) or a particle representation of the prior and posterior density functions (pdf). The discrete filters reduce the state space to a fixed grid and represents the pdf in terms of an array of function values in high to extraordinary high dimensions. Due to the “curse of dimensionality”, data compression techniques such as tensor decompositions have to be applied. Though those methods are computationally burdensome, their advantage is the precise information processing and the ability to model all kinds of stochastic behaviour. In this tutorial, the basic methods for a Bayes formalism in discrete state spaces is explained. Possible solutions to the tensor decomposition (and composition) process are presented. Algorithms will be provided for each solution. The list of topics includes: Short introduction to target tracking and non-linear state estimation, discrete pdfs, Bayes recursion on those, PARAFAC/CANDECOMP Decomposition (CPD), Tucker and Hierarchical Tucker decomposition.


 

Information Transfer Across Adjacent Cameras in a Network

9 August 2022 at 12:00 pm ET/ 4:00 pm GMT

This presentation develops three-dimensional (3D) Cartesian tracking algorithms for a high-resolution wide field of view (FOV) camera surveillance system. This system consists of a network linking multiple narrow FOV cameras side-by-side looking at adjacent areas. In such a multi-camera system, a target usually appears in the FOV of one camera first, and then shifts to an adjacent one. The tracking algorithms estimate target 3D positions and velocities dynamically using the angular information (azimuth and elevation) provided by multiple cameras. The target state (consisting of Cartesian position and velocity) is not fully observable when it is detected by the first camera only. Once it moves into the FOV of the next camera, the state can then be fully estimated. The main challenge is how to transfer the state information from the first camera to the next one when the target moves across cameras. In this presentation, we develop an approach, designated as Cartesian state estimation with full maximum likelihood information transfer (fMLIT), to cope with this challenge. Since the fMLIT consists of an implicit state relationship, the conventional Kalman-like filters (which assumes explicit constraints, like the state propagation equation) are not suitable. We then develop three Gauss–Helmert filters, which can handle implicit constraints, and test them with simulation data.


 

Distributed Detection and Data Fusion

23 August 2022 at 12:00 pm ET/ 4:00 pm GMT

The initial paper on the subject of distributed detection, by Tenney and Sandell, showed that under a fixed fusion rule, for two sensors with one bit outputs, the optimal Bayes sensor decision rule is a likelihood ratio test. It has been shown that the optimal fusion rule for N sensors is a likelihood ratio test on the data received from the sensors. Reibman and Nolte and Hoballah and Varshney have generalized the results to N sensors with optimal fusion, again with the restriction of one bit sensor outputs; this has been relaxed later to multi-bit quantizations. In this “primer” talk we explore a number of issues in distributed detection, including some pathologies, the benefits of fusion, optimal design, structures for decision flow, consensus, sensor biases, feedback, deliberate obfuscation (i.e., security) and censoring. We also devote some time to distributed estimation (i.e., fusion for tracking): why is it difficult and what seems to work best?


 

Tracking Maneuvering Targets in a World of Netted Sensors

7 September 2022 at 12:00 pm ET/ 4:00 pm GMT

With the advancement of sensor and communications systems technologies and the desire for better surveillance, the interest in sensor netting has grown significantly over the past few years. This lecture starts by motivating the need for multisensor/multitarget tracking and then develops the fundamental concepts of single target tracking, tracking in the presence of maneuvers, multiple-model tracking, sensor resource management, multitarget tracking, and multiple sensor tracking. Future directions of sensor netting for target tracking and the associated technical challenges are discussed. An illustrative approach with minimal use of equations is taken in this lecture in order to reach a broad audience.


 

Dual-Function Radar Communication System With Communication and Radar Performance Tradeoff

5 October 2022 at 12:00 pm ET/ 4:00 pm GMT

With today’s technology, radio frequency front-end architectures are very similar in radar and wireless communication systems. Further, in an effort to access more bandwidth, wireless systems have been shifting to frequency bands that have been traditionally occupied by radar systems. Given the hardware and frequency convergence, there is a lot of recent interest in the integration of the radar and communication functions in one system. Such integration will enable more efficient use of spectrum, reduce device size/cost and power consumption, and will also offer the potential for significant performance enhancement of both sensing and communication functions. Dual Function Radar-Communication (DFRC) systems is a class of integrated sensing-communication (ISC) systems that use the same waveform as well as the same hardware platform for both sensing and communication purposes. Thus, DFRC systems can achieve higher spectral efficiency than most ISC systems, require simpler transmitter hardware and a smaller, less expensive device. DFRC systems are prime candidates for autonomous driving vehicles, unmanned aerial vehicles, surveillance, search and rescue, and networked robots in advanced manufacturing applications that rely on censing and communications.

In the talk, we will present a novel DFRC system that uses the available bandwidth efficiently for both communication as well as sensing. The system transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms and allows the transmit antennas to use subcarriers in a shared fashion. When all subcarriers are used in a shared fashion, the proposed system achieves high communication rate, while its sensing performance is limited by the size of the receive array. By reserving some subcarriers for exclusive use by transmit antennas (private subcarriers), the communication rate can be traded off for improved sensing performance. The improvement is achieved by using the private subcarriers to construct a large virtual array that yields higher resolution angle estimates. The system is endowed with beamforming capability, via waveform precoding, where the precoding matrix is optimally designed to meet a joint sensing-communication system performance metric. We also present novel hybrid analog-digital architectures for achieving good performance with reduced hardware and energy cost via the use of double-phase shifters.


 

Radar Tomographic Imaging – Achieving High Resolution With Spatial Diversity

19 October 2022 at 9:00 am ET/ / 1:00 pm GMT

It is well known that the range resolution of conventional radar is typically limited by the bandwidth of adopted radar waveform, and the cross-range resolution is limited by the radar beamwidth. Synthetic aperture radar (SAR) exploits the radar motion to form a long virtual antenna aperture, which can significantly improve the cross-range resolution and make it comparable or equal to the range resolution. However, we should not forget that the potential spatial resolution that radar can achieve could be much better, more precisely, in the order of sub-wavelength. Such radar operating mode is called radar tomography or radar tomographic imaging, which exploits large spatial diversity, instead of large waveform bandwidth, to achieve high spatial resolution. In this talk, the principle of radar tomographic imaging is introduced and the measurement results in microwave anechoic chamber are presented to demonstrate its superior high spatial resolution. Some example of radar tomography technique in real-world applications are also addressed.


 

Radar Technology and Sustainability: How to Conjugate Innovation and Social Duties

9 November 2022 at 12:00 pm ET/ 4:00 pm GMT

This lecture considers the role of Radars in dealing with their impact on the digital economy, sustainable economy, green economy, space economy, and commercial market.

Furthermore, the radar will continue contributing to the safety and fluidity of ATC and Vessel traffic and Defence.

The DL wraps up with considerations of Ethics and Culture as pillars and core values.


 

Simulation of Radar Sea Clutter

14 November 2022 at 4:00 am ET/ 7:30 am GMT+10.5

Realistic simulation of radar sea clutter is extremely important to stimulate radar processors during development and testing, generate realistic displays in radar trainers, and evaluate radar detection algorithms. A simulated signal must reproduce as faithfully as possible the statistical characteristics that are present in real data, including the mean backscatter, amplitude statistics, short-term temporal correlation (including that represented by the Doppler spectra), and any spatial or longer-term temporal variations. It must also reflect the chosen radar parameters, collection geometry, and model the effect of platform motion if the data is being collected by an airborne radar system. This talk will describe a number of approaches for generating hi-fidelity radar sea clutter using statistical models and demonstrate how they compare against data collected from both ground and airborne platforms.


 

Reinforcement Learning: Snake Oil or Good Idea?

16 November 2022 at 12:00 pm ET/ 4:00 pm GMT

Reinforcement learning (RL) has recently shown dramatic improvements in performance without any human teaching. However, standard RL assumes exact complete knowledge of the state of the environment with zero measurement errors, and it assumes stationary models of the environment. In contrast, Bayesian RL (BRL) makes no such assumptions. Moreover, BRL has many other advantages: it provides uncertainty quantification; it gives optimal accuracy using a minimal number of training samples (in theory); it is optimally robust (in theory), and it automatically solves the exploitation vs. explo ration tradeoff in RL. But these benefits of BRL generally require much higher computational resources than standard RL. We show how to mitigate such computational issues by exploiting modern parallel processors (e.g., GPUs and TPUs) as well as the structure and smoothness of the problem. We also explain the connections between RL and stochastic optimal control , Kalman filters and nonlinear filters. We quantify the performance of RL vs. classical AI methods, using an apples and apples comparison n, in contrast to the blatantly unfair tests that have been widely reported. We also discuss AlphaCode, which uses RL with tempering as the workhorse algorithm running on TPUs. This talk is for normal engineers who do not have RL for breakfast.


 

Term Date
-
Type
Distinguished Lecturer

Gravity-Modeling Considerations in High-Integrity Inertial Systems

30 November 2022 at 12:00 pm ET/ 4:00 pm GMT

Despite the name, accelerometers measure specific force and not acceleration. Since specific force is the vector sum of Newtonian acceleration and the reaction to gravity, the isolation of acceleration from accelerometer measurements requires the determination of the local value of gravity (via software models or databases). Although this process is well understood in the inertial and physical geodesy communities, guaranteeing the integrity of this process for safety-of-life applications requires high-integrity gravity models. This lecture will ‘plumb’ the depths of the topic of gravity compensation in inertial systems. We will explore some of the fascinating, but not widely known, characteristics of the Earth’s gravity field. We will investigate how gravity compensation error can impact inertial navigation performance and we will go through techniques that may be used to model gravity for high integrity, safety-of-life applications such as civil aviation.


 

Cognitive Radars

14 December 2022 at 12:00 pm ET/ 4:00 pm GMT

Over the past fifteen years, “cognition” has emerged as an enabling technology for incorporating learning and adaptivity on both transmit and receive to optimize or make more robust the radar performance in dynamic environments. The term ‘cognitive radar’ was coined in 2006, but the foundations of the cognitive systems date back several decades to research on knowledge-aided signal processing, and adaptive radar design. The main element of cognitive radar systems is the ‘perception-action cycle’, that is the feedback mechanism between receiver and transmitter that allows the radar system to learn information about a target and its environment and adapt its transmissions so as to optimize one or more missions, according to a desired goal. But a truly cognitive radar should not be only able to adapt on the fly its transmission waveforms and parameters based on internal fixed rules and on what learned about the environment, but it should also be able to optimize these rules learning with time from its mistakes, as some biological system does. And this is still a big challenge for radar experts.

This talk will provide an overview of the main concept, of methods for modeling cognitive processes in a radar system and of some application example. Some insights into future directions of research will be provided as concluding remarks.