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

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

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

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

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

Distributed Detection and Data Fusion Peter Willett 🌐 Zoom

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

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

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. NOV 2, 2022
5:00 AM  (ET)
9:00 AM  (GMT)

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

Abstract: TBD


 

Content

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

Abstract: TBD


 

Radar Tomographic Imaging – Achieving High Resolution With Spatial Diversity

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

Abstract: TBD


 

Simulation of Radar Sea Clutter

2 November 2022 at 5:00 am ET/ 9:00 am GMT

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

Abstract: TBD


 

Cognitive Radars

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

Abstract: TBD