AESS offers online talks to reach our members around the world. Register today for one of these upcoming webinars from experts in academia, government, and industry.
Systems Engineering is a relatively new discipline of engineering, having only reached recognizable status within the last 25-30 years, and yet the discipline has become of extreme importance, especially in the world of today’s complex systems. It turns out that some industries have been performing “systems engineering” for years even though they did not call it that; product engineering, for example, in the automotive industry and commercial industries, embodies some of the important principles of systems engineering even though it is not always specified as such. Some of the significant principles of systems engineering are that it is multi-disciplined, considers the entire lifecycle of the product or system, and has as its major goal to strike a design balance between cost, schedule, and performance, and all within the reasonable risk.
This tutorial will delve into all the critical principles of systems engineering, and provide emphasis on the nuances of systems engineering that many of the new practitioners tend to overlook, such as safety, security, producibility, reliability, and similar.
Bob Rassa, Raytheon (Ret.)
🗓 9 February 2023 - 11:00 AM ET/4:00PM UTC
This presentation will highlight the DEVCOM Army Research Laboratory's research on radar and communications spectrum sharing over the past decade. We first discuss the state of radio frequency (RF) coexistence and how it benefits from dynamic spectrum sensing and categorize the coexistence of radar and communications in terms of their cooperation and non-cooperation in using the spectrum as well as their roles as primary, secondary, and dual users of the frequency bands. We next discuss several optimizations, machine learning, and adaptive signal processing methodologies that have been explored to achieve real-time dynamic spectrum access for coexistence. Finally, we introduce the topic of metacognition to regulate cognition and determine how to select a particular cognitive radar techniques appropriately for a given dynamic environment.
Anthony F. Martone, U.S. Army Research Laboratory
🗓 22 February 2023 - 11:00 AM ET/4:00PM UTC
In order to compose an operational picture that is as complete as possible, modern surveillance systems have to integrate coherent information from a wide variety of sources. While in the past surveillance had suffered from a lack of data, current technology transformed the problem into one of an overabundance of information, leading to a vital need for automated analysis. Indeed, current surveillance sensors generate volumes of data that only a few years ago would have been inconceivable. All the processing, algorithm calibration, parameter tuning, etc., need to be as much automatic as possible. More importantly, we are in need of novel paradigms for algorithmic design. In this respect, Artificial Intelligence (AI) and Data Fusion (DF) offer an unprecedented opportunity to strengthen the technological edge; however, the risk is to elevate, at the same time, the speed of the threats we face. Indeed, the surveillance task is complicated by the diversification of threats, whose nature and origin are most often unknown. AI and DF techniques have the potential to identify patterns emerging within these very large datasets, fused from a variety of sources and generated from monitoring wide areas on a day-to-day basis, and use the learned knowledge to anticipate the possible evolution(s) of the operational picture.
This webinar will focus on both real-world scenarios and theoretical models, spanning from the underwater to the space domain, including the analysis of scenarios with heterogeneous surveillance sensors (radar, sonar, and satellite). The opportunity will be taken to show also an application of the aforementioned techniques to a completely different scenario which is the epidemiological COVID-19 curve monitoring and forecasting. Finally, a novel theoretical analysis will be described, which provides for an approximation −asymptotically exact− of the error probability of Machine Learning (ML) binary classifiers. This last theory is specifically relevant for radar/sonar detection.
Paolo Braca, Centre for Maritime Research and Experimentation
🗓 29 March 2023 - 11:00 AM ET/4:00PM UTC