Toward Data-Driven Radar STAP

Catalyzed by the recent emergence of site-specific, high-fidelity radio frequency (RF) modeling and simulation tools purposed for radar, data-driven formulations of classical methods in radar have rapidly grown in popularity over the past decade. Despite this surge, limited focus has been directed toward the theoretical foundations of these classical methods. In this regard, as part of our ongoing data-driven approach to radar space-time adaptive processing (STAP), we analyze the asymptotic performance guarantees of select subspace separation methods in the context of radar target localization, and augment this analysis through a proposed deep learning framework for target location estimation. In our approach, we generate comprehensive datasets by randomly placing targets of variable strengths in predetermined constrained areas using RFView, a site-specific RF modeling and simulation tool developed by ISL Inc. For each radar return signal from these constrained areas, we generate heatmap tensors in range, azimuth, and elevation of the normalized adaptive matched filter (NAMF) test statistic, and of the output power of a generalized sidelobe canceller (GSC). Using our deep learning framework, we estimate target locations from these heatmap tensors to demonstrate the feasibility of and significant improvements provided by our data-driven approach in matched and mismatched settings.Comment: 39 pages, 24 figures. Submitted to IEEE Transactions on Aerospace and Electronic Systems. This article supersedes arXiv:2201.1071

Analysis of Software-Defined Networks as a Mechanism for Enforcing Corporate Security Policies in OT Networks

Cyber Security has been given a high priority for operational technology systems in recent years after specific cyber-incidents targeting them. Previously, these systems were primarily concerned with reliability; however, cyber security is now viewed as a critical aspect in avoiding production damage and financial losses. According to certain studies, replacing traditional networks in OT systems with software-defined networks (SDN) minimizes cyber-attacks due to the features provided by these networks. SDN networks have various advantages over traditional networks, due to the separation of the data plane and control plane. The concern is whether SDN networks are more dependable than existing traditional networks, and whether we can take advantage of all of SDN's characteristics when connecting with OT systems. Furthermore, deploying cyber security on a network infrastructure necessitates the creation and implementation of security policies that define the authorized communication between network devices. There is, however, a distinction to be made between security policies and the technologies that implement them. There is also often a distinction between intended policy and deployed or configured policy. Therefore there is a need to confirm compliance between policy and reality in a network. This is especially true in operational technology systems where there is a lot of network infrastructure and special purpose devices which can not be scanned or analyzed using traditional cyber security tools. To address the cyber security issues in operational technology systems, this dissertation reviews cyber-incidents reported on them and summarizes possible attacks on each of their sub-systems to gain broader insight into vulnerabilities present in them and uses the common vulnerability exposure database to enumerate trends. Then, a process is formally developed and evaluated through a proof of concept tool to detect the security policy implemented in the control rules of an SDN switch deployed in an industrial control system network. These rules were analyzed to determine if this security policy is compliant with the organization's high-level policies.doctoral, Ph.D., Computer Science -- University of Idaho - College of Graduate Studies, 2022-0

Adaptive polarization design for target detection and tracking

Transmitting waveforms with different polarizations in radar systems provide morecomplete information about the target and its environment, ensuring a significantenhancement of the radar’s performance. Conventional polarimetric radars transmitwaveforms with a fixed polarization pattern, independent of the target and cluttercharacteristics. In this chapter, we explore the adaptive design of radar polarizationwaveforms. We focus on a closed-loop system that sequentially estimates thetarget and clutter scattering parameters and then uses these estimates to select thepolarization of the subsequent waveforms.We demonstrate that the radar system performanceis significantly improved when the polarization of the transmitted signalis optimally and adaptively selected to match the polarimetric aspects of the targetand the environment. In particular, we include an overview of our recent results inpolarimetric design for radar detection and tracking.Fil: Hurtado, Martin. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Gogineni, Sandeep. Washington University in St. Louis; Estados UnidosFil: Nehorai, Arye. Washington University in St. Louis; Estados Unido