ISARLAB: a radar signal processing tool

Haywood, B.; Kyprianou, Ross; Zyweck, A.

doi:10.1109/icassp.1994.389503

Cited by 6 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Along with the simulated modulation recognition dataset, this paper also utilizes a synthetically generated custom High-Resolution Radar (HRR) dataset, created in MATLAB using the Inverse Synthetic Aperture Radar Simulation and Processing (ISARLAB) toolbox. 5 Generated signals consisted of a 1600 IQ sample HRR reflection return off of 24 different vehicles, including various civilian and military vehicles in several vehicle orientations with respect to the synthetic aperture's pointing direction. No RF channels, front-end hardware, or other effects were modeled within this simulation.…”

Section: Datamentioning

confidence: 99%

Demonstrating confidence in RF machine learning systems

Judah,

Kuzdeba

2024

Assurance and Security for AI-enabled Systems

View full text Add to dashboard Cite

Significant advancements have been achieved in radio frequency (RF) machine learning (ML), yet much of the existing research remains confined to proof-of-concept demonstrations or narrowly defined scenarios that do not fully encompass real-world operational conditions. As machine learning algorithms increasingly assume decision-making roles in this domain, ensuring the reliability and validity of their outcomes becomes imperative. This paper seeks to expand the discourse on ML assurance within the RF ML domain. Building upon the foundation laid by the RiftNet deep learning classifier, this study investigates the robustness of this solution in the context of instilling confidence in RFML applications. Specifically, the paper delves into the challenges posed by inaccurately labeled data during artificial intelligence agent development, the quantification of outcome uncertainties, and the implementation of calibration techniques for RF modulation recognition. In essence, this research marks a pivotal shift from the initial successes of RF ML towards the development of techniques that inspire confidence in real-world, edge-system applications.

show abstract

Section: Datamentioning

confidence: 99%

Demonstrating confidence in RF machine learning systems

Judah,

Kuzdeba

2024

Assurance and Security for AI-enabled Systems

View full text Add to dashboard Cite

show abstract

“…Also foundations are laid towards the study of interference effects (glint). ( 59) [Haywood et al, 1994] have introduced the ISARLAB software package which is a comprehensive set of functions that emulate the particular functions of an ISAR system. And ( 38 In all above indicative work there is no mention of the computing force that provides the motion of the radar and target platforms.…”

Section: Isar Simulatorsmentioning

confidence: 99%

Inverse synthetic aperture radar simulators as software-defined countermeasure systems

Κωστής¹

View full text Add to dashboard Cite

I would like to dedicate this thesis to my loving parents Georgios and Maria Kostis.University of the Aegean i SdCS Phd Thesis 2009 I would like to thank my supervisor Prof. Sokratis Katsikas of the University of Piraeus for the constant help and encouragement without which this project would not have been possible.Sincere acknowledgments to Prof. Chris Baker of the Australian National University and Prof. Hugh Griffiths of University College London for subject matter expert help in the beginning of this research effort.Sincere thanks to Maria Diakaki for helping with the multiple target input data descriptions and to Konstantinos Galanis and Athanasios Goudosis for their constant help, without which this project would not have been possible.I would also like to thank all reviewers either eponymous or anonymous of this work for their valuable and constructive comments.

show abstract