Assessment of Techniques for Detection of Transient Radio-Frequency Interference (RFI) Signals: A Case Study of a Transient in Radar Test Data

• Published in: Eng (Basel, Switzerland) Switzerland), 2023-09, Vol.4 (3), p.2191-2203
• Main Authors: Durden, Stephen L., Vilnrotter, Victor A., Shaffer, Scott J.
• Format: Article
• Language: English
• Subjects: Case studies; Computer vision; Datasets; Decision theory; Deep learning; interference; Machine learning; Neural networks; Parameter estimation; radar; Radar receivers; Radio astronomy; Radio frequency interference; Radio signals; Receivers & amplifiers; Signal processing; Spectrograms; Statistical methods; transient signals
• ISSN: 2673-4117; 2673-4117
• DOI: 10.3390/eng4030126

The authors present a case study of the investigation of a transient signal that appeared in the testing of a radar receiver. The characteristics of the test conditions and data are first discussed. The authors then proceed to outline the methods for detecting and analyzing transients in the data. For this, they consider several methods based on modern signal processing and evaluate their utility. The initial method used for identifying transients is based on computer vision techniques, specifically, thresholding spectrograms into binary images, morphological processing, and object boundary extraction. The authors also consider deep learning methods and methods related to optimal statistical detection. For the latter approach, since the transient in this case was chirp-like, the method of maximum likelihood is used to estimate its parameters. Each approach is evaluated, followed by a discussion of how the results could be extended to analysis and detection of other types of transient radio-frequency interference (RFI). The authors find that computer vision, deep learning, and statistical detection methods are all useful. However, each is best used at different stages of the investigation when a transient appears in data. Computer vision is particularly useful when little is known about the transient, while traditional statistically optimal detection can be quite accurate once the structure of the transient is known and its parameters estimated.