Fast Data-Driven Adaptation of Radar Detection via Meta-Learning

Wei Jiang, Alexander M. Haimovich, Mark Govoni, Timothy Garner, Osvaldo Simeone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


This paper addresses the problem of fast learning of radar detectors with a limited amount of training data. In current data-driven approaches for radar detection, re-training is generally required when the operating environment changes, incurring large overhead in terms of data collection and training time. In contrast, this paper proposes two novel deep learning-based approaches that enable fast adaptation of detectors based on few data samples from a new environment. The proposed methods integrate prior knowledge regarding previously encountered radar operating environments in two different ways. One approach is based on transfer learning: it first pre-trains a detector such that it works well on data collected in previously observed environments, and then it adapts the pre-trained detector to the specific current environment. The other approach targets explicitly few-shot training via meta-learning: based on data from previous environments, it finds a common initialization that enables fast adaptation to a new environment. Numerical results validate the benefits of the proposed two approaches compared with the conventional method based on training with no prior knowledge. Furthermore, the meta-learning-based detector outperforms the transfer learning-based detector when the clutter is Gaussian.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665458283
StatePublished - 2021
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


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