Fast Adaptation of Radar Detection via Online Meta-learning

Zareen Khan, Wei Jiang, Alexander Haimovich, Mark Govoni, Timothy Garner, Osvaldo Simeone

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

Abstract

This paper addresses the problem of continual learning of radar detectors from sequentially provided training data. Given a limited number of labeled samples for each of a number of tasks in a certain class, a model-agnostic meta-learning (MAML) approach is developed to enable few-shot learning of a new task in the class. In the radar detection problem, a task is associated with a set of specific environmental conditions such as target, clutter or interference properties. The proposed detector design is separated into two stages: a meta-training stage and an adaptation stage. The outcome of the meta-training stage is used to initialize the learning of the detector parameter vector, which in turn relies on the training data available for adaptation. Unlike a previously proposed offline approach, where all data available for meta-training was assumed available upfront, the proposed learning procedure is applied online, where meta-training data is made available incrementally, with each task. Numerical results validate that the proposed detector may learn a new task from a limited number of labeled samples, and that the performance improves as the meta-training relies on an increasing number of tasks.

Original languageEnglish (US)
Title of host publication56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages580-585
Number of pages6
ISBN (Electronic)9781665459068
DOIs
StatePublished - 2022
Event56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States
Duration: Oct 31 2022Nov 2 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October
ISSN (Print)1058-6393

Conference

Conference56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/31/2211/2/22

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

Keywords

  • Radar detection
  • machine learning

Fingerprint

Dive into the research topics of 'Fast Adaptation of Radar Detection via Online Meta-learning'. Together they form a unique fingerprint.

Cite this