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

TL;DR

This paper introduces two deep learning methods, transfer learning and meta-learning, for rapid adaptation of radar detectors to new environments with minimal data, reducing retraining overhead.

Contribution

It presents novel deep learning approaches that enable fast radar detector adaptation using prior knowledge and few data samples, outperforming conventional methods.

Findings

Meta-learning-based detector outperforms transfer learning in Gaussian clutter environments.

Proposed methods significantly reduce data and training time for environment adaptation.

Numerical results validate the effectiveness of the approaches over traditional training methods.

Abstract

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.