TY - GEN
T1 - Fast Adaptation of Radar Detection via Online Meta-learning
AU - Khan, Zareen
AU - Jiang, Wei
AU - Haimovich, Alexander
AU - Govoni, Mark
AU - Garner, Timothy
AU - Simeone, Osvaldo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Radar detection
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85150193253&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150193253&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF56349.2022.10051849
DO - 10.1109/IEEECONF56349.2022.10051849
M3 - Conference contribution
AN - SCOPUS:85150193253
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 580
EP - 585
BT - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Y2 - 31 October 2022 through 2 November 2022
ER -