Contrastive Learning for Radar HRRP Recognition With Missing Aspects | IEEE Journals & Magazine | IEEE Xplore

Contrastive Learning for Radar HRRP Recognition With Missing Aspects


Abstract:

High resolution range profile (HRRP) has attracted increasing attention in radar automatic target recognition (RATR). However, the target-aspect missing problem in noncoo...Show More

Abstract:

High resolution range profile (HRRP) has attracted increasing attention in radar automatic target recognition (RATR). However, the target-aspect missing problem in noncooperative targets recognition, which is one of the most challenging tasks in RATR, has received very few contributions recently. The proposed work is motivated by very simple observation, i.e., as compared with HRRP signals of interesting targets, sufficient unlabeled HRRP signals are much easier to acquire. However, these signals are often neglected since there is no label information. This work focuses on the target-aspect missing problem by using a dual self-supervised contrastive learning framework. The proposed model takes advantage of massive unlabeled HRRP signals to enhance the generalization ability. Specifically, we employ self-supervised contrastive learning and online clustering module for extracting target-aspect invariant representations. Additionally, a data augmentation strategy is used as a simple yet effective module. The experimental results demonstrate that the proposed method with fewer labels enhances the recognition performance as compared to supervised and self-supervised methods in missing aspect problem.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 3504605
Date of Publication: 25 May 2023

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