Limited Sample Radar HRRP Recognition Using FWA-GAN
Year: 2024
Type: article
Abstract: In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets.
Source: Remote Sensing
DOI: 10.3390/rs16162963
Authors: Yiheng Song, Liang Zhang, Yanhua Wang
Institution: Beijing Institute of Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,069
OpenAlex ID: W4401510159