a neural network with convolutional module and residual structure for radar target recognition based on high-resolution range profile
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2020
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Abstract
In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures.
| Reference Key |
fu2020sensorsa
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|---|---|
| Authors | ;Zhequan Fu;Shangsheng Li;Xiangping Li;Bo Dan;Xukun Wang |
| Journal | ekonomiczne problemy usług |
| Year | 2020 |
| DOI |
10.3390/s20030586
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| URL | |
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