<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>During the training of target recognition models based on Radar Cross Section (RCS) data, a persistent challenge arises in sampling due to the inherent difficulty in acquiring a sufficient number of samples. This scarcity of data poses a significant impediment to the effective training of models, resulting in diminished accuracy in target recognition. To address this issue, this article proposes a target classification method based on RCS data under small sample conditions. The approach adopts the fundamental concept of Model-Agnostic Meta-Learning (MAML) to train the target recognition model, enhancing the structure of MAML model. An hourglass-shaped convolution layer is introduced to the input layer, with an additional convolution layer preceding the output layer, and a switch to a central loss function. To substantiate the efficacy of the improved MAML model, comprehensive comparative analyses are conducted with benchmark models, including MAML, ResNet 18-layers, Long Short-Term Memory (LSTM), among others. Experimental results conclusively demonstrate the superior performance of the refined MAML model in target recognition under conditions of limited samples, attaining an average prediction accuracy of 85.62%. This signifies a noteworthy 5-percentage-point improvement compared to the baseline model prior to the introduced enhancements.</p>
</abstract>

During the training of target recognition models based on Radar Cross Section (RCS) data, a persistent challenge arises in sampling due to the inherent difficulty in acquiring a sufficient number of samples. This scarcity of data poses a significant impediment to the effective training of models, resulting in diminished accuracy in target recognition. To address this issue, this article proposes a target classification method based on RCS data under small sample conditions. The approach adopts the fundamental concept of Model-Agnostic Meta-Learning (MAML) to train the target recognition model, enhancing the structure of MAML model. An hourglass-shaped convolution layer is introduced to the input layer, with an additional convolution layer preceding the output layer, and a switch to a central loss function. To substantiate the efficacy of the improved MAML model, comprehensive comparative analyses are conducted with benchmark models, including MAML, ResNet 18-layers, Long Short-Term Memory (LSTM), among others. Experimental results conclusively demonstrate the superior performance of the refined MAML model in target recognition under conditions of limited samples, attaining an average prediction accuracy of 85.62%. This signifies a noteworthy 5-percentage-point improvement compared to the baseline model prior to the introduced enhancements.

A Target Recognition Method of Small Sample Based on RCS Data

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Target Recognition Method of Small Sample Based on RCS Data"}

During the training of target recognition models based on Radar Cross Section (RCS) data, a persistent challenge arises in sampling due to the inherent...

Accuracy\Model	Category 1 Accuracy	Category 2 Accuracy	Category 3 Accuracy	Category 4 Accuracy	Average accuracy
MAML	82.16%	72.45%	81.3%	85.97%	80.47%
MAML-New	86.42%	79.70%	87.17%	89.19%	85.62%
ResNet 18-layers	81.7%	62.1%	82.4%	90.1%	73.45%
LSTM	81.1%	68.0%	80.8%	80.3%	77.55%

Parameter	Value	Meaning
epoch	600	Training epochs
k	4	Number of sample categories
k_spt	20	Number of support set samples
k_qry	30	Number of query set samples
imgsz	180	Dimension of input data
imgc	1	Number of channels for input data
task_num (batch_size)	16	Training batch of samples
meta_lr	1e-3	First gradient update learning rate
update_lr	0.01	Second gradient update learning rate

A Target Recognition Method of Small Sample Based on RCS Data

Online veröffentlicht: 28. März 2024

Seitenbereich: 1 - 9

DOI: https://doi.org/10.2478/ijanmc-2024-0001

Schlüsselwörter
RCS Data, Small Sample, Target Recognition, MAML Model

© 2024 Ruocheng Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Comparative experimental results of different models

Partial network parameter values for MAML and MAML-New

A Target Recognition Method of Small Sample Based on RCS Data

Online veröffentlicht: 28. März 2024

Seitenbereich: 1 - 9

DOI: https://doi.org/10.2478/ijanmc-2024-0001

SchlüsselwörterRCS Data, Small Sample, Target Recognition, MAML Model

© 2024 Ruocheng Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Comparative experimental results of different models

Partial network parameter values for MAML and MAML-New

Schlüsselwörter
RCS Data, Small Sample, Target Recognition, MAML Model