• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Information Communication and Signal Processing >Compound Model of Navigation Interference Recognition Based on Deep Sparse Denoising Auto-encoder
【24h】

Compound Model of Navigation Interference Recognition Based on Deep Sparse Denoising Auto-encoder

机译：基于深度稀疏去噪自动编码器的导航干扰识别复合模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

For the navigation problem that has been affected by interference signals for a long time, a compound classification model algorithm based on a deep sparse denoising auto-encoder network is proposed. Firstly, frequency conversion and preprocessing are performed on several typical interference signals listed in this article, and then a deep sparse denoising auto-encoder is used for training sample data. After fine adjustment,final encode layer output the training data features. In the case of removing redundant information, maximize the retention of the original sample information. Finally, by comparing the recognition accuracy of three different classification models, it is concluded that the composite model proposed in this article has the advantages of fast convergence and high recognition rate, and it can get more than 2dB performance gains compared to the other two algorithm. It further demonstrates the advantages of deep learning in the field of navigation interference recognition.
机译：针对长期受到干扰信号影响的导航问题，提出了一种基于深度稀疏去噪自动编码器网络的复合分类模型算法。首先，对本文列出的几种典型干扰信号进行频率转换和预处理，然后使用深度稀疏去噪自动编码器来训练样本数据。经过微调，最终编码层输出训练数据特征。在删除冗余信息的情况下，最大程度地保留原始样本信息。最后，通过比较三种不同分类模型的识别精度，得出本文提出的复合模型具有收敛速度快，识别率高的优点，与其他两种算法相比，可以获得超过2dB的性能提升。 。它还进一步证明了深度学习在导航干扰识别领域的优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号