The satellite-based augmentation system(SBAS)provides differential and integrity augmentation services for life safety fields of aviation and navigation.However,the signal structure of SBAS is public,which incurs a ri...The satellite-based augmentation system(SBAS)provides differential and integrity augmentation services for life safety fields of aviation and navigation.However,the signal structure of SBAS is public,which incurs a risk of spoofing attacks.To improve the anti-spoofing capability of the SBAS,European Union and the United States conduct research on navigation message authentication,and promote the standardization of SBAS message authentication.For the development of Beidou satellite-based augmentation system(BDSBAS),this paper proposes navigation message authentication based on the Chinese commercial cryptographic standards.Firstly,this paper expounds the architecture and principles of the SBAS message authentication,and then carries out the design of timed efficient streaming losstolerant authentication scheme(TESLA)and elliptic curve digital signature algorithm(ECDSA)authentication schemes based on Chinese commercial cryptographic standards,message arrangement and the design of over-the-air rekeying(OTAR)message.Finally,this paper conducts a theoretical analysis of the time between authentications(TBA)and maximum authentication latency(MAL)for L5 TESLA-I and L5 ECDSA-Q,and further simulates the reception time of OTAR message,TBA and MAL from the aspects of OTAR message weight and demodulation error rate.The simulation results can provide theoretical supports for the standardization of BDSBAS message authentication.展开更多
高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布;日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象,针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS...高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布;日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象,针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS和铺粉厚度LT与RD样本数据存在缺失参数和分布不均问题,采用最大期望化(EM)算法对缺失参数进行补齐;采用带有梯度惩罚的WGAN算法(WGAN-GP)对数据稀疏的低RD区间生成虚拟样本数据。然后,分别基于补齐文献数据和补充虚拟数据,采用常青藤算法优化的随机森林(IVYA-RF)构建了RD预测模型,并对模型预测精度进行了定量评估和实验验证。结果表明:基于补充虚拟数据集构建的IVYA-RF模型II比基于补齐文献数据集构建的IVYA-RF模型I具有更好的预测精度,其原因主要来源于在低RD区间生成虚拟数据后,使建模样本数据的分布均匀性得到改善,这也是数据增强与机器学习相结合的意义所在。对新实验数据的验证取得了满意的预测精度,其中,IVYA-RF模型I验证结果的统计学参数R2(决定系数)、RMSE(均方根误差)、MAE(平均绝对误差)和MRE(平均相对误差)分别达到了0.891、1.352%、0.915%和0.98%;IVYA-RF模型II验证结果的R2增大至0.956,RMSE、MAE和MRE分别减小至0.833%、0.687%和0.71%,同样显示出后者比前者具有更好的预测精度。实验验证结果表明,所构建的RD预测模型具有较好的鲁棒性,从而具备了较好的工程应用价值。展开更多
基金supported by National Natural Science Foundation of China:Space-based occultation detection with ground-based GNSS atmospheric horizontal gradient model(41904033).
文摘The satellite-based augmentation system(SBAS)provides differential and integrity augmentation services for life safety fields of aviation and navigation.However,the signal structure of SBAS is public,which incurs a risk of spoofing attacks.To improve the anti-spoofing capability of the SBAS,European Union and the United States conduct research on navigation message authentication,and promote the standardization of SBAS message authentication.For the development of Beidou satellite-based augmentation system(BDSBAS),this paper proposes navigation message authentication based on the Chinese commercial cryptographic standards.Firstly,this paper expounds the architecture and principles of the SBAS message authentication,and then carries out the design of timed efficient streaming losstolerant authentication scheme(TESLA)and elliptic curve digital signature algorithm(ECDSA)authentication schemes based on Chinese commercial cryptographic standards,message arrangement and the design of over-the-air rekeying(OTAR)message.Finally,this paper conducts a theoretical analysis of the time between authentications(TBA)and maximum authentication latency(MAL)for L5 TESLA-I and L5 ECDSA-Q,and further simulates the reception time of OTAR message,TBA and MAL from the aspects of OTAR message weight and demodulation error rate.The simulation results can provide theoretical supports for the standardization of BDSBAS message authentication.
文摘高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布;日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象,针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS和铺粉厚度LT与RD样本数据存在缺失参数和分布不均问题,采用最大期望化(EM)算法对缺失参数进行补齐;采用带有梯度惩罚的WGAN算法(WGAN-GP)对数据稀疏的低RD区间生成虚拟样本数据。然后,分别基于补齐文献数据和补充虚拟数据,采用常青藤算法优化的随机森林(IVYA-RF)构建了RD预测模型,并对模型预测精度进行了定量评估和实验验证。结果表明:基于补充虚拟数据集构建的IVYA-RF模型II比基于补齐文献数据集构建的IVYA-RF模型I具有更好的预测精度,其原因主要来源于在低RD区间生成虚拟数据后,使建模样本数据的分布均匀性得到改善,这也是数据增强与机器学习相结合的意义所在。对新实验数据的验证取得了满意的预测精度,其中,IVYA-RF模型I验证结果的统计学参数R2(决定系数)、RMSE(均方根误差)、MAE(平均绝对误差)和MRE(平均相对误差)分别达到了0.891、1.352%、0.915%和0.98%;IVYA-RF模型II验证结果的R2增大至0.956,RMSE、MAE和MRE分别减小至0.833%、0.687%和0.71%,同样显示出后者比前者具有更好的预测精度。实验验证结果表明,所构建的RD预测模型具有较好的鲁棒性,从而具备了较好的工程应用价值。
