Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoo...Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.展开更多
传统基于相干积分结果最大值检测的开环信号到达时间(Time of Arrival,TOA)估计方法性能受限于采样率,TOA估计分辨率无法突破采样间隔限制。针对这一问题,提出一种基于定时曲线匹配拟合的开环TOA估计方法,通过将相干积分结果与定时曲线...传统基于相干积分结果最大值检测的开环信号到达时间(Time of Arrival,TOA)估计方法性能受限于采样率,TOA估计分辨率无法突破采样间隔限制。针对这一问题,提出一种基于定时曲线匹配拟合的开环TOA估计方法,通过将相干积分结果与定时曲线进行最优匹配拟合来估计信号到达时间。以直接序列扩频信号三角形定时曲线为例,首先基于相干积分结果和定时曲线构建拟合误差平方和模型,然后采用牛顿迭代法,通过最小化拟合误差平方和,实现对信号到达时间的最小二乘估计,最后评估了检测信噪比和采样率对估计精度的影响。与传统方法相比,所提方法能够将TOA估计误差的标准差降低50%~97%。该方法可推广应用于其他类型定时曲线。展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902)。
文摘Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.
文摘传统基于相干积分结果最大值检测的开环信号到达时间(Time of Arrival,TOA)估计方法性能受限于采样率,TOA估计分辨率无法突破采样间隔限制。针对这一问题,提出一种基于定时曲线匹配拟合的开环TOA估计方法,通过将相干积分结果与定时曲线进行最优匹配拟合来估计信号到达时间。以直接序列扩频信号三角形定时曲线为例,首先基于相干积分结果和定时曲线构建拟合误差平方和模型,然后采用牛顿迭代法,通过最小化拟合误差平方和,实现对信号到达时间的最小二乘估计,最后评估了检测信噪比和采样率对估计精度的影响。与传统方法相比,所提方法能够将TOA估计误差的标准差降低50%~97%。该方法可推广应用于其他类型定时曲线。
