In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise p...In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.展开更多
为提高复杂海洋环境中无人机自主降落时分割目标的实时性和精确性,研究主干网络和浅深层特征对分割算法性能的影响问题,基于DeepLabV3+框架建立一种基于主干网络浅深层特征的无人机海上分割(shallow and deep features of backbone,SDFB...为提高复杂海洋环境中无人机自主降落时分割目标的实时性和精确性,研究主干网络和浅深层特征对分割算法性能的影响问题,基于DeepLabV3+框架建立一种基于主干网络浅深层特征的无人机海上分割(shallow and deep features of backbone,SDFB)算法。首先,针对风浪扰动降低目标稳定性的问题,优化MobileNetV2结构提出一种特征提取方法,解决了算法无法处理短时间目标变化较大图像的问题;然后,针对深层特征输出通道数较多且存在不均匀分布大气湍流噪声的问题,利用本地全局信息选择性地聚合特征,提出一种特征筛选机制,剔除冗余通道的同时解决了算法对环境噪声敏感度高的问题;其次,针对光照不匀降低目标边界清晰度问题,从浅层空间维度和深层通道维度中提取轮廓信息建立一种并行轮廓学习机制,解决了算法利用轮廓特征效率低的问题;最后,针对障碍物遮挡破坏目标特征完整性问题,融合优化后的条带池化建立一种特征融合机制,解决了算法无法联系离散分布特征问题。实验表明,SDFB算法的实时性和精确性均高于其他算法,能够更好地适应海上场景无人机分割目标需求。展开更多
基金supported by Shandong Provincial Natural Science Foundation(ZR2020MF015)Aerospace Technology Group Stability Support Project(ZY0110020009).
文摘In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.
文摘特定辐射源识别(Specific emitter identification,SEI)通过分析设备信号硬件特征保障物联网数据安全。现有的深度学习方法在进行特定辐射源识别时,样本数量受限,过于依赖大量已标记样本,无法做到高区分度表征,存在识别性能差的问题。针对这些问题,提出了基于样本插值(Mixup)增强的少样本SEI方法。首先采用Mixup的增强方式来扩展无线电信号样本的数量解决标注样本不足的问题;其次,基于孪生神经网络与复数神经网络(Complex-valued neural networks,CVNN)构建变体三元组网络(Triplet margin network based on CVNN,CVNN-TMN)提高模型的泛化能力和区分度,实现了少样本场景下特定辐射源的精准识别。实验结果表明,与现有多种先进SEI方法对比,在训练集和测试集样本划分比例不同情况下,提出的CVNN-TMN识别精度整体有5%~30%的提升,表明所构建的CVNN-TMN模型在区分度上的优异表现。
文摘为提高复杂海洋环境中无人机自主降落时分割目标的实时性和精确性,研究主干网络和浅深层特征对分割算法性能的影响问题,基于DeepLabV3+框架建立一种基于主干网络浅深层特征的无人机海上分割(shallow and deep features of backbone,SDFB)算法。首先,针对风浪扰动降低目标稳定性的问题,优化MobileNetV2结构提出一种特征提取方法,解决了算法无法处理短时间目标变化较大图像的问题;然后,针对深层特征输出通道数较多且存在不均匀分布大气湍流噪声的问题,利用本地全局信息选择性地聚合特征,提出一种特征筛选机制,剔除冗余通道的同时解决了算法对环境噪声敏感度高的问题;其次,针对光照不匀降低目标边界清晰度问题,从浅层空间维度和深层通道维度中提取轮廓信息建立一种并行轮廓学习机制,解决了算法利用轮廓特征效率低的问题;最后,针对障碍物遮挡破坏目标特征完整性问题,融合优化后的条带池化建立一种特征融合机制,解决了算法无法联系离散分布特征问题。实验表明,SDFB算法的实时性和精确性均高于其他算法,能够更好地适应海上场景无人机分割目标需求。
