针对欠密度流星余迹干扰影响天波超视距雷达目标检测的问题,提出了基于总体最小二乘旋转不变估计信号参数(Total Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques TLS-ESPRIT)的欠密度流星余迹干扰抑...针对欠密度流星余迹干扰影响天波超视距雷达目标检测的问题,提出了基于总体最小二乘旋转不变估计信号参数(Total Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques TLS-ESPRIT)的欠密度流星余迹干扰抑制算法.首先应用复数据经验模式分解估算流星余迹干扰的位置,并将该位置的回波数据组成Hankel矩阵,然后采用TLS-ESPRIT方法求解Hankel矩阵,解得流星余迹干扰的时域回波,最后从回波数据中去除流星余迹干扰的时域回波,得到流星余迹干扰抑制后的回波数据.与现有流星余迹抑制算法相比,该方法减少了流星余迹干扰的残余和提高了目标的信杂比(SCNR).展开更多
Inverse synthetic aperture radar(ISAR)imaging of the target with the non-rigid body is very important in the field of radar signal processing.In this paper,a motion compensation method combined with the preprocessing ...Inverse synthetic aperture radar(ISAR)imaging of the target with the non-rigid body is very important in the field of radar signal processing.In this paper,a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain,and the micro-Doppler(m-D)signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition(CEMD)algorithm,which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center.Then,a better focused ISAR image of the target with the non-rigid body can be obtained consequently.Results of the simulated and raw data demonstrate the effectiveness of the algorithm.展开更多
为了减少复杂环境因素对电力负荷超短期预测效果的影响,提高算法的预测精度和运算效率,该文提出一种基于聚类经验模态分解(clusterempiricalmodedecomposition,CEMD)的卷积神经网络和长短期记忆网络(convolutional neural network and l...为了减少复杂环境因素对电力负荷超短期预测效果的影响,提高算法的预测精度和运算效率,该文提出一种基于聚类经验模态分解(clusterempiricalmodedecomposition,CEMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short term memory network,CNNLSTM)混合预测算法。该算法首先通过经验模态分解法将负荷数据分解为平稳性好、规律性强的若干本征模态函数(intrinsic mode functions,IMF)和残差(residual,Res)。其次为了简化后续模型的计算体量,运用k均值聚类方法对分解所得的各分量进行分组集成,同时分析不同聚类数对应的预测效果,选取最优聚类标签构造神经网络输入数据。之后将各组数据分别输入到CNN-LSTM混合神经网络中,利用CNN挖掘数据间的特征形成特征向量,并将其输入到LSTM中进行预测。最后将所有预测结果进行线性相加得到完整预测负荷。通过在真实负荷上进行验证并与现有模型进行比较,所提方法具有更高的预测精度。展开更多
为了抑制高频地波雷达(high frequency ground wave radar,HFGWR)射频干扰(radio frequency interference,RFI),提出了复数经验模态分解(CEMD)方法,在抑制射频干扰的同时,最大程度上保留有用信号。通过模拟及实测数据的验证分析,该方法...为了抑制高频地波雷达(high frequency ground wave radar,HFGWR)射频干扰(radio frequency interference,RFI),提出了复数经验模态分解(CEMD)方法,在抑制射频干扰的同时,最大程度上保留有用信号。通过模拟及实测数据的验证分析,该方法在不损失有用信号的基础上有效抑制了射频干扰,且处理速度快,满足高频地波雷达实时工作要求。展开更多
文摘针对欠密度流星余迹干扰影响天波超视距雷达目标检测的问题,提出了基于总体最小二乘旋转不变估计信号参数(Total Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques TLS-ESPRIT)的欠密度流星余迹干扰抑制算法.首先应用复数据经验模式分解估算流星余迹干扰的位置,并将该位置的回波数据组成Hankel矩阵,然后采用TLS-ESPRIT方法求解Hankel矩阵,解得流星余迹干扰的时域回波,最后从回波数据中去除流星余迹干扰的时域回波,得到流星余迹干扰抑制后的回波数据.与现有流星余迹抑制算法相比,该方法减少了流星余迹干扰的残余和提高了目标的信杂比(SCNR).
基金supported by the National Natural Science Foundation of China(61871146)the Fundamental Research Funds for the Central Universitiesthe State Key Laboratory of Millimeter Waves(K202022)。
文摘Inverse synthetic aperture radar(ISAR)imaging of the target with the non-rigid body is very important in the field of radar signal processing.In this paper,a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain,and the micro-Doppler(m-D)signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition(CEMD)algorithm,which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center.Then,a better focused ISAR image of the target with the non-rigid body can be obtained consequently.Results of the simulated and raw data demonstrate the effectiveness of the algorithm.
文摘为了减少复杂环境因素对电力负荷超短期预测效果的影响,提高算法的预测精度和运算效率,该文提出一种基于聚类经验模态分解(clusterempiricalmodedecomposition,CEMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short term memory network,CNNLSTM)混合预测算法。该算法首先通过经验模态分解法将负荷数据分解为平稳性好、规律性强的若干本征模态函数(intrinsic mode functions,IMF)和残差(residual,Res)。其次为了简化后续模型的计算体量,运用k均值聚类方法对分解所得的各分量进行分组集成,同时分析不同聚类数对应的预测效果,选取最优聚类标签构造神经网络输入数据。之后将各组数据分别输入到CNN-LSTM混合神经网络中,利用CNN挖掘数据间的特征形成特征向量,并将其输入到LSTM中进行预测。最后将所有预测结果进行线性相加得到完整预测负荷。通过在真实负荷上进行验证并与现有模型进行比较,所提方法具有更高的预测精度。
文摘为了抑制高频地波雷达(high frequency ground wave radar,HFGWR)射频干扰(radio frequency interference,RFI),提出了复数经验模态分解(CEMD)方法,在抑制射频干扰的同时,最大程度上保留有用信号。通过模拟及实测数据的验证分析,该方法在不损失有用信号的基础上有效抑制了射频干扰,且处理速度快,满足高频地波雷达实时工作要求。
