In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(...In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
Separation and recognition of radar signals is the key function of modern radar reconnaissance,which is of great sig-nificance for electronic countermeasures and anti-countermea-sures.In order to improve the ability o...Separation and recognition of radar signals is the key function of modern radar reconnaissance,which is of great sig-nificance for electronic countermeasures and anti-countermea-sures.In order to improve the ability of separating mixed signals in complex electromagnetic environment,a blind source separa-tion algorithm based on degree of cyclostationarity(DCS)crite-rion is constructed in this paper.Firstly,the DCS criterion is con-structed by using the cyclic spectrum theory.Then the algo-rithm flow of blind source separation is designed based on DCS criterion.At the same time,Givens matrix is constructed to make the blind source separation algorithm suitable for multiple sig-nals with different cyclostationary frequencies.The feasibility of this method is further proved.The theoretical and simulation results show that the algorithm can effectively separate and re-cognize common multi-radar signals.展开更多
单主用户信号的出现主要引起多天线接收信号取样协方差矩阵中极值特征值的变化,而多主用户信号的出现则会同时扰动取样协方差矩阵极值特征值和其他特征值,此时,经典的极值特征值检测算法则会表现出次佳的检测性能。针对这一问题,本研究...单主用户信号的出现主要引起多天线接收信号取样协方差矩阵中极值特征值的变化,而多主用户信号的出现则会同时扰动取样协方差矩阵极值特征值和其他特征值,此时,经典的极值特征值检测算法则会表现出次佳的检测性能。针对这一问题,本研究设计了一种基于极值特征值差与特征值几何平均(difference of extreme eigenvalues and geometric average of eigenvalues,DEEGAE)的多主用户信号检测判决规则;提出了一种基于Wishart矩阵特征值统计分布理论的感知判决门限的闭式求解方法。该算法在频谱感知过程中直接利用认知用户的多天线接收数据构造判决规则并实施感知判决,具有全盲检测的优点;通过融合2种极限特征值门限分析结果,提高了非渐近感知条件下感知结果的准确性。Monte-Carlo仿真试验表明,新算法具有比经典的最大最小特征值之比算法和协方差绝对值检测算法更优的多主用户信号检测性能,同时能获得比传统基于最大最小特征值之差及其改进算法更为可靠的检测结果;与此同时,新算法的检测性能随着样本数目以及天线数目的增大而显著提升。展开更多
针对传统波达方向(Direction of Arrival,DOA)估计方法在低信噪比、少快拍数条件下表现性能差甚至失效的问题,提出了一种基于重构频域协方差矩阵的波达方位估计方法。该方法根据转化的频域信号进行共轭反向修正实现对噪声的抑制,构造出...针对传统波达方向(Direction of Arrival,DOA)估计方法在低信噪比、少快拍数条件下表现性能差甚至失效的问题,提出了一种基于重构频域协方差矩阵的波达方位估计方法。该方法根据转化的频域信号进行共轭反向修正实现对噪声的抑制,构造出了新的频域协方差矩阵,利用平均噪声子空间建立空间谱估计函数,通过谱峰搜索估计出信源的方位角。经仿真对比分析,所提改进方法可以识别多个相干信号,并且在低信噪比、少快拍数条件下仍然获得较好的方位估计性能,估计误差较传统算法降低2%~25%。展开更多
由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方...由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方差矩阵;利用该协方差矩阵对接收信号进行“白化”抑制;利用KF进行室内定位,同时针对KF滤波发散、误差较大的问题,利用RBF神经网络对误差进行在线修正,提升滤波性能。实验结果表明,该方法在NLOS环境下能够获得亚米级的定位精度,并具有较强的环境适应性。展开更多
基金supported by the National Science Foundation of China(61561016 61861008+4 种基金 11603041)the Guangxi Natural Science Foundation Project(2018JJA170090)the Innovation Project of Guet Graduate Education(2018YJCX19 2018YJCX31)Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology(DH201707)
文摘In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金supported by the National Natural Science Foundation of China(61502522).
文摘Separation and recognition of radar signals is the key function of modern radar reconnaissance,which is of great sig-nificance for electronic countermeasures and anti-countermea-sures.In order to improve the ability of separating mixed signals in complex electromagnetic environment,a blind source separa-tion algorithm based on degree of cyclostationarity(DCS)crite-rion is constructed in this paper.Firstly,the DCS criterion is con-structed by using the cyclic spectrum theory.Then the algo-rithm flow of blind source separation is designed based on DCS criterion.At the same time,Givens matrix is constructed to make the blind source separation algorithm suitable for multiple sig-nals with different cyclostationary frequencies.The feasibility of this method is further proved.The theoretical and simulation results show that the algorithm can effectively separate and re-cognize common multi-radar signals.
文摘单主用户信号的出现主要引起多天线接收信号取样协方差矩阵中极值特征值的变化,而多主用户信号的出现则会同时扰动取样协方差矩阵极值特征值和其他特征值,此时,经典的极值特征值检测算法则会表现出次佳的检测性能。针对这一问题,本研究设计了一种基于极值特征值差与特征值几何平均(difference of extreme eigenvalues and geometric average of eigenvalues,DEEGAE)的多主用户信号检测判决规则;提出了一种基于Wishart矩阵特征值统计分布理论的感知判决门限的闭式求解方法。该算法在频谱感知过程中直接利用认知用户的多天线接收数据构造判决规则并实施感知判决,具有全盲检测的优点;通过融合2种极限特征值门限分析结果,提高了非渐近感知条件下感知结果的准确性。Monte-Carlo仿真试验表明,新算法具有比经典的最大最小特征值之比算法和协方差绝对值检测算法更优的多主用户信号检测性能,同时能获得比传统基于最大最小特征值之差及其改进算法更为可靠的检测结果;与此同时,新算法的检测性能随着样本数目以及天线数目的增大而显著提升。
文摘针对传统波达方向(Direction of Arrival,DOA)估计方法在低信噪比、少快拍数条件下表现性能差甚至失效的问题,提出了一种基于重构频域协方差矩阵的波达方位估计方法。该方法根据转化的频域信号进行共轭反向修正实现对噪声的抑制,构造出了新的频域协方差矩阵,利用平均噪声子空间建立空间谱估计函数,通过谱峰搜索估计出信源的方位角。经仿真对比分析,所提改进方法可以识别多个相干信号,并且在低信噪比、少快拍数条件下仍然获得较好的方位估计性能,估计误差较传统算法降低2%~25%。
文摘由于非视距(Non-Line of Sight,NLOS)信号的存在,基于卡尔曼滤波(Kalman Filter,KF)的超宽带室内定位方法会出现定位精度下降的问题,提出一种自适应NLOS信号抑制联合KF的UWB定位算法。对UWB接收信号进行建模,并估计得到NLOS信号的协方差矩阵;利用该协方差矩阵对接收信号进行“白化”抑制;利用KF进行室内定位,同时针对KF滤波发散、误差较大的问题,利用RBF神经网络对误差进行在线修正,提升滤波性能。实验结果表明,该方法在NLOS环境下能够获得亚米级的定位精度,并具有较强的环境适应性。
