The received signal of the polarization sensitive array is proved to have trilinear model characteristics. The blind parallel factor（PARAFAC） signal detection algorithm for the polarization sensitive array is propos...The received signal of the polarization sensitive array is proved to have trilinear model characteristics. The blind parallel factor（PARAFAC） signal detection algorithm for the polarization sensitive array is proposed. The trilinear alternating least square （TALS） algorithm is used to abtain the source matrix, and then the matrix is judged. Simulation results show that the bit error rate （BER） of the detection algorithm is close to that of the non-blind decorrelating method and the algorithm works well under the array error condition. BER difference between the non-blind method and this algorithm is less than 2 dB under a high SNR. The algorithm is blind and robust. The channel fading, the direction of arrive（DOA） imformation and the polarization information are needless in the algorithm.展开更多
Parallel arrays with coprime subarrays have shown its potential advantages for two dimensional direction of arrival(DOA)estimation.In this paper,by introducing two flexible coprime factors to enlarge the inter-element...Parallel arrays with coprime subarrays have shown its potential advantages for two dimensional direction of arrival(DOA)estimation.In this paper,by introducing two flexible coprime factors to enlarge the inter-element spacing of parallel uniform subarrays,we propose a generalized parallel coprime array(GPCA)geometry.The proposed geometry enjoys flexible array layouts by the coprime factors and enables to extend the array aperture to achieve great improvement of estimation performance.Meanwhile,we verify that GPCA always can obtain M2 degrees of freedom(DOFs)in co-array domain via 2M sensors after optimization,which outperforms sparse parallel array geometries,such as parallel coprime array(PCA)and parallel augmented coprime array(PACA),and is the same as parallel nested array(PNA)with extended aperture.The superiority of GPCA geometry has been proved by numerical simulations with sparse representation methods.展开更多
平行因子(Parallel Factor,PARAFAC)作为一种张量数据处理算法,在宽松约束条件下其模型分解具有唯一性。本文将局域均值分解(Local mean decomposition,LMD)和PARAFAC相结合,提出一种新的欠定盲源分离算法。利用局域均值分解得到观测信...平行因子(Parallel Factor,PARAFAC)作为一种张量数据处理算法,在宽松约束条件下其模型分解具有唯一性。本文将局域均值分解(Local mean decomposition,LMD)和PARAFAC相结合,提出一种新的欠定盲源分离算法。利用局域均值分解得到观测信号的生产函数(Production functions,PF)分量,再与原观测信号组合得到新的观测信号,从而将欠定混合转换为额定或超定混合源分离问题。对新观测信号进行白化预处理并构造为PARAFAC模型,并利用三线性交替最小二乘(Trilinear Alternating Least Square,TALS)算法实现PARAFAC模型分解,从而得到源信号的估计。通过仿真结果表明LMDPARAFAC算法能够从非平稳欠定混合信号中准确估计源信号。将所提算法应用到多机振动源实验中,实验结果进一步验证了该算法的有效性。展开更多
近年来,基于张量补全的频谱制图得到了广泛研究.目前用于频谱制图的张量补全算法大多隐含地假设张量具有平衡特性,而对于非平衡张量,难以利用其低秩性估计完整的张量信息,导致补全算法性能受损.本文提出基于重叠Ket增强(Overlapping Ket...近年来,基于张量补全的频谱制图得到了广泛研究.目前用于频谱制图的张量补全算法大多隐含地假设张量具有平衡特性,而对于非平衡张量,难以利用其低秩性估计完整的张量信息,导致补全算法性能受损.本文提出基于重叠Ket增强(Overlapping Ket Augmentation,OKA)和张量列车(Tensor Train,TT)的非平衡频谱制图算法,以解决非平衡张量在应用传统张量补全算法时性能下降的问题.首先使用OKA将低阶高维张量表示为高阶低维张量,在无信息损耗的情况下解决非平衡张量无法利用其低秩性进行张量补全的问题;然后使用TT矩阵化得到较平衡的矩阵,在维度较平衡条件下提高补全算法的精确度;最后利用高阶低维张量的低秩性,使用并行矩阵分解或基于F范数的无奇异值分解(Singular Value Decomposition Free,SVDFree)算法完成张量补全.仿真结果表明,针对非平衡张量,所提方案与现有的张量补全算法相比,可以获得更精确的无线电地图,同时所提SVDFree算法具有更低的计算复杂度.展开更多
文摘The received signal of the polarization sensitive array is proved to have trilinear model characteristics. The blind parallel factor（PARAFAC） signal detection algorithm for the polarization sensitive array is proposed. The trilinear alternating least square （TALS） algorithm is used to abtain the source matrix, and then the matrix is judged. Simulation results show that the bit error rate （BER） of the detection algorithm is close to that of the non-blind decorrelating method and the algorithm works well under the array error condition. BER difference between the non-blind method and this algorithm is less than 2 dB under a high SNR. The algorithm is blind and robust. The channel fading, the direction of arrive（DOA） imformation and the polarization information are needless in the algorithm.
文摘Parallel arrays with coprime subarrays have shown its potential advantages for two dimensional direction of arrival(DOA)estimation.In this paper,by introducing two flexible coprime factors to enlarge the inter-element spacing of parallel uniform subarrays,we propose a generalized parallel coprime array(GPCA)geometry.The proposed geometry enjoys flexible array layouts by the coprime factors and enables to extend the array aperture to achieve great improvement of estimation performance.Meanwhile,we verify that GPCA always can obtain M2 degrees of freedom(DOFs)in co-array domain via 2M sensors after optimization,which outperforms sparse parallel array geometries,such as parallel coprime array(PCA)and parallel augmented coprime array(PACA),and is the same as parallel nested array(PNA)with extended aperture.The superiority of GPCA geometry has been proved by numerical simulations with sparse representation methods.
文摘平行因子(Parallel Factor,PARAFAC)作为一种张量数据处理算法,在宽松约束条件下其模型分解具有唯一性。本文将局域均值分解(Local mean decomposition,LMD)和PARAFAC相结合,提出一种新的欠定盲源分离算法。利用局域均值分解得到观测信号的生产函数(Production functions,PF)分量,再与原观测信号组合得到新的观测信号,从而将欠定混合转换为额定或超定混合源分离问题。对新观测信号进行白化预处理并构造为PARAFAC模型,并利用三线性交替最小二乘(Trilinear Alternating Least Square,TALS)算法实现PARAFAC模型分解,从而得到源信号的估计。通过仿真结果表明LMDPARAFAC算法能够从非平稳欠定混合信号中准确估计源信号。将所提算法应用到多机振动源实验中,实验结果进一步验证了该算法的有效性。
文摘近年来,基于张量补全的频谱制图得到了广泛研究.目前用于频谱制图的张量补全算法大多隐含地假设张量具有平衡特性,而对于非平衡张量,难以利用其低秩性估计完整的张量信息,导致补全算法性能受损.本文提出基于重叠Ket增强(Overlapping Ket Augmentation,OKA)和张量列车(Tensor Train,TT)的非平衡频谱制图算法,以解决非平衡张量在应用传统张量补全算法时性能下降的问题.首先使用OKA将低阶高维张量表示为高阶低维张量,在无信息损耗的情况下解决非平衡张量无法利用其低秩性进行张量补全的问题;然后使用TT矩阵化得到较平衡的矩阵,在维度较平衡条件下提高补全算法的精确度;最后利用高阶低维张量的低秩性,使用并行矩阵分解或基于F范数的无奇异值分解(Singular Value Decomposition Free,SVDFree)算法完成张量补全.仿真结果表明,针对非平衡张量,所提方案与现有的张量补全算法相比,可以获得更精确的无线电地图,同时所提SVDFree算法具有更低的计算复杂度.
