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
In tensor theory, the parallel factorization （PARAFAC）decomposition expresses a tensor as the sum of a set of rank-1tensors. By carrying out this numerical decomposition, mixedsources can be separated or unknown sys...In tensor theory, the parallel factorization （PARAFAC）decomposition expresses a tensor as the sum of a set of rank-1tensors. By carrying out this numerical decomposition, mixedsources can be separated or unknown system parameters can beidentified, which is the so-called blind source separation or blindidentification. In this paper we propose a numerical PARAFACdecomposition algorithm. Compared to traditional algorithms, wespeed up the decomposition in several aspects, i.e., search di-rection by extrapolation, suboptimal step size by Gauss-Newtonapproximation, and linear search by n steps. The algorithm is ap-plied to polarization sensitive array parameter estimation to showits usefulness. Simulations verify the correctness and performanceof the proposed numerical techniques.展开更多
This paper addresses the problem of direction-of-arrival （DOA） and polarization estima- tion with polarization sensitive arrays （PSA）, which has been a hot topic in the area of array signal processing during the p...This paper addresses the problem of direction-of-arrival （DOA） and polarization estima- tion with polarization sensitive arrays （PSA）, which has been a hot topic in the area of array signal processing during the past two or three decades. The sparse Bayesian learning （SBL） technique is introduced to exploit the sparsity of the incident signals in space to solve this problem and a new method is proposed by reconstructing the signals from the array outputs first and then exploit- ing the reconstructed signals to realize parameter estimation. Only 1-D searching and numerical calculations are contained in the proposed method, which makes the proposed method computa- tionally much efficient. Based on a linear array consisting of identically structured sensors, the proposed method can be used with slight modifications in PSA with different polarization structures. It also performs well in the presence of coherent signals or signals with different degrees of polarization. Simulation results are given to demonstrate the parameter estimation precision of the proposed method.展开更多
In this paper, new Cramér-Rao lower bounds （CRB） of the estimates of frequencies, two-dimensional arrival angles and polarization parameters of multiple incident signals are derived for a polarization sensitive...In this paper, new Cramér-Rao lower bounds （CRB） of the estimates of frequencies, two-dimensional arrival angles and polarization parameters of multiple incident signals are derived for a polarization sensitive array. The incident sources have distinct carrier-frequencies, in contrast to the modeling of all sources to be at the same known carrier-frequency, which has been investigated in the existing research literature on the Cramér-Rao bounds （CRB） for polarization sensitive direction finding. The derived CRBs are compact closed-form expressions and applicable to an arbitrary array geometry. Numerical examples and analysis of some special cases provide insights into the fact that the estimation accuracy of all parameters is enhanced with the increasing signal-to-noise ratio （SNR） and number of snapshots. In addition, they are hardly influenced by the sampling frequency and independent of the initial phase of incident sources. These insights offer guidelines to the system engineer on how to improve parameters＇ estimation accuracy.展开更多
This paper proposes a new algorithm for joint frequency, two-dimensional （2-D） directions-of-arrival （DOA）, and polarization estimation using parallel factor （PARAFAC） analysis model and cumulant. The proposed a...This paper proposes a new algorithm for joint frequency, two-dimensional （2-D） directions-of-arrival （DOA）, and polarization estimation using parallel factor （PARAFAC） analysis model and cumulant. The proposed algorithm designs a new array configuration, and extends the PARAFAC analysis model from the common data-domain and subspace-domain to the cumulant one, and forms three-way arrays by using the three cumulant matrices obtained from the properly chosen dipole outputs, and analyzes the uniqueness of low-rank decomposition of the three-way arrays, and then jointly estimates the source parameters via the low-rank decomposition of the constructed PARAFAC model. In comparison with the conventional methods, the proposed method alleviates the aperture loss, and avoids pairing parameter. Finally, the simulation results are presented to validate the performance of the proposed method.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.
基金supported by the National Natural Science Foundation of China(61571131)the Technology Innovation Fund of the 10th Research Institute of China Electronics Technology Group Corporation(H17038.1)
文摘In tensor theory, the parallel factorization （PARAFAC）decomposition expresses a tensor as the sum of a set of rank-1tensors. By carrying out this numerical decomposition, mixedsources can be separated or unknown system parameters can beidentified, which is the so-called blind source separation or blindidentification. In this paper we propose a numerical PARAFACdecomposition algorithm. Compared to traditional algorithms, wespeed up the decomposition in several aspects, i.e., search di-rection by extrapolation, suboptimal step size by Gauss-Newtonapproximation, and linear search by n steps. The algorithm is ap-plied to polarization sensitive array parameter estimation to showits usefulness. Simulations verify the correctness and performanceof the proposed numerical techniques.
基金co-supported by the National Natural Science Foundation of China(No.61302141)the Special Fund for Doctoral Subjects in Higher Education Institutions of China(No.20134307120023)
文摘This paper addresses the problem of direction-of-arrival （DOA） and polarization estima- tion with polarization sensitive arrays （PSA）, which has been a hot topic in the area of array signal processing during the past two or three decades. The sparse Bayesian learning （SBL） technique is introduced to exploit the sparsity of the incident signals in space to solve this problem and a new method is proposed by reconstructing the signals from the array outputs first and then exploit- ing the reconstructed signals to realize parameter estimation. Only 1-D searching and numerical calculations are contained in the proposed method, which makes the proposed method computa- tionally much efficient. Based on a linear array consisting of identically structured sensors, the proposed method can be used with slight modifications in PSA with different polarization structures. It also performs well in the presence of coherent signals or signals with different degrees of polarization. Simulation results are given to demonstrate the parameter estimation precision of the proposed method.
基金supported by the National Natural Science Foundation of China (61001209)Chinese State Oceanic Administration’s Special Funds for Scientific Research on Public Cause (200905029)+1 种基金the Fundamental Research Funds for the Central Universities (JY10000902010)the Aeronautical Science Fund(20100181010)
文摘In this paper, new Cramér-Rao lower bounds （CRB） of the estimates of frequencies, two-dimensional arrival angles and polarization parameters of multiple incident signals are derived for a polarization sensitive array. The incident sources have distinct carrier-frequencies, in contrast to the modeling of all sources to be at the same known carrier-frequency, which has been investigated in the existing research literature on the Cramér-Rao bounds （CRB） for polarization sensitive direction finding. The derived CRBs are compact closed-form expressions and applicable to an arbitrary array geometry. Numerical examples and analysis of some special cases provide insights into the fact that the estimation accuracy of all parameters is enhanced with the increasing signal-to-noise ratio （SNR） and number of snapshots. In addition, they are hardly influenced by the sampling frequency and independent of the initial phase of incident sources. These insights offer guidelines to the system engineer on how to improve parameters＇ estimation accuracy.
基金Supported by the National Natural Science Foundation of China (Grant No. 60901059/F0103)the Educational Department Foundations of Shaanxi Province (Grant No. 09JK629)the Doctor Research Start Fund of Xi’an University of Technology (Grant No. 116-210903)
文摘This paper proposes a new algorithm for joint frequency, two-dimensional （2-D） directions-of-arrival （DOA）, and polarization estimation using parallel factor （PARAFAC） analysis model and cumulant. The proposed algorithm designs a new array configuration, and extends the PARAFAC analysis model from the common data-domain and subspace-domain to the cumulant one, and forms three-way arrays by using the three cumulant matrices obtained from the properly chosen dipole outputs, and analyzes the uniqueness of low-rank decomposition of the three-way arrays, and then jointly estimates the source parameters via the low-rank decomposition of the constructed PARAFAC model. In comparison with the conventional methods, the proposed method alleviates the aperture loss, and avoids pairing parameter. Finally, the simulation results are presented to validate the performance of the proposed method.
