Stokes parameters and DOA estimation of polarised sources with unknown number of sources

Chang, Wenjuan; Ru, Jingpei; Deng, Lifei

doi:10.1049/iet-rsn.2017.0187

Cited by 13 publications

(12 citation statements)

References 39 publications

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“…However, the RMCVS has a larger size and the spatial phase shifts could add the complexity. Comparing with the antenna array used in Wong and Yuan (2011), Song et al (2014), , Wong and Lai (2005), Wong (1999), Li and Stoica (1994), Chang, Ru, et al (2018), Chang, Li, et al (2018), He et al (2013), and , the manifold of the RMCVS is better suited for a biquaternion-based representation.…”

Section: 1029/2019rs006796mentioning

confidence: 97%

“…However, these approaches cannot estimate the polarization parameters of PP sources. In Chang, Ru, et al () and Chang, Li, et al (), the polarization parameters and DOAs of polarized sources were estimated using sparse signal reconstruction, based on a coprime array of crossed dipoles. In these approaches, however, the grid mismatch error appears when the location of target do not fall on predefined grid.…”

Section: Introductionmentioning

confidence: 99%

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Estimation Algorithm of Incident Sources' Stokes Parameters and 2‐D DOAs Based on Reduced Mutual Coupling Vector Sensor

Tao

Yang

2019

Radio Science

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To estimate Stokes parameters and two-dimensional (2-D) direction-of-arrival (DOA) of incident sources, a computational efficient algorithm is proposed based on reduced mutual coupling vector sensor. The biquaternion-based output model of reduced mutual coupling vector sensor is presented. The contribution in this paper is mainly on the estimation of Stokes parameters. By exploiting the information in both the covariance matrix and the pseudo-covariance matrix of the biquaternion-based sensor's output, the Stokes parameters and 2-D DOAs of partially polarized source can be extracted in closed form. Comparing with the Jone's formulation, the Stokes' formulation can characterize both completely polarized waves and partially polarized waves. Thus, the proposed algorithm is also applicable to completely polarized source. Because the proposed algorithm presents in closed form and does not require the search or iterative process, it is computationally more efficient. Simulations are used to verify the performance of the proposed algorithm.

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Section: 1029/2019rs006796mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Estimation Algorithm of Incident Sources' Stokes Parameters and 2‐D DOAs Based on Reduced Mutual Coupling Vector Sensor

Tao

Yang

2019

Radio Science

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“…The major steps of the proposed algorithm are summarized as follows. 1) Compute the cross-covariance matrices R 12 and R 31 via (11) and (22) by using the electric and magnetic field vector components of subarray 1, 2 and 3, and get the vector forms through vectorization operation. 2) Obtain the selection matrices P 12 and P 31 according to the property of co-prime numbers M , N .…”

Section: Algorithm Summarymentioning

confidence: 99%

“…In this section, a series of numerical simulations under different conditions are conducted to investigate the estimation performance of the proposed algorithm. The results are compared with PCPA algorithm [22], TPCPA algorithm [27], and LV-MUSIC algorithm [10]. The sensor number is set to be 14.…”

Section: Simulationsmentioning

confidence: 99%

Three-Parallel Co-Prime Polarization Sensitive Array for 2-D DOA and Polarization Estimation via Sparse Representation

Si¹,

Wang²,

Zhang³

2019

IEEE Access

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Co-prime array configurations are considered attractive due to the extension of degrees of freedom (DOFs) and the sparse placement of array elements. In this paper, a 2-D direction-of-arrival (DOA) and polarization estimation algorithm are proposed with the three-parallel co-prime polarization sensitive array which consists of the co-centered orthogonal loop and dipole. A novel cross-covariance matrix, that not contains the polarization parameters, is constructed to decouple the joint estimation problem of 2-D DOA angles and polarization parameters. Then, by using the vectorization operation and linear transformation, a virtual uniform linear array with larger DOFs is achieved. Meanwhile, a sparse representation-based algorithm is presented to estimate 2-D DOA angles with the only 1-D dictionary. To avoid the selection of regularization parameter in the sparse recovery process, we derive the constraint form of the optimization problem based on the upper bound of the data fitting error, which can reduce the effect of improper selection on regularization parameter. Finally, the polarization parameters are estimated via a least squares method. Since the proposed algorithm constructs the data vector with cross-covariance matrices between subarrays, the influence of noise is suppressed, and the estimation accuracy with low signal-to-noise ratio is enhanced. In the end, the simulation results demonstrate the effectiveness of the proposed algorithm.

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“…For example, the twolevel nested array with only N = N 1 + N 2 physical antennas can provide 2(N 1 + 1)N 2 − 1 DOFs for its difference co-array, which will increase the resolution of the number of signals and efficiently improve the estimation performance of methods [11], [18], [19]. More recently, the nested and coprime scalar-sensor arrays have been extended to nested vector-sensor array [20]- [22] (called nested PSA) and coprime vector-sensor array (called coprime PSA) [23]- [25], which inherit the advantages of their scalar-sensor counterparts and then are applied for DOA and polarization state estimation. Accordingly, the subspace-based algorithms are exploited for estimating DOA and polarization state for EM signals [20], [21], [25].…”

Section: Introductionmentioning

confidence: 99%

Direction of Arrival and Polarization Estimation for Nested Polarization Sensitive Array

et al. 2019

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This paper presents a new polarization sensitive array composed of a two-level nested subarray and an optimally nested subarray with orthogonal oriented antennas, and then proposes a correlation matrix reconstruction based estimation method for direction of arrival (DOA) and polarization state. The optimally nested array with N antennas can provide maximum degrees of freedom (DOF) of difference co-array (i.e., (N − 1)N + 1), which increases the aperture of the proposed array. However, both the difference co-arrays of the optimally nested subarray and between subarrays are nonuniform linear arrays (i.e., holes appear), and hence most existing methods fall to use all information received from the proposed array, resulting in estimation performance loss. Depending on the oblique projection (OP) operator constructed by initial DOAs from the two-level nested subarray, the proposed method first fills the holes to generate the virtual correlation matrix with increased DOF. Then the DOA and polarization state are estimated efficiently. The resulting DOAs can be regarded as the new initial angles for OP operator construction, to iterate aforesaid steps for estimation performance enhancement. The Cramér-Rao bound (CRB) and the computational complexity of the proposed method are provided. Simulation results are given to validate the effectiveness of the proposed array and the proposed method.

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Stokes parameters and DOA estimation of polarised sources with unknown number of sources

Cited by 13 publications

References 39 publications

Estimation Algorithm of Incident Sources' Stokes Parameters and 2‐D DOAs Based on Reduced Mutual Coupling Vector Sensor

Estimation Algorithm of Incident Sources' Stokes Parameters and 2‐D DOAs Based on Reduced Mutual Coupling Vector Sensor

Three-Parallel Co-Prime Polarization Sensitive Array for 2-D DOA and Polarization Estimation via Sparse Representation

Direction of Arrival and Polarization Estimation for Nested Polarization Sensitive Array

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