Fast direction finding for bistatic EMVS-MIMO radar without pairing

Wen, Fangqing; Shi, Junpeng

doi:10.1016/j.sigpro.2020.107512

Cited by 71 publications

(67 citation statements)

References 20 publications

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“…Therefore, the virtual aperture of the proposed EMVS-MIMO is M t N t M r M r λ/2. In contrast, the transmit aperture of the methods in [14] (marked with 'ESPRIT'), [16] (marked with 'HOSVD'), [17] (marked with 'PM'), and [18] (marked with 'PARAFAC') is M λ/2, and the receive aperture of methods in ESPRIT, HOSVD, PM and PARAFAC is N λ/2. Usually, we have M t N t > M and M r N r > N , thus the proposed method offers larger aperture than that in [14]- [17].…”

Section: Algorithm Analysis a Degree-of-freedommentioning

confidence: 99%

“…Besides, additional pairing calculation is required, making these algorithms more complex. In [17], an improved PM estimator is derived. Unlike the PM-like approach in [15], the improved PM estimator achieves the elevation angles via the rotation invariance property of the whole virtual array, i.e., it makes full use of the array virtual aperture and thus provides improved AOA estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the vector cross-product were adopted to obtain the azimuth angles. Like the PM-like estimator in [17], the PARAFAClike estimator can achieve automatically paired 2D-DOD and 2D-DOA estimation. Moreover, it performs better than [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

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Fast Logistics Vehicle Localizing Based on EMVS-MIMO Radar and Edge Computing

Liu

Shi

et al. 2020

IEEE Access

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This paper focus on logistics vehicle active localizing using multiple-input multiple output (MIMO) radar, and a fast localizing architecture that integrates edge computing and cloud platform is proposed. The core of the proposed methodology is to measure the angles of logistics vehicle using bistatic MIMO radars, which are configured with coprime electromagnetic vector sensors (EMVS). Unlike the existing localization systems, the proposed localizing architecture provides two-dimensional (2D) angle estimation, i.e., 2D direction-of-departure (DOD) estimation and 2D direction-of-arrival (DOA) estimation, and it offers additional polarization status of the logistics vehicle. A parallel factor (PARAFAC) estimator is developed. Firstly, it estimate the factor matrices via PARAFAC decomposition. Thereafter, the elevation angle estimation is accomplished via least squares (LS) technique, which are ambiguous due to the coprime property of the EMVS. The azimuth angle estimation are followed via vector cross-product. Besides, the polarization information of the targets can be obtained estimated via LS approach. The above process is accomplished at cloud platform. Finally, the localization of the logistics vehicle is achieved with the estimated 2D angles, which is computationally friendly and achieved via edge computing. Detailed analyses concerning degree-of-freedom, identifiability, complexity as well as Cramér-Rao bound (CRB) are provided. To show the effectiveness of the proposed architecture, numerical simulations have been designed.

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Section: Algorithm Analysis a Degree-of-freedommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Logistics Vehicle Localizing Based on EMVS-MIMO Radar and Edge Computing

Liu

Shi

et al. 2020

IEEE Access

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“…Till now, various estimation strategies have been proposed for MIMO radars, such as multiple signal classification (MUSIC) [5], Capon, subspace fitting, estimating signal parameters via rotational invariance technique (ESPRIT) [6], least squares (LS) [7], maximum likelihood (ML) [8], tensor approaches [9]- [12], optimization-aware algorithms [13]- [15]. With respect to tensor approaches, there are two main tensor decomposition frameworks, called higher-order singular value decomposition (HOSVD) and parallel factor analysis (PARAFAC) decomposition.…”

Section: Introductionmentioning

confidence: 99%

Fast Angle Estimation and Sensor Self-Calibration in Bistatic MIMO Radar With Gain-Phase Errors and Spatially Colored Noise

et al. 2020

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Multiple-input multiple-output (MIMO) radars are essential in many Internet-of-Things (IoT) applications. Gain-phase error calibration is an important branch in MIMO radar. Existing calibration algorithms are only suitable for white Gaussian noise scenario, however, spatially colored noise is more practical in engineering implementation. In this paper, we stress the problem of direction finding and sensor self-calibration in a bistatic MIMO radar in the co-exist of unknown gain-phase error and spatially colored noise, and a covariance tensor-based parallel factor analysis (PARAFAC) estimator is proposed. To suppress the spatially colored noise and exploit the multi-dimensional structure of the array measurement, a covariance tensor is firstly established and the temporal cross-correlation operation is followed. Then the PARAFAC decomposition is carried out to obtain the factor matrices. Thereafter, automatically paired direction estimation is achieved via least squares. Finally, the element-wise multiply/divide technique and the Lagrange multipliers are adopted to obtain the gain-phase error vectors. The algorithm is analyzed in terms of identifiability and Cramer-Rao bound (CRB). Numerical simulations results show the effectiveness and improvement of the proposed estimator.

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“…e theory of compressive sensing (CS) uses a small amount of measurement information to reconstruct the original signal with a large probability through optimization, which is the research hot spot in the field of signal processing [3][4][5][6]. It has been used in ISAR imaging [7,8], MIMO radar signal processing [9,10], and radar parameter estimation [11][12][13][14]. Because the actual contour of the target in sky imaging background is smaller than imaging area, the scatters of the target have sparse structure compared with the imaging area.…”

Section: Introductionmentioning

confidence: 99%

ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

Feng

2020

Mathematical Problems in Engineering

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A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

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Fast direction finding for bistatic EMVS-MIMO radar without pairing

Cited by 71 publications

References 20 publications

Fast Logistics Vehicle Localizing Based on EMVS-MIMO Radar and Edge Computing

Fast Logistics Vehicle Localizing Based on EMVS-MIMO Radar and Edge Computing

Fast Angle Estimation and Sensor Self-Calibration in Bistatic MIMO Radar With Gain-Phase Errors and Spatially Colored Noise

ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

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