Multi-SVD based subspace estimation to improve angle estimation accuracy in bistatic MIMO radar

Cheng, Yuanbing; Yu, Rusheng; Gu, Hong; Su, Weimin

doi:10.1016/j.sigpro.2012.12.021

Cited by 71 publications

(53 citation statements)

References 16 publications

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“…The Doppler shifts are

{f_{d p}}_{p = 1}^{3} = {300, 400, 500} Hz

. The spatial colored noise is modeled as a spatial complex autoregressive (AR) model of second-order with the coefficients z = [1, −1,0.2] [12], and the root mean square error (RMSE) of the angle estimation is defined as:

RMSE = \frac{1}{2 Q} \sqrt{\sum_{i = 1}^{Q} false({\hat{φ}}_{i} - φ_{i} false) + false({\hat{θ}}_{i} - θ_{i} false)}

where Q is the number of Monte Carlo trials and φ̂ i and θ̂ i are the estimation of DOD φ i and DOA θ i of the i -th Monte Carlo trial.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

Wang

et al. 2014

Sensors

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In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

show abstract

“…The Doppler shifts are

{f_{d p}}_{p = 1}^{3} = {300, 400, 500} Hz

RMSE = \frac{1}{2 Q} \sqrt{\sum_{i = 1}^{Q} false({\hat{φ}}_{i} - φ_{i} false) + false({\hat{θ}}_{i} - θ_{i} false)}

where Q is the number of Monte Carlo trials and φ̂ i and θ̂ i are the estimation of DOD φ i and DOA θ i of the i -th Monte Carlo trial.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

Wang

et al. 2014

Sensors

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“…Some available publications have explored several aspects of mutual coupling effect between antenna array elements on MIMO channels [7][8][9][10][11]. However, their research is only limited in the influences of the near-field mutual coupling effect on MIMO channels.…”

Section: Mechanism and Measurement Of The Simce In Mmw Mimo Communicamentioning

confidence: 99%

On the Rain-Induced Mutual Coupling Effect of Multiple-Input Multiple-Output Communication Systems at Millimeter Wave Band

Gong¹,

Wang²,

Yan³

2015

PIER M

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Abstract-The concept of Scattering-Induced Mutual Coupling Effect (SIMCE) is proposed, and the mechanism of producing this phenomenon in Multiple-Input Multiple-Output (MIMO) communication systems at MilliMeter Wave (MMW) band is demonstrated. The model of estimating the scatteringinduced mutual impedance in rain environment is derived, and the characteristics of Rain-Induced Scattering Mutual Impedance (RISMI) are discussed taking parabolic antennas as an example. The model of estimating the rain-induced mutual impedance is helpful for investigating the SIMCE in other discrete random media. And, the results given in this paper are significant for developing MMW MIMO communication systems.

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“…Then the signal subspace or noise subspace can be estimated by eigenvalue decomposition (EVD) of the covariance matrix or singular value decomposition (SVD) of the received data. In [13,14], the multidimensional structure of the received data in bistatic MIMO radar is considered for improving the angle estimation performance. Compared with conventional ESPRIT algorithm, the multi-SVD technique in [14] provides better angle estimation, especially in low SNR region and limited snapshots.…”

Section: Introductionmentioning

confidence: 99%

“…In [13,14], the multidimensional structure of the received data in bistatic MIMO radar is considered for improving the angle estimation performance. Compared with conventional ESPRIT algorithm, the multi-SVD technique in [14] provides better angle estimation, especially in low SNR region and limited snapshots. Unfortunately, the method in [14] is not suitable for the angle estimation in the presence of mutual coupling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling

Wang

Liu

et al. 2015

Signal Processing

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Multi-SVD based subspace estimation to improve angle estimation accuracy in bistatic MIMO radar

Cited by 71 publications

References 16 publications

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

On the Rain-Induced Mutual Coupling Effect of Multiple-Input Multiple-Output Communication Systems at Millimeter Wave Band

Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling

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