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

Wang, Xianpeng; Wang, Wei; Liu, Jing; Liu, Qi; Wang, Ben

doi:10.1016/j.sigpro.2015.03.020

Cited by 73 publications

(45 citation statements)

References 20 publications

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“…Tensor-based decomposition frameworks such as parallel factor (PARAFAC) decomposition and Tucker decomposition, which make full use of the strong algebraic structure of multidimensional signals, are widely used for MIMO radar target localization [20][21][22]. The paper [21] shows a tensor-based real-valued subspace scheme, which combines the higher order singular value decomposition (HOSVD) technique with the methods based on real-valued subspace to estimate DODs and DOAs. In [22], a unitary PARAFAC method based on the transmit beam-space is proposed for bistatic MIMO radar.…”

Section: Introductionmentioning

confidence: 99%

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Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

Han

Jin

et al. 2020

Electronics

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The direction-of-departure (DOD) and the direction-of-arrival (DOA) are important localization parameters in bistatic MIMO radar. In this paper, we are interested in DOD/DOA estimation of both single-pulse and multiple-pulse multiple-input multiple-output (MIMO) radars. An iterative super-resolution target localization method is firstly proposed for single-pulse bistatic MIMO radar. During the iterative process, the estimated DOD and DOA can be moved from initial angles to their true values with high probability, and thus can achieve super-resolution estimation. It works well even if the number of targets is unknown. We then extend the proposed method to multiple-pulse configuration to estimate target numbers and localize targets. Compared with existing methods, both of our proposed algorithms have a higher localization accuracy and a more stable performance. Moreover, the proposed algorithms work well even with low sampling numbers and unknown target numbers. Simulation results demonstrate the effectiveness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

“…(3) In contrast to tensor-based methods in [20][21][22], the two proposed schemes do not need to transmit mutually orthogonal waveforms. In addition, the proposed ISR-M method has more easy uniqueness conditions than the algorithm in [20].…”

Section: Introductionmentioning

confidence: 99%

Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

Han

Jin

et al. 2020

Electronics

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“…On the other hand, all algorithms mentioned above need to stack the received data into a special structure matrix, which ignores the inherence multidimensional structure of signal. To utilize the inherent multidimensional structure of the signals, many methods have been developed [ 21 , 22 , 23 , 24 , 25 ]. A multi-SVD algorithm is developed to estimate DOD and DOA in MIMO radar [ 21 ], and the estimation performance is improved remarkably.…”

Section: Introductionmentioning

confidence: 99%

“…A multi-SVD algorithm is developed to estimate DOD and DOA in MIMO radar [ 21 ], and the estimation performance is improved remarkably. Considering the mutual coupling in transmit and receive arrays, the subspace estimation method based on unitary tensor decomposition is introduced in [ 23 ]. The algorithm converts the tensor subspace into a new real-valued tensor through using the unitary transformation while eliminating the influence of mutual coupling.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Guo

Wang

et al. 2018

Sensors

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In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

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“…However, in the case of small snapshots, the accuracy of angle estimation using both of the above approaches will degrade remarkably. By exploiting the multidimensional structure of the received data, a three-order tensor is constructed [15], which are DOD, DOA, and temporal dimensions, respectively. And a realvalued subspace approach is proposed; it computes the subspace utilizing the higher order singular value decomposition (HOSVD).…”

Section: Introductionmentioning

confidence: 99%

Transmit/Receive Spatial Smoothing with Improved Effective Array Aperture for Angle and Mutual Coupling Estimation in Bistatic MIMO Radar

Liu

Yang

Wang

et al. 2016

International Journal of Antennas and Propagation

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We proposed a transmit/receive spatial smoothing with improved effective aperture approach for angle and mutual coupling estimation in bistatic MIMO radar. Firstly, the noise in each channel is restrained, by exploiting its independency, in both the spatial domain and temporal domain. Then the augmented transmit and receive spatial smoothing matrices with improved effective aperture are obtained, by exploiting the Vandermonde structure of steering vector with uniform linear array. The DOD and DOA can be estimated by utilizing the unitary ESPRIT algorithm. Finally, the mutual coupling coefficients of both the transmitter and the receiver can be figured out with the estimated angles of DOD and DOA. Numerical examples are presented to verify the effectiveness of the proposed method.

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

Cited by 73 publications

References 20 publications

Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Transmit/Receive Spatial Smoothing with Improved Effective Array Aperture for Angle and Mutual Coupling Estimation in Bistatic MIMO Radar

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