Reduced-Dimension MUSIC for Angle and Array Gain-Phase Error Estimation in Bistatic MIMO Radar

Li, Jianfeng; Zhang, Xiaofei; Cao, Renzheng; Zhou, Ming

doi:10.1109/lcomm.2013.012313.122113

Cited by 73 publications

(50 citation statements)

References 10 publications

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“…where a m (∅ k ) � Γ t a m (∅ k ) and b n (θ k ) � Γ r b n (θ k ) with Γ t and Γ r as the unknown gain-phase error matrices among the transmit array elements and receive array elements, respectively. We define the gain-phase errors as follows [15]:…”

Section: Data Modelmentioning

confidence: 99%

“…e proposed algorithm computes Z circumventing the need for the formation of the entire SCM as it only requires Y 11 + q)). Nonetheless, the methods in [15,29]…”

Section: Computationalmentioning

confidence: 99%

“…In [14], an ESPRIT-like method is presented but requires an extra paring scheme. en, Li et al [15] proposed a propitious reduced-dimension MUSIC procedure to achieve angle and gain-phase error estimations. Unfortunately, these subspace methods do not consider the additional characteristics of the received signals and only resort to distinguishing the noise subspaces.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

Baidoo

Zhan

et al. 2020

International Journal of Antennas and Propagation

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In this paper, a robust angle estimator for uncorrelated targets that employs a compressed sense (CS) scheme following a fast greedy (FG) computation is proposed to achieve improved computational efficiency and performance for the bistatic MIMO radar with unknown gain-phase errors. The algorithm initially avoids the wholly computation of the received signal by compiling a lower approximation through a greedy Nyström approach. Then, the approximated signal is transformed into a sparse signal representation where the sparsity of the target is exploited in the spatial domain. Finally, a CS method, Simultaneous Orthogonal Matching Pursuit with an inherent gradient descent method, is utilized to reconstruct the signal and estimate the angles and the unknown gain-phase errors. The proposed algorithm, aside achieving closed-form resolution for automatically paired angle estimation, offers attractive computational competitiveness, specifically in large array scenarios. Additionally, the analyses of the computational complexity and the Cramér–Rao bounds for angle estimation are derived theoretically. Numerical experiments demonstrate the improvement and effectiveness of the proposed method against existing methods.

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Section: Data Modelmentioning

confidence: 99%

“…e proposed algorithm computes Z circumventing the need for the formation of the entire SCM as it only requires Y 11 + q)). Nonetheless, the methods in [15,29]…”

Section: Computationalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

Baidoo

Zhan

et al. 2020

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…5,6 To compensate the phase error, several eigenstructure-based methods are proposed. [7][8][9][10][11] These methods are less sensitive to phase error but lack adaptation to demanding scenarios with low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources. 12 Recently, sparse recovery and compressive sensing 13 are introduced into signal processing by exploiting the sparsity.…”

Section: Introductionmentioning

confidence: 99%

Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

Zhou

Wang

Cheng

et al. 2016

J. Electron. Imaging

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Abstract. Radar coincidence imaging (RCI) is a high-resolution imaging technique without the limitation of relative motion between target and radar. In sparsity-driven RCI, the prior knowledge of imaging model requires to be known accurately. However, the phase error generally exists as a model error, which may cause inaccuracies of the model and defocus the image. The problem is formulated using Bayesian hierarchical prior modeling, and the self-calibration variational message passing (SC-VMP) algorithm is proposed to improve the performance of RCI with phase error. The algorithm determines the phase error as part of the imaging process. The scattering coefficient and phase error are iteratively estimated using VMP and Newton's method, respectively. Simulation results show that the proposed algorithm can estimate the phase error accurately and improve the imaging quality significantly.

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“…Using Wiener filter to obtain the noise subspace was studied by Wang (2011) to reduce the complexity, but it only considered one-dimensional angle in monostatic MIMO radar. MUSIC algorithm can also be transformed to deal with the mutual coupling and gain-phase error problems (Li & Zhang, 2012;Li, Zhang, Cao, & Zhou, 2013), but heavy computation burden was still required. Iteration algorithms like parallel factor analysis algorithms (Nion & Sidiropoulos, 2009;Zhang, Xu, Xu, & Xu, 2011) also need high complexity due to the huge number of iterations for the convergence.…”

Section: Introductionmentioning

confidence: 99%

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

Zhang

Han

2013

International Journal of Electronics

Self Cite

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In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.

show abstract

Reduced-Dimension MUSIC for Angle and Array Gain-Phase Error Estimation in Bistatic MIMO Radar

Cited by 73 publications

References 10 publications

Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

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