An Eigenstructure Method for Estimating DOA and Sensor Gain-Phase Errors

Liu, Aifei; Liao, Guisheng; Zeng, Cao; Yang, Zhiwei; Xu, Qing

doi:10.1109/tsp.2011.2165064

Cited by 182 publications

(157 citation statements)

References 29 publications

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“…Therefore, the above algorithms will suffer from performance degradation or even fail to achieve accurate DOD and DOA estimation in many scenarios. A number of algorithms have been proposed to deal with the array calibration problem [13][14][15][16][17]. An iterative algorithm based on the MUSIC technique is proposed in [13], which can simultaneously achieve the angle and gain-phase uncertainties estimation.…”

Section: Introductionmentioning

confidence: 99%

Joint DOA and DOD Estimation Based on Tensor Subspace with Partially Calibrated Bistatic MIMO Radar

Wang

Huang

2018

International Journal of Antennas and Propagation

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A joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation algorithm based on tensor subspace approach for partially calibrated bistatic multiple-input multiple-output (MIMO) radar is proposed. By exploiting the multidimensional structure of the received data, a third-order measurement tensor is constructed. Consequently, the tensor-based signal subspace is achieved using the higher-order singular value decomposition (HOSVD). To achieve accurate DOA estimation with partially calibrated array, a closed-form solution is provided to estimate the gain-phase uncertainties of the transmit and receive arrays by modeling the imperfections of the arrays. Simulation results demonstrate the effectiveness of the proposed calibration algorithm.

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Section: Introductionmentioning

confidence: 99%

Joint DOA and DOD Estimation Based on Tensor Subspace with Partially Calibrated Bistatic MIMO Radar

Wang

Huang

2018

International Journal of Antennas and Propagation

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“…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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“…DOA estimation by array antenna is important in various applications including location information [1,2]. 2-D DOA also has played an important role in areas such as radar, sonar, radio astronomy, and mobile communication systems.…”

Section: Introductionmentioning

confidence: 99%

Decoupled Nominal 2-D Direction-of -Arrival Estimation Algorithm for Coherently Distributed Source

Han¹,

Wang²,

Zhao³

2014

Appl. Math. Inf. Sci.

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A computationally efficient method for nominal 2-D (azimuth and elevation) direction-of-arrival (DOA) estimation of coherently distributed source impinging on the far field is presented. Since the coherently distributed source is characterized by four parameters, the nominal azimuth DOA, angular spread of the nominal azimuth DOA, the nominal elevation DOA, and angular spread of the nominal elevation DOA, the computational complexity of the parameter estimation is normally high demanding. So a low complexity estimation algorithm is proposed in this paper, the key idea of which is to apply a subspace-based method without eigendecomposition in beamspace and a proposed second-order statistics for estimating the nominal elevation and azimuth DOAs. The proposed decoupled estimation algorithm does not involve any searching. It has a lower computational complexity particularly when the radio of array size to the number of source is large, at the expense of negligible performance loss. Simulation results are included to demonstrate the performance of the proposed technique.

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An Eigenstructure Method for Estimating DOA and Sensor Gain-Phase Errors

Cited by 182 publications

References 29 publications

Joint DOA and DOD Estimation Based on Tensor Subspace with Partially Calibrated Bistatic MIMO Radar

Joint DOA and DOD Estimation Based on Tensor Subspace with Partially Calibrated Bistatic MIMO Radar

Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

Decoupled Nominal 2-D Direction-of -Arrival Estimation Algorithm for Coherently Distributed Source

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