A Super-Resolution Sparse Aperture ISAR Sensors Imaging Algorithm via the MUSIC Technique

Liu, Qiuchen; Liu, Aijun; Wang, Yong; Li, Hongzhi

doi:10.1109/tgrs.2019.2911686

Cited by 23 publications

(12 citation statements)

References 32 publications

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“…In the above method, the sparsity of the signals should be previously estimated. For decades, various estimation methods of source number have been proposed, such as, the MDL [23], the AIC [24], and the GDE [25]. Among these type methods, the GDE can work efficiently in the situations of low SNR and measured data.…”

Section: The Estimation Of Signal Numbermentioning

confidence: 99%

Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors

Yao

Xin

et al. 2020

Sensors

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An adaptive beamformer is sensitive to model mismatch, especially when the desired signal exists in the training samples. Focusing on the problem, this paper proposed a novel adaptive beamformer based on the interference-plus-noise covariance (INC) matrix reconstruction method, which is robust with gain-phase errors for uniform or sparse linear array. In this beamformer, the INC matrix is reconstructed by the estimated steering vector (SV) and the corresponding individual powers of the interference signals, as well as noise power. Firstly, a gain-phase errors model of the sensors is deduced based on the first-order Taylor series expansion. Secondly, sensor gain-phase errors, the directions of the interferences, and the desired signal can be accurately estimated by using an alternating descent method. Thirdly, the interferences and noise powers are estimated by solving a quadratic optimization problem. To reduce the computational complexity, we derive the closed-form solutions of the second and third steps with compressive sensing and total least squares methods. Simulation results and measured data demonstrate that the performance of the proposed beamformer is always close to the optimum, and outperforms other tested methods in the case of gain-phase errors.

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Section: The Estimation Of Signal Numbermentioning

confidence: 99%

Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors

Yao

Xin

et al. 2020

Sensors

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“…After translational motion compensation, the target can be modelled as a rotating object in the range‐Doppler image domain. Based on the range compression result of the radar principle, range scaling can be achieved by exploiting the relationship

Δ r = c / 2 B

, where

Δ r

is the range resolution, c denotes the speed of light and B is the bandwidth of the transmitted signal [7,8]. If we use traditional ISAR imaging algorithms, say the range‐Doppler algorithm, the cross‐range resolution can be defined as

Δ y = λ / 2 T_{m} ω_{0}

, where

λ

denotes the wavelength of the transmitted signal, T m is the coherent processing interval (CPI), and

ω_{0}

denotes rotational velocity (RV) of the target [9,10].…”

Section: Introductionmentioning

confidence: 99%

A modified ISAR cross‐range scaling method based on iterative principle component analysis

Tong

Liu

2021

IET Signal Processing

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High-resolution inverse synthetic aperture radar (ISAR) imaging can produce electromagnetic images of a target in the range and cross-range domain. However, to make full use of an ISAR image in target detection and recognition, we need to rescale the image in a homogeneous range and cross-range domain, and further, to estimate the effective rotational velocity (RV) of the target. A precise ISAR image cross-range scaling method is proposed. A candidate RV is obtained based on the bisection method. Then, the effective rotation angle (RA) between two sequential rescaled images is characterised by the RA between the major axes of the images. These axes are extracted by principle component analysis (PCA). A precise estimation of the RV is obtained through iterative PCA and bisection. Simulated and real data experimental results validate the effectiveness of the proposed method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…However, the FFT spectrum has comparatively wide mainlobes and high sidelobes, whereas the resolutions were limited by the bandwidth and array aperture. The MUltiple SIgnal Classification (MUSIC) algorithms [33]- [37], most frequently used for angle estimation, were recently applied in Through-the-Wall Imaging (TWI) [38], MIMO radar [23], [25], [26] and ISAR [39] for azimuth imaging due to its super-resolution ability. In these works, the radial-range profile was firstly retrieved computing FFT.…”

Section: Introductionmentioning

confidence: 99%

“…In these works, the radial-range profile was firstly retrieved computing FFT. In [25], [26], [39], within each range unit, the cross-range was estimated using MUSIC afterwards by exploiting the phase differences among the virtual elements (virtual antennas in MIMO or Doppler virtual elements in ISAR). The transmitted waveform bandwidth needs to be high enough to meet the radial-range resolution requirement, whereas the computation and storage cost is heavy for these methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel High-Resolution Imaging Method Using Reduced-Dimension Beamspace Unitary MUSIC for OFDM-MIMO Radar

et al. 2020

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Imaging of maneuvering targets has become a challenging topic recently. This study proposes an improved MUSIC-based single-snapshot imaging method for OFDM-MIMO radar. At the transmitter part, for orthogonal waveform generation, the interleaved OFDM structure is developed that each transmitter only radiates at an equidistant set of OFDM subcarriers unique to itself. At the receiver part, a novel preprocessing scheme is presented to separate multiple transmit/receive paths and compensate the reference point. Preprocessed echo matrix can fit the model of two-Dimensional (2D) MUSIC algorithm, except for the coupling between radial/cross-range in the virtual antenna array. Correspondingly, we modify the 2D ElementSpace (ES)-MUSIC. In the meantime, a 2D Reduced-Dimension BeamSpace Unitary-MUSIC (RD-BS-UMUSIC) algorithm is proposed for computational complexity reduction and estimation performance enhancement. Numerical results of a simulated target consists of limited number of scatterers and a complex target (a Boeing 737-800 plane) verify that the proposed algorithm can improve the imaging quality and reduce computational complexity effectively compared with the traditional method. INDEX TERMS High-resolution imaging, OFDM-MIMO radar, single-snapshot imaging, 2D modified ES-MUSIC, 2D RD-BS-UMUSIC.

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A Super-Resolution Sparse Aperture ISAR Sensors Imaging Algorithm via the MUSIC Technique

Cited by 23 publications

References 32 publications

Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors

Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors

A modified ISAR cross‐range scaling method based on iterative principle component analysis

A Novel High-Resolution Imaging Method Using Reduced-Dimension Beamspace Unitary MUSIC for OFDM-MIMO Radar

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