Sparse aperture ISAR imaging algorithm based on adaptive filtering framework

Zeng, Chuangzhan; Zhu, Wei; Xin, Jia

doi:10.1049/iet-rsn.2018.5420

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…Recently, a l 1 norm optimisation algorithm based on the ADMM algorithm has attracted much attention due to its small computational burden [15]. The l 1 norm optimisation problem (7) in ADMM can be rewritten as:…”

Section: Sa-isar Imaging Based On Admmmentioning

confidence: 99%

“…(2) In modern radar systems, a radar usually has multiple functions. The radar resources have to periodically or nonperiodically switch to accomplish different tasks, such as target detection, tracking, imaging, and so on [7][8][9]. Then the radar beam can't continuously observe one target, which means that the observation time of the same target is not continuous.…”

Section: Introductionmentioning

confidence: 99%

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Novel approach for sparse aperture inverse synthetic aperture radar imaging via improved alternating direction method of multipliers algorithm

Yang

Wang

2023

IET Radar Sonar & Navi

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As a new algorithm to achieve sparse aperture inverse synthetic aperture radar (SA‐ISAR) imaging, the alternating direction method of multipliers (ADMM) suffers from selection of model parameters and the inability to discriminate the target and non‐target components. Therefore, an improved ADMM (I‐ADMM) algorithm is proposed to solve these problems. First, the particle swarm optimisation (PSO) algorithm is utilised to obtain the optimal model parameters. Aiming to adequately preserve the target's shape information in the ISAR image, a modified image entropy (IE) is proposed to be the objective function of the PSO. Then, a new regularisation function is obtained from the ADMM based ISAR image. The proposed algorithm can enhance the target components and suppress the non‐target components by utilising the regularisation function. Finally, results based on the simulated and measured data show that the proposed I‐ADMM algorithm not only achieves at least 10% improvement in IE but also greatly improves the image quality as visually compared with the traditional ADMM algorithm which validates the effectiveness and superiority of the proposed algorithm.

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Section: Sa-isar Imaging Based On Admmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel approach for sparse aperture inverse synthetic aperture radar imaging via improved alternating direction method of multipliers algorithm

Yang

Wang

2023

IET Radar Sonar & Navi

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show abstract

“…The filter noise reduction method is mainly based on the statistical characteristics of the signal, and estimates the useful signal from the mixed signal or filters out the noise according to the prior statistical characteristics to improve the SNR. The widely used methods include: Kalman filter [13]; Wiener filter [14]; and adaptive filter [15], etc. However, these types of methods are relatively dependent on the prior information of the statistical characteristics of the signal and noise and have poor noise reduction performance and high computational complexity for non-stationary large bandwidth signals.…”

Section: Relative Workmentioning

confidence: 99%

Research on Bi–ISAR Sparse Aperture High Resolution Imaging Algorithm under Low SNR

Zhu

Guo

et al. 2022

Electronics

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In the imaging process of bistatic inverse synthetic aperture radar (Bi–ISAR), the echo is easily affected by the internal interference and external environment of the radar system, resulting in the problems of sparse aperture and low echo signal noise ratio. The efficiency of conventional sparse aperture imaging methods is greatly reduced. To solve the above problems, a Bi–ISAR sparse aperture imaging algorithm based on Complex Variational Modal Decomposition (CVMD) and wavelet threshold de–noising is proposed. Firstly, the Bi–ISAR sparse aperture echo signal model is established, the sparse basis matching the echo is constructed, the echo signal is decomposed into different intrinsic mode functions (IMF) by CVMD, and the IMFs belonging to the signal are separated by the energy relationship criterion. Then, after the IMFs is de–noising by the improved wavelet threshold de–noising algorithm, the original signal is synthesized, and the signal after de–noising is reconstructed by using the orthogonal matching pursuing algorithm. Simulation results show that the proposed algorithm can achieve Bi–ISAR sparse aperture high−resolution imaging under low signal noise ratio and has fine anti−noise performance.

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“…Echo data may be discontinuous with sparse aperture 5 – 8 The coherence between pulses and echo data is destroyed with sparse aperture, which affects the autofocus imaging performance 9 – 11 Serious side lobes and energy leakage are induced by the range-Doppler (RD) imaging algorithm 2 .…”

Section: Introductionmentioning

confidence: 99%

Inverse synthetic aperture radar autofocus imaging of block structure targets with sparse aperture

Zhu

Zhang

Guo³

et al. 2023

J. Appl. Rem. Sens.

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Inverse synthetic aperture radar (ISAR) autofocus imaging performance is challenged by the residual phase errors that arise from the traditional motion compensation methods with sparse aperture. Moreover, sparse reconstruction algorithms usually ignore the block sparsity in the ISAR image, which cannot recover structured information of the block structure target completely. An imaging method based on joint minimum Tsallis entropy and pattern-coupled sparse Bayesian learning algorithm is proposed to achieve ISAR autofocus imaging of block structure targets with sparse aperture. A pattern-coupled hierarchical complex Gaussian prior is utilized to characterize the correlation among ISAR image pixels in the complex domain. Such a prior model has the potential to encourage block-sparse patterns and achieve ISAR sparse aperture imaging of block structure targets without knowing the block partition prior information. The residual phase errors are estimated based on the minimum Tsallis entropy criterion during the sparse reconstruction of ISAR image to achieve ISAR autofocus imaging, which has the advantage of improving the computational efficiency compared with minimum Shannon entropy criterion. The superiority of the proposed algorithm is verified by the experimental results based on both simulated and measured data.

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Sparse aperture ISAR imaging algorithm based on adaptive filtering framework

Cited by 10 publications

References 17 publications

Novel approach for sparse aperture inverse synthetic aperture radar imaging via improved alternating direction method of multipliers algorithm

Novel approach for sparse aperture inverse synthetic aperture radar imaging via improved alternating direction method of multipliers algorithm

Research on Bi–ISAR Sparse Aperture High Resolution Imaging Algorithm under Low SNR

Inverse synthetic aperture radar autofocus imaging of block structure targets with sparse aperture

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