A Two-Step Method for Multitarget ISAR Imaging Based on Dual-Precision Optimization

Li, Hongxu; Su, Fulin; Xu, Xinbo

doi:10.1109/tgrs.2022.3208037

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…A defined diagonal matrix D with the p th diagonal entry

{D}_{pp}=\sum\limits _{q}{W}_{pq}

. Define B = D − W as the graph Laplacian matrix [26, 29]. Then the Laplacian regularisation is

\begin{align*}\hfill \underset{\mathbf{X}}{\min }\begin{array}{c}\hfill \mathrm{T}\mathrm{r}\hfill \end{array}\left(\mathbf{X}\mathbf{B}{\mathbf{X}}^{H}\right)\hfill \end{align*}

where

\mathrm{T}\mathrm{r}\left(\mathbf{A}\right)

is the trace of matrix A .…”

Section: Modified Rap‐so Methodsmentioning

confidence: 99%

“…A defined diagonal matrix D with the pth diagonal entry D pp ¼ P q W pq . Define B ¼ D W as the graph Laplacian matrix [26,29]. Then the Laplacian regularisation is min…”

Section: Laplacian Regularisationmentioning

confidence: 99%

“…where rank(X) is the rank of X, kAk F is the Frobenius norm of A and Ω is the sample number. Equation ( 17) is a NP-hard problem, so the nuclear norm can be used as an approximation [25,26]. Nonetheless, considering an ISAR system which is working under low SNR conditions, the nuclear norm minimisation is difficult to timely discard the smaller singular values which represent the noise.…”

Section: Weighted Nuclear Normmentioning

confidence: 99%

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Similarity‐oriented method for inverse synthetic aperture radar imaging with low signal‐to‐noise ratio

Xu,

Zhang,

et al. 2024

IET Radar Sonar & Navi

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Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.

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“…A defined diagonal matrix D with the p th diagonal entry

{D}_{pp}=\sum\limits _{q}{W}_{pq}

. Define B = D − W as the graph Laplacian matrix [26, 29]. Then the Laplacian regularisation is

\begin{align*}\hfill \underset{\mathbf{X}}{\min }\begin{array}{c}\hfill \mathrm{T}\mathrm{r}\hfill \end{array}\left(\mathbf{X}\mathbf{B}{\mathbf{X}}^{H}\right)\hfill \end{align*}

where

\mathrm{T}\mathrm{r}\left(\mathbf{A}\right)

is the trace of matrix A .…”

Section: Modified Rap‐so Methodsmentioning

confidence: 99%

“…A defined diagonal matrix D with the pth diagonal entry D pp ¼ P q W pq . Define B ¼ D W as the graph Laplacian matrix [26,29]. Then the Laplacian regularisation is min…”

Section: Laplacian Regularisationmentioning

confidence: 99%

Section: Weighted Nuclear Normmentioning

confidence: 99%

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