Sparse aperture bistatic ISAR imaging under low signal-to-noise ratio condition

Chen, Wenfeng; Lv, Mingjiu; Yang, Jun; Xiao-yan, MA

doi:10.1117/1.jrs.14.036515

Cited by 5 publications

(2 citation statements)

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“…Therefore, it is challenging to separate the last two nearby targets because the distance between them is less than a frequency resolution cell. For the purpose of comparison, the CCS‐based methods, matrix smoothed L0‐norm (MSL0) [34] and adaptive matching pursuit (AMP) [17] and the TLS‐ESPRIT method [15] are also applied. The simulation conditions are the same as those for Simulation 1, and the reconstruction results obtained by these six methods are depicted in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…If the original effective aperture length is insufficient, the estimation accuracy will be affected by the sparse aperture. In recent years, compressive sensing (CS) theory has been introduced in sparse-aperture ISAR imaging and is proven effective in acquiring a superresolution image although the measurements are only partly available [16][17][18]. Based on the conventional CS theory, the true scattering points are always supposed to fall onto the discrete grids by discretising a continuous scene.…”

Section: Introductionmentioning

confidence: 99%

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Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

Lv¹,

Ma²,

Ma³

et al. 2022

IET Signal Processing

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Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%