Iterative MMSE method and recurrent Kalman procedure for ISAR image reconstruction

Lazarov, Andon

doi:10.1109/7.976978

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…It is also widely accepted that the problem in (15) is also equivalent to the l 1 -constrained optimization in CS [48]; however, BSR imaging has evident differences with them. Compared to the pointenhanced algorithm [26], the l 1 -norm weighting coefficient in (15) is explicitly associated with the noise and target statistics in a Bayesian sense. Furthermore, different from the CS-based SR imaging in [29], BSR is closely connected to convex quadratic programming [49] making it very efficient and accurate.…”

Section: A Signal Modelmentioning

confidence: 99%

“…where s m = S(:, m) denotes the mth column of S corresponding to a m . In contrast to BSR in (15), the image is modeled by the nonidentical Laplace distribution in (20). We designate it improved BSR (IBSR).…”

Section: Improved Bayesian Sr (Ibsr) Imaging Based On Nonidenticalmentioning

confidence: 99%

“…Probabilistic strategies which model the signal and noise statistically, including maximum a posteriori and maximum entropy [11,12] schemes have also been developed, yielding good performance. Adaptive spectral estimators [13][14][15][16][17][18] utilize sinusoid parametric model to estimate the coefficients of signals by finding a set of filters adaptively. Among this sort, RELAX [13], [14], which estimates the amplitude and frequency of a multicomponent complex sinusoidal signal by minimizing the residual error energy, is one of the most popular algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Resolution enhancement for ISAR imaging via improved statistical compressive sensing

Zhang

Wang

Qiao

2016

EURASIP J. Adv. Signal Process.

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Developing compressed sensing (CS) theory reveals that optimal reconstruction of an unknown signal can be achieved from very limited observations by utilizing signal sparsity. For inverse synthetic aperture radar (ISAR), the image of an interesting target is generally constructed by limited strong scattering centers, representing strong spatial sparsity. Such prior sparsity intrinsically paves a way to improved ISAR imaging performance. In this paper, we develop a super-resolution algorithm for forming ISAR images from limited observations. When the amplitude of the target scattered field follows an identical Laplace probability distribution, the approach converts super-resolution imaging into sparsity-driven optimization in the Bayesian statistics sense. We show that improved performance is achievable by taking advantage of the meaningful spatial structure of the scattered field. Further, we use the nonidentical Laplace distribution with small scale on strong signal components and large scale on noise to discriminate strong scattering centers from noise. A maximum likelihood estimator combined with a bandwidth extrapolation technique is also developed to estimate the scale parameters. Real measured data processing indicates the proposal can reconstruct the high-resolution image though only limited pulses even with low SNR, which shows advantages over current super-resolution imaging methods.

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Section: A Signal Modelmentioning

confidence: 99%

Section: Improved Bayesian Sr (Ibsr) Imaging Based On Nonidenticalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resolution enhancement for ISAR imaging via improved statistical compressive sensing

Zhang

Wang

Qiao

2016

EURASIP J. Adv. Signal Process.

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“…improving the ISAR image quality with less data. Some modern spectral estimation methods are applied in [2][3][4] to improve the resolution and suppress the sidelobes, such as amplitude and phase estimation (APES) [2]. However, these algorithms require that the ISAR signal should be approximately stationary, i.e., the MTRC of the scatterers should be negligible and the ISAR echo can be modulated by sinusoids during the observation duration.…”

Section: Introductionmentioning

confidence: 99%

Comparison of parametric sparse recovery methods for ISAR image formation

Rao

Wang

et al. 2014

Sci. China Inf. Sci.

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“…Many super resolution approaches [1][2][3][4][5][6] can be sorted into this class. To ensure success of these applications, sufficient resolution is usually required to distinguish prominent portions of target.…”

Section: Introductionmentioning

confidence: 99%

Generating dense and super-resolution ISAR image by combining bandwidth extrapolation and compressive sensing

Quan

Zhang

Guo

et al. 2011

Sci. China Inf. Sci.

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Recent developing compressive sensing (CS) theory indicates that it is possible to obtain precise recovery of a sparse signal from very limited measurements, which provides a new way for data acquisition and signal processing as nature signals usually involve some degree of sparsity. In this paper, we present an algorithm for inversed synthetic aperture radar (ISAR) imaging with super resolution by combining CS and bandwidth extrapolation (BWE) technique. For ISAR imaging, the backscattering field of target is usually contributed by a few strong scattering centers, whose number is much less than that of image pixels. Thus, CS is intuitively suitable for constructing super resolution ISAR image. According to CS theory, the number of extracted dominating scatterers relies on the signal length, which indicates that if only limited data is available, it is difficult to generate dense ISAR image robustly by CS, and some signal components tend to lose. To soften this constraint, BWE is combined with CS imaging to increase the degree of freedom of signal while preserving its coherence. A refined CS-based formation for ISAR image-resolution enhancement is then developed. Both real and simulated data experiments are performed to evaluate the proposed approach, and an example of using this technique demonstrates the enhanced image resolution in application of maneuvering target imaging.

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Iterative MMSE method and recurrent Kalman procedure for ISAR image reconstruction

Cited by 18 publications

References 19 publications

Resolution enhancement for ISAR imaging via improved statistical compressive sensing

Resolution enhancement for ISAR imaging via improved statistical compressive sensing

Comparison of parametric sparse recovery methods for ISAR image formation

Generating dense and super-resolution ISAR image by combining bandwidth extrapolation and compressive sensing

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