Inverse synthetic aperture radar imaging based on sparse signal processing

Zou, Fei; Li, Xiang; Togneri, Roberto

doi:10.1007/s11771-011-0879-z

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…: reconstruct the clean speech magnitude spectrum with (15), (16) and (17). 7: obtain the reconstructed speech signal x in time domain via inverse discrete Fourier transformation with the reconstructed magnitude and the noisy speech phases ζ 1 , …, ζ M .…”

Section: Sparse Reconstruction Based On Adapted Srementioning

confidence: 99%

“…Recently, sparse representation has attracted much attention and is widely used in compressed sensing [15], image [16] and audio signal processing [17][18][19][20].The objective of sparse representation is to represent the most information of a signal with a linear combination of only a small number of atoms. Recent results have indicated that many signals, including the speech signal, can be approximated sparsely [17], which provides a new avenue for the quasi-clean speech construction.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, sparse representation has attracted much attention and is widely used in compressed sensing [15], image [16] and audio signal processing [17–20]. The objective of sparse representation is to represent the most information of a signal with a linear combination of only a small number of atoms.…”

Section: Introductionmentioning

confidence: 99%

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Sparse representation‐based quasi‐clean speech construction for speech quality assessment under complex environments

Zhou

Wang

et al. 2017

IET signal process.

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A non-intrusive speech quality assessment method for complex environments was proposed. In the proposed approach, a new sparse representation-based speech reconstruction algorithm was presented to acquire the quasi-clean speech from the noisy degraded signal. Firstly, an over-complete dictionary of the clean speech power spectrum was learned by the K-singular value decomposition algorithm. Then in the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the noise spectrum which was adjusted by a posteriori SNR-weighted factor, and the orthogonal matching pursuit approach was applied to reconstruct the clean speech spectrum from the noisy speech. The quasi-clean speech was considered as the reference to a modified PESQ perceptual model, and the mean opinion score of the noisy degraded speech was achieved via the distortions estimation between the quasi-clean speech and the degraded speech. Experimental results show that the proposed approach obtains a correlation coefficient of 0.925 on NOIZEUS complex environment database, which is 99% similar to the performance of the intrusive standard ITU-T PESQ, and 7.1% outperforms non-intrusive standard ITU-T P.563.

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Section: Sparse Reconstruction Based On Adapted Srementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparse representation‐based quasi‐clean speech construction for speech quality assessment under complex environments

Zhou

Wang

et al. 2017

IET signal process.

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“…The physical features include physical size and scattering structure which can only be obtained by wideband and polarization radar. The physical size can be extracted from high resolution range profile (HRRP) [3], inverse synthetic aperture radar (ISAR) image [4] and three-dimensional image [5], while the HRRP is the easiest one.…”

Section: Introductionmentioning

confidence: 99%

Length Estimation of Ballistic Targets Based on Full-Polarization Range Profiles

Li¹,

Ai²

2016

PIER M

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This study focuses on the length estimation of ballistic targets based on the full-polarization range profiles measured by the wideband full-polarization radar system. Firstly, the mathematical model of full-polarization range profiles is introduced, and the full-polarization range profiles characteristics of typical ballistic targets are analyzed by using the microwave anechoic chamber measurement data. Secondly, three methods are proposed for target length estimation based on single-channel detection synthesis, SPAN power synthesis and target characteristic polarization, respectively. Then, comparison experiments among the proposed methods are carried out. The results demonstrate that the extraction accuracy and the anti-noise performance of the method based on target characteristic polarization are better than the others. Furthermore, the influence of the signal-to-noise ratio (SNR) on the length estimation is also discussed.

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Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

Wang

Yang

2014

J. Cent. South Univ.

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Inverse synthetic aperture radar imaging based on sparse signal processing

Cited by 4 publications

References 9 publications

Sparse representation‐based quasi‐clean speech construction for speech quality assessment under complex environments

Sparse representation‐based quasi‐clean speech construction for speech quality assessment under complex environments

Length Estimation of Ballistic Targets Based on Full-Polarization Range Profiles

Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

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