The effects of input signal-to-noise ratio on compressive sensing SAR imaging

Tian, Jin; Sun, Jinping; Zhang, Yuxi; Ahmad, Najeeb; Xiaoyang, Su

doi:10.1109/icsps.2010.5555816

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…According to Figure 3 (b)∼(e); same as the comparison methods under correct preset parameters; the FBCS-RVM algorithm can estimate the scattering coefficients accurately and obtain high-quality imaging results. To evaluate the performance of the above four algorithms in more detail; we have conducted point-target simulations under different sampling rates and SNR [23]. The Monte Carlo experiments of the above algorithms are all set hundreds of times to evaluate their performance more accurately.…”

Section: Results On the 2d Point-target Simulation Datamentioning

confidence: 99%

“…Then, the target-areas are used as the prior information to simplify the measurement matrix and conduct sparse imaging. Under several imaging conditions (e.g., low signal to noise ratio (SNR) [23], low sampling rate, or high sparsity of the imaging scene), the target-areas cannot be always extracted accurately. They probably contain several elements whose scattering coefficients are too small or closer to 0 compared to other elements.…”

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confidence: 99%

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Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

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Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

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Section: Results On the 2d Point-target Simulation Datamentioning

confidence: 99%

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confidence: 99%

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

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“…CS is a sampling paradigm that allows to simultaneously measure and compress signals that are sparse or compressible in some domains. Consider a one-dimensional signal x, which can be viewed as an N×1 column vector with element x[n], n=1, 2, …, N. x can be expressed as [28]- [30]:…”

Section: Principlementioning

confidence: 99%

Accelerated Fast BOTDA Assisted by Compressed Sensing and Image Denoising

et al. 2021

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We propose and experimentally demonstrate a scheme for accelerated fast BOTDA. The effect of signalto-noise ratio (SNR) on recovery performance of compressed sensing is simulated and analyzed, it is found that a reduction in SNR requires much larger frequency data to recover the original Brillouin gain spectrum (BGS). To enable a high recovery probability, Block-Matching and 3D filtering (BM3D) algorithm is employed to enhance the SNR of Brillouin time trace and reduce the number of averages. Combining with a principal component analysis (PCA) based compressed sensing technique, the Brillouin gain spectrum (BGS) can be successfully reconstructed from only 37.5% frequency data. In the experiment, 75 randomly selected frequency data is acquired to reconstruct the BGS. Distributed strain sensing is achieved over 15 km single-mode fiber with 3 m spatial resolution and 0.52 MHz Brillouin frequency shift (BFS) uncertainty. Due to the accelerated process, the measurement time with 40 averages is less than 0.5 s.

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“…Since the radar echo and matrices in the above equation are complex valued, in contrast with the original CS environment, we transform them to real variables [11] S =Ãσ (10) …”

Section: Signal Model and Imagingmentioning

confidence: 99%

Optimal MIMO array for compressive sensing image formation

Dai

Zhou

Liang

et al. 2014

Journal of Electromagnetic Waves and Applications

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Many studies have addressed compressive sensing (CS) theory in recent years, especially the use of CS in radar 2D and 3D image formation. Much of this research suggests that sparse targets can be perfectly reconstructed according to the CS theory. However, most of these studies did not examine the design of antenna array for CS reconstruction of sparse targets. Therefore, this study investigates the design method of the Multipleinput/multiple-output (MIMO) antenna array for reconstructing sparse targets. Based on CS theory, mutual coherence of the dictionary matrix was selected as the criteria for evaluating the MIMO antenna array. The analytical expression of the criteria was derived from the MIMO echo signal. Due to the complex mathematical expression of the criteria, the genetic algorithm was used to find the optimal MIMO antenna array. Finally, a comparative simulation and a Monte-Carlo experiment were performed to validate the proposed optimal solution.

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The effects of input signal-to-noise ratio on compressive sensing SAR imaging

Cited by 8 publications

References 9 publications

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Accelerated Fast BOTDA Assisted by Compressed Sensing and Image Denoising

Optimal MIMO array for compressive sensing image formation

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