Waveform Design for Compressive Sensing Radar Based on Minimizing the Statistical Coherence of the Sensing Matrix

He, Yapeng; Zhuang, Shanna; Li, Hongtao; Zhu, Xiaohua

doi:10.3724/sp.j.1146.2011.00021

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…We consider the target scene with five targets ð4Dr; 7Df d Þ; ð10Dr; 6Df d Þ; ð14Dr; 15Df d Þ; ð5Dr; 13Df d Þ and ð19Dr; 12Df d Þ where Dr ¼ DR=N r and Df d ¼ DF d =N d . That is to say, these targets are located at (4, 7), (10,6), (14,15), (5,13) and (19,12), respectively.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Though cubic sequence of prime number samples leads to nearly ideal incoherent dictionary, the incoherence degenerates rapidly when the sample number is not a prime [12]. He [13,14] investigated the transmission waveform optimization by minimizing the statistical coherence of sensing matrix. Considering algorithms (genetic algorithm, GA and simulated annealing, SA) utilized to solve the object function, these proposed methods were not computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

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Sensing Matrix Restriction Method for Compressed Sensing Radar

Lin

Liang

2015

Wireless Pers Commun

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To improve the robustness of target scene extraction for compressed sensing radar, an sensing matrix orthogonal restriction method is proposed through solving the matrix nearness problem between the complex Gram matrix and the target matrix. First, we introduce a target matrix maintaining the character of any subset in sensing matrix and minimizing mutual-coherence of sensing matrix. Then least square method and properties of Kronecker product and vector operator are employed to solve the matrix nearness problem with fixed transmission waveform or measurement matrix. The restricted sensing matrix can be achieved through optimizing the transmission waveform and measurement matrix separately or simultaneously. Our approach can effectively reduce the sensing matrix mutual-coherence and improve the performance of target scene recovery. Lastly, numerical simulations are performed to illustrate the priority of the proposed restriction method.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensing Matrix Restriction Method for Compressed Sensing Radar

Lin

Liang

2015

Wireless Pers Commun

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“…Another strength of Yuzu is that improvements in compressed sensing theory can be directly applied to improve its speed even further. For instance, in some contexts one can design a sensing matrix in an informative way instead of using random values [27,28,29,30], potentially requiring far fewer probes to be constructed to achieve perfect reconstruction. Because a large portion of time is spent applying convolution operations to the constructed probes, decreasing the number of probes is a straightforward way to speed up Yuzu further.…”

Section: Discussionmentioning

confidence: 99%

Acceleratingin silicosaturation mutagenesis using compressed sensing

et al. 2022

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Motivation In silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined. Results In this work, we propose a modification of ISM that leverages the principles of compressed sensing to require only a constant number of forward passes, regardless of sequence length, when applied to models that contain operations with a limited receptive field, such as convolutions. Our method, named Yuzu, can reduce the time that ISM spends in convolution operations by several orders of magnitude and, consequently, Yuzu can speed up ISM on several commonly used architectures in genomics by over an order of magnitude. Notably, we found that Yuzu provides speedups that increase with the complexity of the convolution operation and the length of the sequence being analyzed, suggesting that Yuzu provides large benefits in realistic settings. Availability and implementation We have made this tool available at https://github.com/kundajelab/yuzu.

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