کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6953725 | 1451823 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A highly efficient compressed sensing algorithm for acoustic imaging in low signal-to-noise ratio environments
ترجمه فارسی عنوان
الگوریتم سنجش فشرده بسیار کارآمد برای تصویربرداری صوتی در محیط های نسبت سیگنال به نویز کم
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کلمات کلیدی
سنجش فشرده، آرایه میکروفون، تصویربرداری آکوستیک، تجزیه مقدار منفرد، بسیار کارآمد، 00-01، 99-00،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
We study the acoustic imaging in low signal-to-noise ratio (SNR) environments with compressed sensing (CS) and microphone arrays. In this work, we propose an OMP-SVD method which combines the orthogonal matching pursuit (OMP) method of CS and the singular value decomposition (SVD). The performance of the proposed OMP-SVD method is compared with the CBF method, the OMP method and the l1-SVD method. In terms of the CPU time, the proposed method is highly efficient like the CBF method and the OMP method, and much more efficient than the l1-SVD method. In terms of the accuracy of the source maps, the OMP-SVD method can locate the sources exactly for the SNR as low as â10â¯dB and the frequency as low as 2000â¯Hz, while the other three different methods can only locate the sources when the SNR is greater than or equal to 5â¯dB. In addition, we find that the proposed method can obtain good performance when the target sparsity KT is overestimated and there is basis mismatch. Finally, a gas leakage experiment was conducted to verify the performance of the OMP-SVD method in practical application. The results show that the OMP-SVD method is robust in low SNR environments.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 112, November 2018, Pages 113-128
Journal: Mechanical Systems and Signal Processing - Volume 112, November 2018, Pages 113-128
نویسندگان
Fangli Ning, Feng Pan, Chao Zhang, Yong Liu, Xiaofan Li, Juan Wei,