الگوریتم و محدوده اندازه گیری
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الگوریتم و محدوده اندازه گیری

عنوان فارسی مقاله: بازیابی پراکنده سیگنال با اطلاعات متعدد پیشین: الگوریتم و محدوده اندازه گیری
عنوان انگلیسی مقاله: Sparse signal recovery with multiple prior information: Algorithm and measurement bounds
مجله/کنفرانس: پردازش سیگنال - Signal Processing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، هوش مصنوعی، مهندسی نرم افزار
کلمات کلیدی فارسی: سنجش فشرده، اطلاعات پیشین، کمینه سازی n-1 تعدیل شده، محدوده اندازه گیری
کلمات کلیدی انگلیسی: Compressed sensing، prior information، weighted n-`1 minimization، measurement bounds
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.sigpro.2018.06.019
دانشگاه: Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, Erlangen 91058, Germany
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 3/933 در سال 2017
شاخص H_index: 105 در سال 2019
شاخص SJR: 0/940 در سال 2017
شناسه ISSN: 0165-1684
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 32
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E11148
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Background


3- Recovery with multiple prior information


4- Bounds for weighted n-ℓ1 minimization


5- Experimental results


6- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


We address the problem of reconstructing a sparse signal from compressive measurements with the aid of multiple known correlated signals. We propose a reconstruction algorithm with multiple side information signals (RAMSI), which solves an n-`1 minimization problem by weighting adaptively the multiple side information signals at every iteration. In addition, we establish theoretical bounds on the number of measurements required to guarantee successful reconstruction of the sparse signal via weighted n-`1 minimization. The analysis of the derived bounds reveals that weighted n-`1 minimization can achieve sharper bounds and significant performance improvements compared to classical compressed sensing (CS). We evaluate experimentally the proposed RAMSI algorithm and the established bounds using numerical sparse signals. The results show that the proposed algorithm outperforms state-of-the-art algorithms-including classical CS, `1-`1 minimization, Modified-CS, regularized Modified-CS, and weighted `1 minimization-in terms of both the theoretical bounds and the practical performance.


Introduction


Compressed sensing (CS) [1–15] states that sparse signals can be recovered in a computationally tractable manner from a limited set of measurements by minimizing the `1-norm. The CS performance can be improved by replacing the `1-norm with a weighted `1-norm [8, 9, 16–18]. The studies in [11, 12] provide bounds on the number of measurements required for successful signal recovery based on convex optimization. Furthermore, distributed compressed sensing [13, 14] allows a correlated ensemble of sparse signals to be jointly recovered by exploiting the intra- and inter-signal correlations. We consider the problem of reconstructing a signal given side or prior information, gleaned from a set of known correlated signals. Initially, this problem was studied in [16, 19–28], where the modified CS method [19, 21] considered that a part of the support is available from prior knowledge and tried to find the signal that satisfies the measurement constraint and is sparsest outside the known support. Prior information on the sparsity pattern of the data was also considered in [23] and informationtheoretic guarantees were presented. The studies in [24, 25] introduced weights into the `1 minimization framework that depend on the partitioning of the source signal into two sets, with the entries of each set having a specific probability of being nonzero.

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