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OMP-based DOA estimation performance analysis
Qualcomm Technol Inc, CA USA.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7765-1747
2018 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 79, p. 57-65Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a new performance guarantee for Orthogonal Matching Pursuit (OMP) in the context of the Direction Of Arrival (DOA) estimation problem. For the first time, the effect of parameters such as sensor array configuration, as well as signal to noise ratio and dynamic range of the sources is thoroughly analyzed. In particular, we formulate a lower bound for the probability of detection and an upper bound for the estimation error. The proposed performance guarantee is further developed to include the estimation error as a user-defined parameter for the probability of detection. Numerical results show acceptable correlation between theoretical and empirical simulations. (C) 2018 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
ACADEMIC PRESS INC ELSEVIER SCIENCE , 2018. Vol. 79, p. 57-65
Keywords [en]
Direction of arrival; Orthogonal Matching Pursuit (OMP); Mutual coherence; Array configuration
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-149841DOI: 10.1016/j.dsp.2018.04.006ISI: 000437386200006OAI: oai:DiVA.org:liu-149841DiVA, id: diva2:1236456
Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2018-11-23
In thesis
1. Sparse representation of visual data for compression and compressed sensing
Open this publication in new window or tab >>Sparse representation of visual data for compression and compressed sensing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more. A key challenge inherent to such imaging techniques is the large amount of high dimensional visual data that is produced, often requiring GBs, or even TBs, of storage. Moreover, the utilization of these datasets in real time applications poses many difficulties due to the large memory footprint. Furthermore, the acquisition of large-scale visual data is very challenging and expensive in most cases. This thesis makes several contributions with regards to acquisition, compression, and real time rendering of high dimensional visual data in computer graphics and imaging applications.

Contributions of this thesis reside on the strong foundation of sparse representations. Numerous applications are presented that utilize sparse representations for compression and compressed sensing of visual data. Specifically, we present a single sensor light field camera design, a compressive rendering method, a real time precomputed photorealistic rendering technique, light field (video) compression and real time rendering, compressive BRDF capture, and more. Another key contribution of this thesis is a general framework for compression and compressed sensing of visual data, regardless of the dimensionality. As a result, any type of discrete visual data with arbitrary dimensionality can be captured, compressed, and rendered in real time.

This thesis makes two theoretical contributions. In particular, uniqueness conditions for recovering a sparse signal under an ensemble of multidimensional dictionaries is presented. The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data. Moreover, we derive the probability of successful recovery of a noisy sparse signal using OMP, one of the most widely used algorithms for solving compressed sensing problems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 158
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1963
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-152863 (URN)10.3384/diss.diva-152863 (DOI)9789176851869 (ISBN)
Public defence
2018-12-14, Domteatern, Visualiseringscenter C, Kungsgatan 54, Campus Norrköping, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2018-11-23Bibliographically approved

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Output format
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