Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

Donoho, David L.; Tsaig, Y.; Drori, Iddo; Starck, Jean-Luc

doi:10.1109/tit.2011.2173241

Cited by 1,442 publications

(1,019 citation statements)

References 48 publications

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“…indicates the complexity of the StOMP algorithm according to [7]. N 1 and N 2 denote the number of rows and columns of the sensing matrix respectively.…”

Section: The Proposed Cs-tbd Algorithmmentioning

confidence: 99%

“…The proposed algorithm is different from existing TBD algorithms which adopt hypothesis testing using the radar measurements. The proposed CS-TBD algorithm reconstructs the whole radar scenario (DOA-Doppler plane) for each range gate at consecutive scans using an improved StOMP (Stagewise OMP [7]), resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal tracking in the newly built three-dimensional range-DOA-Doppler space, based on the information from multiple illuminations during each scan, as well as among consecutive scans.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Liu¹,

Han²,

Yao³

et al. 2013

PIER

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Abstract-This paper presents a novel compressed sensing based track before detect (CS-TBD) algorithm. The proposed algorithm reconstructs the whole radar scenario (direction of arrival (DOA)-Doppler plane) for each range gate at consecutive scans using an improved stagewise orthogonal matching pursuit (StOMP) algorithm, resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal tracking in the newly built three-dimensional range-DOA-Doppler space, based on the information from multiple illuminations during each scan, as well as among consecutive scans. In the proposed CS-TBD algorithm, the improved StOMP algorithm together with the temporal tracking, can effectively distinguish true targets from false targets and clutter based on information from multiple illuminations.

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“…indicates the complexity of the StOMP algorithm according to [7]. N 1 and N 2 denote the number of rows and columns of the sensing matrix respectively.…”

Section: The Proposed Cs-tbd Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Liu¹,

Han²,

Yao³

et al. 2013

PIER

View full text Add to dashboard Cite

show abstract

“…Several other greedy algorithms exist that are based on the OMP, such as Compressive Sampling MP (CoSaMP), Stagewise OMP (StOMP) and Probabilistic OMP (PrOMP) [12], [15], [16]. We herein use the OMP algorithm, since it is commonly used as a comparison.…”

Section: A Orthogonal Matching Pursuitmentioning

confidence: 99%

Sparse Multi-User Detection for CDMA transmission using greedy algorithms

Schepker

Dekorsy

2011

2011 8th International Symposium on Wireless Communication Systems

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Abstract-A possible future application in communications is the wireless uplink transmission in sensor networks. This application is mainly characterized by sporadic transmission over a random access channel. Since each sensor has a low activity probability, the signal for Multi-User Detection (MUD) is sparse. Compressive Sensing (CS) theory introduces detectors that are able to recover sparse signals reliably. When applied to MUD, CS detectors perform a joint detection of both data and user activity. This allows for less control signaling, since information about the activity state of each user no longer need to be signaled. Additionally, CS detectors are able to reliably detect sparse signals even in under-determined systems. In the context of transmission with Code Division Multiple Access (CDMA) this property can be exploited by reducing the length of spreading sequences, which increases the symbol-rate for a given bandwidth, and making the CDMA system overloaded. In this paper we introduce the application of greedy CS detection algorithms to detect CDMA spread multi-user data.

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“…Out of these potentials SMR based BCI provides high degrees of freedom in association with real and imaginary movements of hands, arms, feet and tongue [10]. The neural activities associated with SMR based motor imagery (MI) BCI are the so-called mu (7)(8)(9)(10)(11)(12)(13) and beta (13-30 Hz) rhythms [16]. These rhythms are readily measurable in both healthy and disabled people with neuromuscular injuries.…”

mentioning

confidence: 99%

Classification of Motor Imagery Based EEG Signals Using Sparsity Approach

Sreeja

Rabha

Samanta

et al. 2017

Intelligent Human Computer Interaction

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Abstract. The advancement in brain-computer interface systems (BCIs) gives a new hope to people with special needs in restoring their independence. Since, BCIs using motor imagery (MI) rhythms provides high degree of freedom, it is been used for many real-time applications, especially for locked-in people. The available BCIs using MI-based EEG signals usually makes use of spatial filtering and powerful classification methods to attain better accuracy and performance. Inter-subject variability and speed of the classifier is still a issue in MI-based BCIs. To address the aforementioned issues, in this work, we propose a new classification method, spatial filtering based sparsity (SFS) approach for MIbased BCIs. The proposed method makes use of common spatial pattern (CSP) to spatially filter the MI signals. Then frequency bandpower and wavelet features from the spatially filtered signals are used to bulid two different over-complete dictionary matrix. This dictionary matrix helps to overcome the issue of inter-subject variability. Later, sparse representation based classification is carried out to classify the two-class MI signals. We analysed the performance of the proposed approach using publicly available MI dataset IVa from BCI competition III. The proposed SFS method provides better classification accuracy and runtime than the well-known support vector machine (SVM) and logistic regression (LR) classification methods. This SFS method can be further used to develop a real-time application for people with special needs.

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Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

Cited by 1,442 publications

References 48 publications

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Sparse Multi-User Detection for CDMA transmission using greedy algorithms

Classification of Motor Imagery Based EEG Signals Using Sparsity Approach

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