Flexible Tree-Search Based Orthogonal Matching Pursuit Algorithm

Karabulut, G.Z.; Moura, Luís Mauro; Panario, Daniel; Yongaçoğlu, Abbas

doi:10.1109/icassp.2005.1416098

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The FTB-OMP algorithm (Karabulut, Moura et al, 2005;Karabulut, Moura, Panario, & Yongacog Lu, 2004) is an efficient TB-OMP algorithm with branch pruning by using a correlation threshold parameter. The FTB-OMP algorithm can be summarised as follows: FTB-OMP ðd; p; r; L; ε; Þ Global K ¼ ½k 1 ; k 2 ; .…”

Section: Ftb-omp Algorithmmentioning

confidence: 99%

“…A tree search-based orthogonal matching pursuit (TB-OMP) consider L possible vectors at each interaction instead of a single vector (Cotter & Rao, 2001), and the TB-OMP algorithm improves the approximation performance, but its computation time requirement increases exponentially, making the algorithm impractical for certain applications. To solve this problem, Karabulut, Moura, Panario, and Yongacoglu (2005) proposed a flexible tree search based on orthogonal matching pursuit (FTB-OMP). The objective of the algorithm is to prune the unnecessary branches, that is, only to keep branches among fk ð1Þ p ; k ð2Þ p ; .…”

Section: Introductionmentioning

confidence: 99%

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Double-constraint flexible tree search-based orthogonal matching pursuit for DOA estimation using dynamic sensor arrays

Chen

Wan

Fan

2015

International Journal of Electronics

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Greedy algorithms have leveraged sparse signal models for parameter estimation purposes in applications including bearing estimation and direction-of-arrival (DOA) estimation. A dictionary whose elements correspond to observations for a sampling of the angle space is used for sparse approximation of the received signals; the resulting sparse coefficient vector's support identifies the DOA estimates. Increasing the angle space sampling resolution provides better sparse approximations for arbitrary observations, while the resulting high dictionary coherence hampers the performance of standard sparse approximation, preventing accurate DOA estimation. To alleviate this shortcoming, in the each iteration, we use the structured sparsity model that keeps high coherent and close spacing dictionary elements. Besides, the proposed approach allows exploitation of the array orientation diversity (achievable via array dynamics) in the compressive sensing framework to address challenging array signal processing problems such as left-right ambiguity and poor estimation performance. And the simulation results show that our proposed algorithm can offer significantly improved performance in singlesnapshot scenario with multiple sources. ARTICLE HISTORY

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Section: Ftb-omp Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Double-constraint flexible tree search-based orthogonal matching pursuit for DOA estimation using dynamic sensor arrays

Chen

Wan

Fan

2015

International Journal of Electronics

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“…In this section, we summarize the efficient tree-searchbased OMP algorithms with branch pruning, the flexible tree-search-based OMP (FTB-OMP), that has been recently proposed in [7]. A maximum of L branches are searched at each partial solution.…”

Section: Flexible Tree-search-based Orthogonal Matching Pursuitmentioning

confidence: 99%

“…For estimation of DOA, we consider a high-resolution basis selection algorithm, the flexible tree-search-based orthogonal matching pursuit (FTB-OMP) algorithm that is proposed in [7]. The FTB-OMP algorithm heuristically converges to the maximum likelihood solution.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Directions of Arrival by Matching Pursuit (EDAMP)

Karabulut

Kurt

Yongaçoğlu

2005

J Wireless Com Network

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We propose a novel system architecture that employs a matching pursuit-based basis selection algorithm for directions of arrival estimation. The proposed system does not require a priori knowledge of the number of angles to be resolved and uses very small number of snapshots for convergence. The performance of the algorithm is not affected by correlation in the input signals. The algorithm is compared with well-known directions of arrival estimation methods with different branch-SNR levels, correlation levels, and different angles of arrival separations.

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