Open Access
Issue
JNWPU
Volume 38, Number 1, February 2020
Page(s) 31 - 39
DOI https://doi.org/10.1051/jnwpu/20203810031
Published online 12 May 2020
  1. Nayak P, Devulapalli A. A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime[J]. IEEE Sensors Journal, 2016, 16(1): 137–144 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  2. Tomic S, Beko M, Dinis R. RSS-Based Localization in Wireless Sensor Networks Using Convex Relaxation:Noncooperative and Cooperative Schemes[J]. IEEE Trans on Vehicular Technology, 2015, 64(5): 2037–2050 [Article] [CrossRef] [Google Scholar]
  3. TOMIC S, Beko M, DINIS R. Distributed RSS-AoA Based Localization with Unknown Transmit Powers[J]. IEEE Wireless Communications Letters, 2016, 5(4): 392–395 [Article] [CrossRef] [Google Scholar]
  4. Liu Y, Guo F, Yang L, et al. An Improved Algebraic Solution for TDOA Localization with Sensor Position Errors[J]. IEEE Communications Letters, 2015, 19(12): 2218–2221 [Article] [CrossRef] [Google Scholar]
  5. Eldar Y C, Kutyniok G. Compressed Sensing:Theory and Applications[M]. Cambridgeshire: Cambridge University Press, 2012 [CrossRef] [Google Scholar]
  6. Liu L, CUI T, LV W. A Range-Free Multiple Target Localization Algorithm Using Compressive Sensing Theory in Wireless Sensor Networks[C]//2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, 2014: 690–695 [Google Scholar]
  7. Wang J, CHEN X, Fang D, et al. Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity[J]. IEEE Trans on Industrial Electronics, 2015, 62(4): 2397–2409 [Article] [CrossRef] [Google Scholar]
  8. Xie R, Jia X. Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing[J]. IEEE Trans on Parallel and Distributed Systems, 2014, 25(3): 806–815 [Article] [CrossRef] [Google Scholar]
  9. Lagunas E, SHARMA S K, CHATZINOTAS S, et al. Compressive Sensing Based Target Counting and Localization Exploiting Joint Sparsity[C]//Proc IEEE Int Conf Acoust, Speech Signal Process, 2016: 3231–3235 [Google Scholar]
  10. Tao Q, Jinxun W, Yan Y. Matching Pursuits Among Shifted Cauchy Kernels in Higher-Dimensional Spaces[J]. Acta Mathematica Scientia, 2014, 34(3): 660–672 [Article] [CrossRef] [Google Scholar]
  11. You C, ROBINSON D, VIDAL R. Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 3918–3927 [Google Scholar]
  12. Tropp J A, Gilbert A C. Signal Recovery from Random Measurements via Orthogonal Matching Pursuit[J]. IEEE Trans on Information Theory, 2007, 53(12): 4655–4666 [Article] [CrossRef] [Google Scholar]
  13. Davenport M A, Wakin M B. Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property[J]. IEEE Trans on Information Theory, 2010, 56(9): 4395–4401 [Article] [CrossRef] [Google Scholar]
  14. Sajjad M, Mehmood I, Baik S W. Sparse Coded Image Super-Resolution Using K-Svd Trained Dictionary Based on Regularized Orthogonal Matching Pursuit[J]. Bio-Medical Materials and Engineering, 2015, 26(suppl 1): S1399–S1407 [Article] [CrossRef] [Google Scholar]
  15. Lee D. MIMO OFDM Channel Estimation via Block Stagewise Orthogonal Matching Pursuit[J]. IEEE Communications Letters, 2016, 20(10): 2115–2118 [Article] [CrossRef] [Google Scholar]
  16. Wu F, YANG K, Sun Q, et al. Stage-Wise Orthogonal Matching Pursuit for Estimation of Time Delay and Doppler Doubly Spreading of Underwater Acoustic Channels[J]. The Journal of the Acoustical Society of America, 2018, 144(3): 1736–1736 [Article] [NASA ADS] [Google Scholar]
  17. Kaur A, Kumar P, Gupta G P. A Weighted Centroid Localization Algorithm for Randomly Deployed Wireless Sensor Networks[J]. Journal of King Saud University-Computer and Information Sciences, 2019, 31(1): 82–91 [Article] [CrossRef] [Google Scholar]
  18. Sturm B L, CHRISTENSEN M G. Comparison of Orthogonal Matching Pursuit Implementations[C]//2012 Proceedings of the 20th European Signal Processing Conference, 2012: 220–224 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.