199
Views
1
CrossRef citations to date
0
Altmetric
Articles

Faulty elements diagnosis of phased array antennas using a generative adversarial learning-based stacked denoising sparse autoencoder

&
Pages 382-407 | Received 01 Sep 2018, Accepted 21 Nov 2018, Published online: 04 Dec 2018

References

  • Dolph CL. A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level. Proc Ire. 1946;35(6):335–348.
  • Harrington RF. Sidelobe reduction by nonuniform element spacing. Ire Trans Antennas Propag. 1961;9(2):187–192.
  • Kumar BP, Branner GR. Design of unequally spaced arrays for performance improvement. IEEE Trans Antennas Propag. 1999;47(3):511–523.
  • Skolnik MI, Nemhauser G, Sherman JW. Dynamic programming applied to unequally spaced arrays. IEEE Trans Antennas Propag. 1964;12(1):35–43.
  • Balanis CA. Antenna theory: analysis and design. New York City: Harper & Row; 1982.
  • Elliott R. Mechanical and electrical tolerances for two-dimensional scanning antenna arrays. Ire Trans Antennas Propag. 1958;6(1):114–120.
  • Isermann R. Fault-diagnosis systems: an introduction from fault detection to fault tolerance. 2006;28(2):195–197.
  • Migliore MD, Panariello G. A comparison of interferometric methods applied to array diagnosis from near-field data. IEE Proc Microwaves Antennas Propag. 2001;148(4):261–267.
  • Bregains JC, Ares F, Moreno E. Matrix pseudo-inversion technique for diagnostics of planar arrays. Electron Lett. 2006;41(1):1–4.
  • Vakula D, Sarma NVSN. Fault diagnosis of planar antenna arrays using neural networks. Prog Electromagnet Res. 2009;6(2):35–46.
  • Patnaik A, Choudhury B, Pradhan P, et al. An ANN application for fault finding in antenna arrays. IEEE Trans Antennas Propag. 2007;55(3):775–777.
  • Gehani A, Pujara D, editors. Fault diagnosis in planar array antenna using Takagi-Sugeno type neuro-fuzzy model. IEEE Applied Electromagnetics Conference; Guwahati, India; 2015.
  • Lei Y, Jia F, Lin J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Elect. 2016;63(5):3137–3147.
  • Khaleghi B, Khamis A, Karray FO, et al. Multisensor data fusion: a review of the state-of-the-art. Inf Fusion. 2013;14(1):28–44.
  • Serdio F, Lughofer E, Pichler K, et al. Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations. Inf Fusion. 2014;20(1):272–291.
  • Ince T, Kiranyaz S, Eren L, et al. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans Ind Elect. 2016;63(11):7067–7075.
  • Croswell W. Antenna theory, analysis, and design. IEEE Antennas Propag Soc Newsl. 2003;24(6):28–29.
  • Schmid CM, Schuster S, Feger R, et al. On the effects of calibration errors and mutual coupling on the beam pattern of an antenna array. IEEE Trans Antennas Propag. 2013;61(8):4063–4072.
  • Friedlander B, Weiss AJ. Direction finding in the presence of mutual coupling. IEEE Trans Antennas Propag. 1991;39(3):273–284.
  • Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798–1828.
  • Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436.
  • Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks. 2015;61:85–117.
  • Bengio Y. Learning deep architectures for AI. Found Trends® Mach Learn. 2009;2(1):1–127.
  • Tan S, Mayrovouniotis ML. Reducing data dimensionality through optimizing neural network inputs. AICHE J. 1995;41(6):1471–1480.
  • Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Adv Neural Inf Process Syst. 2014;3:2672–2680.
  • Baldi P, editor. Autoencoders, unsupervised learning and deep architectures. International Conference on Unsupervised and Transfer Learning Workshop; Bellevue, Washington; 2011.
  • Erhan D, Bengio Y, Courville A, et al. Why does unsupervised pre-training help deep learning. J Mach Learn Res. 2010;11(3):625–660.
  • Makhzani A, Frey B. k-sparse autoencoders. Computer Science; 2013.
  • Kingma D, Ba J. Adam: a method for stochastic optimization. Computer Science; 2014.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.