Fault Detection in Antenna Arrays

Balamati Choudhury; Rakesh Mohan Jha

doi:10.1017/CBO9781316402924.008

7 - Fault Detection in Antenna Arrays

Published online by Cambridge University Press: 05 July 2016

Balamati Choudhury and

Rakesh Mohan Jha

Show author details

Balamati Choudhury: Affiliation:
National Aerospace Laboratories
Rakesh Mohan Jha: Affiliation:
National Aerospace Laboratories

Book contents

Get access

Summary

Modern day communication systems and other microwave systems like sonar, radar, etc., use antenna arrays for signal acquisition. Arrays allow the designer to attain highly directional patterns that can be steered in the required directions. These arrays are typically composed of a large number of elements. Due to the presence of this large number of elements, the probability of experiencing faults in some of the elements is very high. Consequently, diagnosis of faults in a large array is a problem that antenna engineers need to tackle often. These faults—elements that do not contribute to the radiation pattern—damage the pattern by increasing the sidelobe levels. Traditionally, engineers conduct measurements in the near field of the antenna in order to pinpoint the location of these faults [Lee et al., 1988; Migliore and Panariello, 2001; Bregains et al., 2005]. This technique is not feasible if the antenna is mounted on a remote system like a spacecraft and human access to the system is impossible [Lord et al., 1992]. Nonetheless, spacecraft antenna arrays either use test couplers or other calibration probes in the beam forming network, to detect failed elements and send the information to the ground using telemetry. These calibration probe based networks are very much complex and expensive [Bucci et al., 2000]. This brings about a need to devise methods to detect faults in antenna arrays by studying the far-field radiation pattern. In this chapter, soft computing is used to meet this objective, i.e., to detect faults in antenna arrays by studying the information obtained from its far-field radiation pattern.

Preliminaries and Overview

Failure of individual elements in an antenna array results in destruction of symmetry and often causes unacceptable distortion of the radiation pattern. Locating the defective elements in large arrays is a problem that is often described using the theory of inverse scattering. A practical solution to this problem may be obtained by installing sensors in the beam forming network. However, such a sensor network is often expensive and might be susceptible to the same faults that the elements succumb to [Bucci et al., 2000]. A simpler solution, therefore, is to study the far-field pattern of the antenna array and predict the location of these failures.

Type: Chapter
Information: Soft Computing in Electromagnetics
Methods and Applications
, pp. 124 - 154

DOI: https://doi.org/10.1017/CBO9781316402924.008 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acharya,, O. P., A., Patnaik, and S. N., Sinha, “Null steering in failed antenna array,” Applied Computational Intelligence and Soft Computing (Hindawi), vol. 2011, Article ID 692197, doi: 10.1155/2011/692197., 2011.CrossRef

Acharya,, O. P., A., Patnaik, and S. N., Sinha, “Limits of compensation in a failed antenna array,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 24, no. 6, pp. 635–645, Nov. 2014.

Anderson,, J. A. and E., Rosnefeld (eds.), Neurocomputing: Foundation of Research, Cambridge, MA, MIT Press, ISBN: 9780262510486, 752p., 1988.

Balanis,, C. A., Antenna Theory: Analysis and Design, John Wiley and Sons, Inc., ISBN: 9780471714613, 1136p., 2005.

Biswas,, A., S., Dasgupta, S., Das, and A., Abraham, “Synergy of PSO and bacterial foraging optimization – A comparative study on numerical benchmarks”, Innovations in Hybrid Intelligent Systems (Springer-Verlag), vol. ASC 44, pp. 255–263, 2007.Google Scholar

Bregains,, J. C., F., Ares, and E., Moreno, “Matrix pseudo-inversion technique for diagnostics of planar arrays,” Electronics Letters, vol. 41, no. 1, pp. 7–8, 6th Jan. 2005.Google Scholar

Bucci,, O. M., A., Capozzoli, and G., D'elia, “Diagnosis of array faults from far-field amplitudeonly data,” IEEE Transactions on Antennas and Propagation, vol. 48, no. 5, pp. 647–652, 2000.Google Scholar

Chakrabarty,, A., B. N., Das, and A., Bhattacharya, “Detection of localized array fault from near field data”, Antenna and Propagation Society International Symposium Digest, vol. 3, pp. 1408–1411, June 1991.Google Scholar

Choudhury,, B., O. P., Acharya, and A., Patnaik, “A PSO application for locating defective elements in antenna arrays,” World Congress on Natural and Biologically Inspired Computing, pp. 1094–1098, Bhubaneswar, 2009.

Choudhury,, B., O. P., Acharya, and A., Patnaik, “Bacteria foraging optimization in antenna engineering: An application to array fault finding”, International Journal of RF and Microwave Computer-Aided Engineering, vol. 23, no. 2, pp. 141–148, Mar. 2013.Google Scholar

Choudhury,, B., O. P., Acharya, and A., Patnaik, “Fault finding in antenna arrays using bacteria foraging optimization technique”, National Conference on Communications (NCC 2011), pp. 1–5, Banglore, 2011.

Christodoulou,, C. G. and M., Georgiopoulous, Application of Neural Networks in Electromagnetics, Nowrood, MA, Artech House, ISBN: 9780890068809, 512p., 2000.

Datta,, T. and I. S., Misra, “A comparative study of optimization techniques in adaptive antenna array processing: The bacteria foraging algorithm and particle swarm optimization”, IEEE Antennas and Propagation Magazine, vol. 51, no. 6, pp. 69–79, Dec. 2009.Google Scholar

Datta,, T., I. S., Misra, B. B., Mangaraj, and S. K., Imtiaj, “Improved adaptive array for faster convergence,” Progress in Electromagnetic Research C, vol. 1, pp. 143–157, 2008.Google Scholar

Gattoufi,, L., D., Picard, A., Rekiouak, and J. Ch., Bolomey, “Matrix method for near-field diagnostic techniques of phased arrays”, IEEE International Symposium on Phased Array Systems and Technology Digest, pp. 52–57, 1996.Google Scholar

Gattoufi,, L., D., Picard, Y., Rahmat Samii, and J. Ch., Bolomey, “Regularized matrix method for near-field diagnostic techniques of phased array antennas,” IEEE Antennas and Prop. Soc. Int. Symp. Digest, vol. 2, pp. 1066–1069, 1997.Google Scholar

Haykins,, S., Neural Networks: A Comprehensive Foundation, New York: IEEE Press/ IEEE Computer Society Press, ISBN: 9780139083853, 842p., 1994.

Holland,, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Harbor, 1975.

Hornik,, K., M., Stichcombe, and H., White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.Google Scholar

Jang,, J. S. R., C. T., Sun, and E., Mizutani, Neuro-Fuzzy and Soft Computing: A computational Approach to Learning and Machine Intelligence, Prentice Hall, NJ, ISBN: 9780132610667, 614p., 1997.

Kennedy,, J., and R., Eberhart, “Particle swarm optimization,” Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.Google Scholar

Lee,, J. J., E. M., Ferren, D., Pat Woollen, and K. M., Lee, “Near-field probe used as a diagnostic tool to locate defective elements in an antenna array,” IEEE Transactions on Antennas and Propagation, vol. 36, no. 6, pp. 884–890, June 1988.Google Scholar

Levitas,, M., D. A., Horton, and T.C., Cheston, “Practical failure compensation in active phased arrays,” IEEE Transactions on Antennas and Propagation, vol. 47, no. 3, pp. 524–535, 1999.Google Scholar

Lord,, J. A., G. G., Cook, and A. P., Anderson, “Reconstruction of the excitation of an array antenna from measured near-field intensity using phase retrieval,” in Proceedings of Institute of Electrical Engineers, vol. 139, pp. 392–396. 1992.Google Scholar

Mailloux,, R. J., “Array failure correction with a digitally beamformed array,” IEEE Transactions on Antennas and Propagation, vol. 44, pp. 1542–1550, 1996.Google Scholar

Mailloux,, R. J., Phased Array Antennas Handbook, Norwood, MA: Artech House, ISBN: 9781580536899, 496p., 1994.

Migliore,, M. D. and G., Panariello, “A comparison of interferometric methods applied to array diagnosis from near-field data,” IEE Proceedings – Microwave Antennas Propagation, vol. 148, no. 4, pp. 261–267, Aug. 2001.Google Scholar

Mishra,, R. K., “An overview of neural network methods in computational electromagnetics,” International Journal of RF and Microwave Computer Aided Engineering, vol. 12, no. 1, pp. 98–108, 2002.Google Scholar

Mishra,, R. K. and A., Patnaik “Designing rectangular patch antenna using the neurospectral method,” IEEE Transactions on Antennas ' Propagation, vol. 51, no. 8, pp., 1914–1921, Aug. 2003.Google Scholar

Panikhom,, S., N., Sarasiri, and S., Sujitjorn, “Hybrid bacteria foraging and tabu search optimization (BTSO) algorithms for Lyapunov's stability analysis of non-linear systems,” International Journal of Mathematics and Computers in Simulation, vol. 4, issue 3, pp. 81–89, 2010.Google Scholar

Passino,, K. M., “Biomimicry of bacteria foraging for distributed optimization and control,” IEEE Control Systems Magazine, vol. 22, pp. 52–67, 2002.Google Scholar

Patnaik,, A., and R. K., Mishra, “ANN techniques in microwave engineering,” IEEE Microwave Magazine, vol. 1, no. 1, pp. 55–60, 2000.Google Scholar

Patnaik,, A., B., Choudhury, P., Pradhan, R. K., Mishra, C., Christodoulou, “An ANN application for fault finding in antenna arrays,” IEEE Transactions on Antennas and Propagation, vol. 55, no. 3, pp. 775–777, Mar. 2007.Google Scholar

Patnaik,, A., D., Anagnostou, C. G., Christodoulou, and J. C., Lyke, “Neurocomputational analysis of a multiband reconfigurable planar antenna,” IEEE Transactions on Antennas and Propagation, vol. 53, no.11, pp. 3453–3458, Nov. 2005.Google Scholar

Peters,, T. J., “A conjugate gradient-based algorithm to minimize the sidelobe level of planar arrays with element failures,” IEEE Transactions on Antennas and Propagation, vol. 39, pp.1497–1503, 1991. Google Scholar

Proakis,, J. G., and M., Salehi, Digital Communications, McGraw-Hill International Ed., ISBN: 9780071263788, 1150p., 2008.

Robinson,, J. and Y., Rahmat-Samii, “Particle swarm optimization in electromagnetics,” IEEE Transactions on Antennas and Propagation, vol. 52, pp. 397–407, 2004.Google Scholar

Rodriguez,, J. A. and F., Ares, “Optimization of the perfromance of arrays with failed elements using simulated annealing technique,” Journal of Electromagnetics Wave and Applications, vol. 12, pp.1625–1638, 1998.Google Scholar

Rodriguez,, J. A., F., Ares, and E., Moreno, “GA procedure for linear array failure correction”, Electronics Letters, 36, pp. 196–198, 2000.Google Scholar

Steyskal,, H. and R. J., Mailloux, “Generalization of a phased array error correction method,” IEEE Antennas and Propagation Society, AP-S International Symposium (Digest) Proceedings of the 1996 AP-S International Symposium & URSI Radio Science Meeting. pp. 506–509, Jul. 1996.Google Scholar

Su,, C. and S. M., Lin, “A method for locating defective elements in the large planar array,” IEEE Antenna and Propagation Society International Symposium Digest, vol. 24, pp. 31–33, 1986.Google Scholar

Wang,, L. L., D.G., Fang, and W. X., Sheng, “Combination of genetic algorithm and fast Fourier transform for synthesis of arrays,” Microwave and Optical Technology Letters, vol. 37, pp. 56–59, 2003.Google Scholar

Yeo, B. K. and Y., Lu, “Adaptive array digital beam forming using complex-coded particle swarm optimization-genetic algorithm,” Proceedings of Asia-Pacific Microwave Conference, 3p., 4–7 Dec. 2005.Google Scholar

Yeo,, B. K. and Y., Lu, “Array failure correction with a genetic algorithm,” IEEE Transactions on Antennas and Propagation, vol. 47, pp. 823–828, 1999.Google Scholar

Zadeh,, L. A., Fuzzy Logic, Neural Networks and Soft Computing. One-page course announcement of CS 294–4, Spring 1993, The University of California at Berkeley, Nov. 1992.

Zainud-Deen, S.H., et. al., “Array failure correction with orthogonal methods,” Proceedings of the 21st National Radio Science Conference,(NRSC 2004), pp. B7:1–9, March 2004.Google Scholar

Zhang,, Q. J. and K. C., Gupta, Neural networks for RF and Microwave Design, Norwood, MA,Artech House, ISBN: 9781580531009, 369p., 2000.

Book contents

7 - Fault Detection in Antenna Arrays

Summary

Access options

References

Save book to Kindle

Save book to Dropbox

Save book to Google Drive