MCA Learning Algorithm for Incident Signals Estimation:A Review
| ||International Journal of ComputerTrends and Technology (IJCTT)|| |
|© 2014 by IJCTT Journal|
|Volume-8 Number-1 |
|Year of Publication : 2014|
|Authors : Rashid Ahmed , John A. Avaritsiotis|
|DOI : 10.14445/22312803/IJCTT-V8P102|
Rashid Ahmed , John A. Avaritsiotis. "MCA Learning Algorithm for Incident Signals Estimation:A Review". International Journal of Computer Trends and Technology (IJCTT) 8(1):5-9, February 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Recently there has been many works on adaptive subspace filtering in the signal processing literature. Most of them are concerned with tracking the signal subspace spanned by the eigenvectors corresponding to the eigenvalues of the covariance matrix of the signal plus noise data. Minor Component Analysis (MCA) is important tool and has a wide application in telecommunications, antenna array processing, statistical parametric estimation, etc. As an important feature extraction technique, MCA is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will present a MCA learning algorithm to extract minor component from input signals, and the learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Simulation results will be furnished to illustrate the theoretical results achieved.
 Alexander I. Galushkin “Neural Networks Theory”Springer-Verlag Berlin Heidlberg, 2007, ISBN 0-387-94162-5 .
[ 2] TimoHonkela, WlodzislawDuch.” Artificial Neural Networks and Machine Learning –ICANN 2011” 21st International Conference on Artificial Neural Networks Espoo, Finland, June 14-17, 2011 Proceedings.
 G. Dreyfus.”Neural Networks, Methodology and Applications” Original French edition published by Eyrolles, springer, Paris,2004, ISBN 103-540-22980.
 Dovid Levin, Emanuel A., Sharon G. “Maximum Likelihood Estimation of Direction of Arrival usingan acoustic vector-sensor”, International Audio Laboratories Erlangen, Germany, 2012.
 Malcolm Hawkes “Acoustic Vector-Sensor Beamformingand Capon Direction Estimation “IEEE Transection on Signal Processing, Vol. 46, No. 9, SEPTEMBER 1998.
 K R SRINIVAS and V U REDDY “Sensitivity of TLS and Minimum Norm Methods of DOA Estimation to errors due to either Unite data or sensor gainand phase perturbations”, Sadhana, Vol. 16,Part 3 November 1991,pp. 195-212. © Printed in India.
 MitsuharuM., Shuji H. “Multiple Signal Classification by Aggregated Microphones” 2005, IEICE, ISSN: 0916-8508.
 FeifeiGao and Alex B. Gershman “A Generalized ESPRIT Approach toDirection-of-Arrival Estimation”IEEE Signal Processing Letter, Vol. 12, No. 3, March 2005.
 PradhumnaS., Michael H., PuttipongM., “Performance Analysis for Direction of ArrivalEstimating Algorithms, IEEE 75th Vehicular Technology Conference(VTC Spring), 2012.
 G.Wang and X.-G.Xia.” Iterative Algorithm for Direction of Arrival Estimation with wideband chirp signals” IEEE.,2000, ISSN : 1350-2395 .
 YanwaZhang“CGHA For Principal Component Extraction In The Complex Domain “,IEEE, Transaction on Neural Networks ,Vol. 8,no. 5,pp. 1031-1036 ,sept. 1997.
 Adnan S.,” DOA Based Minor Component Estimation using Neural Networks”,AJES, Electrical Engineering Dept., Vol.3, No.1, 2010.
 Kwang In Kim, Matthias O. Franz, Bernhard, “KernelHebbian Algorithm forIterative Kernel PrincipalComponentAnalysis ”,Max Planck Institutefor Biological Cybernetics, June 2003.
 K. Gao, M.O. Ahmad, M.N. Swamy, “Learning Algorithm for Total Least Squares Adaptive Signal Processing, Electronics Letters.Feb. 1992.
 S. Barbarossa, E. Daddio, G. Galati, “Comparison of Optimum and Linear Prediction Technique for Clutter Cancellation”, Communications, Radar and Signal Processing, IEE Proceedings, ISSN (0143-7070).
 L. Xu, E. Oja, C. Suen, “Modified Hebbian Learning for Curve and Surface Fitting, Neural Networks, 1992.
 J.W. Griffiths, “Adaptive Array Processing, A Tutorial, Communications, Radar and Signal Processing, IEE Proceedings, ISSN :0143-7070.
 R. Schmidt, Multiple Emitter Location and Signal Parameter Estimation, IEEE Trans. Antennas Propagation (1986) 276–280.
DezhongPeng, Zhang Yi.” A New Algorithm for Sequential Minor Component Analysis”International Journal of Computational Intelligence Research ,ISSN 0973-1873 Vol.2, No.2 (2006).
 JieLuo, Xieting Ling “Minor Component Analysis with Independent to Blind 2 Channel Equalization,” IEEE, Fudan University-China.
 Donghai Li, ShihaiGao, Feng Wang, “Direction of Arrival Estimation Based on MinorComponent Analysis Approach”,Neural Information Processing, Springer-Verlag Berlin Heidelberg , 2006.
 Belloni F., Richter A.,Koivunen V. “ DOA Estimation via Manifold Separation for Arbitrary Array Structures,” IEEE, Transaction on signal processing, Vol, 55,No.10, October, 2007.
 Qingfu Zhang, Yiu-Wung Leung, “A Class of Learning Algorithms for Principal Component Analysis and Minor Component Analysis”, IEEE Transection on Neural Network, Vol. 11, No.2, March 2000.
 D. Randall Wilson, Tony R.,” The Need for Small Learning Rates on Large Problems”, International Joint Conference on Neural Networks, 2001.
 Tadeu N., Sergio L. Netto, Paulo S.,“Low complexity covariance-Based DOA Estimation Algorithm,” EURASIP, 2007.
 C.Chatterjee, San Diego,"On relative convergence properties of principal component analysis algorithms", presented at IEEE Transactions on Neural Networks, 1998.
Keywords Direction of Arrival; Neural networks; Principle Component Analysis; Minor Component Analysis.