Abstract
Most of the real-world practical systems are inherently dynamic and their characteristics are represented by transfer functions which are IIR in nature. In literature-distributed estimation, algorithms have been developed for stable FIR system. In this paper, a distributed estimation technique is developed for identification of IIR system present at each node of a wireless sensor network. The distributed parameter estimation generally based on two modes of cooperation strategies: Incremental and Diffusion. In case of change in network topology, the diffusion mode of cooperation works well and shows robustness to link and node failure. Thus, an infinite impulse response diffusion least mean square (IIR DLMS) algorithm is introduced. In simulation, its performance is compared with the incremental version (infinite impulse response incremental least mean square algorithm (IIR ILMS)). Superior performance by the proposed approach is reported for parameter estimation of two IIR systems under various noisy environments.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Estrin, L. Girod, G. Pottie, M. Srivastava, Instrumenting the world with wireless sensor networks, in: Proceedings of the IEEE Inter- national Conference on Acoustics, Speech, Signal Processing (ICASSP), Salt Lake City, UT, vol. 4, May 2001, pp. 2033–2036.
I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor net-works, IEEE Commun. Mag. 40(8) (2002) 102–114.
Lopes, CassioG, and Ali H. Sayed. “Distributed processing over adaptive networks.” Proc. adaptive sensor array processing workshop. 2006.
Sayed, Ali H., and Cassio G. Lopes. “Adaptive processing over distributed networks.” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 90.8 (2007): 1504–1510.
Majhi, B., Panda, G., & Mulgrew, B. Distributed identification of nonlinear processes using incremental and diffusion type PSO algorithms. In IEEE Congress on Evolutionary Computation, 2009. CEC’09. (pp. 2076–2082).
Lopes, Cassio G., and Ali H. Sayed. “Distributed adaptive incremental strategies: Formulation and performance analysis.” 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP Proceedings. Vol. 3. IEEE, 2006.
C.G. Lopes, A.H. Sayed, Diffusion least-mean squares over adaptive networks, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP), Honolulu, HI, April 2007, pp. 917–920.
C.G. Lopes, A.H. Sayed, Incremental adaptive strategies over distributed network, IEEE Trans. Signal Process. 55(August(8)) (2007) 4064–4077.
Cattivelli, F. S., and A. H. Sayed. “Diffusion LMS Strategies for Distributed Estimation.” IEEE Transactions on Signal Processing 3.58 (2010): 1035–1048.
Turajlic, Emir, and Olja Bozanovic. “A novel adaptive IIR filter algorithm.” Telecommunications Forum (TELFOR), 2012 20th. IEEE, 2012.
Shynk, John J. “Adaptive IIR filtering.” IEEE Assp Magazine 6.2 (1989): 4–21.
Majhi, B. and Panda G. “Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization.” Digital Signal Processing 23.4 (2013): 1303–1313.
Lopes, Cassio G., and Ali H. Sayed. “Diffusion least-mean squares over adaptive networks: Formulation and performance analysis.” IEEE Transactions on Signal Processing 56.7 (2008): 3122–3136.
Li, Leilei, Yonggang Zhang, and Jonathon A. Chambers. “Variable length adaptive filtering within incremental learning algorithms for distributed networks.” Signals, Systems and Computers, 2008 42nd Asilomar Conference on. IEEE, 2008.
Nedic, Angelia, and Dimitri P. Bertsekas. “Incremental subgradient methods for nondifferentiable optimization.” SIAM Journal on Optimization 12, no. 1 (2001): 109–138.
Karaboga, Nurhan. “A new design method based on artificial bee colony algorithm for digital IIR filters.” Journal of the Franklin Institute 346.4 (2009): 328–348.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dimple, K., Kotary, D.K., Nanda, S.J. (2019). Diffusion Least Mean Square Algorithm for Identification of IIR System Present in Each Node of a Wireless Sensor Networks. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_63
Download citation
DOI: https://doi.org/10.1007/978-981-10-8055-5_63
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8054-8
Online ISBN: 978-981-10-8055-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)