A Distributed Target Localization Algorithm for Mobile Adaptive Networks
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Published:
May 23, 2016
Keywords:
Mobile adaptive network Distributed estimation Selective cooperation Target localization
Main Article Content
Abstract
Adaptive networks with mobile nodes possess the distributed adaptation abilities in addition to the collective patterns of motion. Thus, mobile adaptive networks have been used in new applications such as modeling the biological networks and source localization. The original mobile adaptive networks needs full cooperation among the neighbor nodes meaning that each node gathers the information from all of its neighbour nodes. This strategy requires large amount of communications per iteration per node. To address this problem, in this paper we propose a mobile diffusion adaptive network with selective cooperation. In the proposed algorithm each node selects a subset of its neighbors so that its steady-state performance be as close as possible to the traditional mobile diffusion network. Since the selective cooperation reduces the learning rate we also use affine projection algorithm (APA) as the learning rules at the nodes. Our simulation results reveal that the proposed algorithm is able to achieve the same mean-square deviation (MSD) as the original mobile adaptive network but with a lower communication per iteration per node.
Article Details
How to Cite
Pak, A. L., Rastegarnia, A., Khalili, A., & Islam, M. K. (2016). A Distributed Target Localization Algorithm for Mobile Adaptive Networks. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 14(2), 47–56. https://doi.org/10.37936/ecti-eec.2016142.171139
Section
Signal Processing
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[47] A. Pak, S. Aghazadeh, A. Rastegarnia, and A. Khalili, “Target position estimation with mobile adaptive network with selective cooperation,” in Computer Architecture and Digital Systems (CADS), 2013 17th CSI International Symposium on, Oct 2013, pp. 107–110.
[2] R. P. Yadav, P. V. Varde, P. S. V. Nataraj et al. Model-based tracking for agent-based control systems in the case of sensor failures, International Journal of Automation and Computing,
2012, 9(6): 561-569.
[3] Chao-Lei Wang, Tian-Miao Wang, Jian-Hong Liang et al. Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments, International Journal of Automation and Computing, 2013, 10(5): 387-396.
[4] S. Datta, K. Bhaduri, C. Giannella, R. Wolff, and H. Kargupta, “Distributed data mining in peer-to-peer networks,” IEEE Internet Computing., vol. 10, no. 4, pp. 1826, 2006.
[5] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM, 2012, pp. 1316.
[6] J. McLurkin and D. Yamins, “Dynamic Task Assignment in Robot Swarms,” in Robotics: Science and Systems, vol. 8, 2005.
[7] G. Kowadlo and R. Russell, “Robot odor localization: a taxonomy and survey,” The International Journal of Robotics Research, vol. 27, no. 8, pp. 869894, Aug. 2008.
[8] A. Dhariwal, G. Sukhatme, and A. Requicha, “Bacterium-inspired robots for environmental monitoring,” in IEEE International Conference on Robotics and Automation, 2004, pp. 14361443.
[9] S. Squartini, D. Liu, F. Piazza, D. Zhao, and H. He, “Computational Energy Management in Smart Grids,” Neurocomputing, vol. 170, pp. 267 269, 2015.
[10] Y. Xia, S. C. Douglas, and D. P. Mandic, “Adaptive frequency estimation in smart grid applications: Exploiting noncircularity and widely linear adaptive estimators,” Signal Processing Magazine,
IEEE, vol. 29, no. 5, pp. 4454, 2012.
[11] I. Schizas, G. Mateos, and G. Giannakis, “Distributed LMS for consensus-based in-network adaptive processing,” Signal Processing, IEEE Transactions on, vol. 57, no. 6, pp. 2365–2382, 2009.
[12] S. Kar and J. Moura, “Convergence rate analysis of distributed gossip (linear parameter) estimation: Fundamental limits and tradeoffs,” Selected Topics in Signal Processing, IEEE Journal of, vol. 5, no. 4, pp. 674–690, Aug 2011.
[13] J. Chen and A. H. Sayed, “Diffusion adaptation strategies for distributed optimization and learning over networks,” IEEE Trans. Signal Process., vol. 60, no. 8, pp. 4289–4305, August 2012.
[14] N. Bogdanovic, J. Plata-Chaves, and K. Berberidis, “Distributed incremental-based lms for node-specific parameter estimation over adaptive networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, May 2013, pp. 5425–5429.
[15] C. G. Lopes and A. H. Sayed, “Incremental adaptive strategies over distributed networks,” IEEE Trans. Signal Processing, vol. 55, no. 8, pp. 4064–4077, August 2007.
[16] S. Ram, A. Nedic, and V. Veeravalli, “Stochastic incremental gradient descent for estimation in sensor networks,” in Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on, Nov 2007, pp. 582–586.
[17] L. Li and J. A. Chambers, “A new incremental affine projection-based adaptive algorithm for distributed networks,” Signal Processing, vol. 88, no. 10, pp. 2599 – 2603, 2008.
[18] N. Takahashi and I. Yamada, “Incremental adaptive filtering over distributed networks using parallel projection onto hyperslabs,” IEICE Technical Report, vol. 108, pp. 17–22, 2008.
[19] A. Khalili, M. Tinati, and A. Rastegarnia, “An incremental block lms algorithm for distributed adaptive estimation,” in Communication Systems (ICCS), 2010 IEEE International Conference on, Nov 2010, pp. 493–496.
[20] M. Saeed and A. U. H. Sheikh, “A new lms strategy for sparse estimation in adaptive networks,” in Personal Indoor and Mobile Radio Communications (PIMRC), 2012 IEEE 23rd International
Symposium on, Sept 2012, pp. 1722–1733.
[21] N. Bogdanovic, J. Plata-Chaves, and K. Berberidis, “Distributed incremental-based lms for node-specific parameter estimation over adaptive networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE
International Conference on, May 2013, pp.
5425–5429.
[22] W. Bazzi, A. Rastegarnia, and A. Khalili, “A quality-aware incremental lms algorithm for distributed adaptive estimation,” International Journal of Automation and Computing, vol. 11, no. 6, pp. 676–682, 2014.
[23] Y. Liu and W. K. Tang, “Enhanced incremental LMS with norm constraints for distributed innetwork estimation,” Signal Processing, vol. 94, no. 0, pp. 373 – 385, 2014.
[24] A. Khalili, A. Rastegarnia, W. Bazzi, and Z. Yang, “Derivation and analysis of incremental augmented complex least mean square algorithm,” Signal Processing, IET, vol. 9, no. 4, pp. 312–319, 2015.
[25] A. Rastegarnia, A. Khalili, W. Bazzi, and S. Sanei, “An incremental LMS network with reduced communication delay,” Signal, Image and Video Processing, pp. 1–7, 2015.
[26] W. M. Bazzi, A. Rastegarnia, and A. Khalili, A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation, International Journal of Automation and Computing, vol. 11, no. 6, pp. 676-682, 2015.
[27] C. G. Lopes and A. H. Sayed, “Diffusion leastmean squares over adaptive networks: Formulation and performance analysis,” IEEE Trans. on Signal Process., vol. 56, no. 7, pp. 3122–3136, July 2008.
[28] O. Gharehshiran, V. Krishnamurthy, and G. Yin, “Distributed energy-aware diffusion least mean squares: Game-theoretic learning,” Selected Topics in Signal Processing, IEEE Journal of, vol. 7, no. 5, pp. 821–836, Oct 2013.
[29] S. Kumar, A. Sahoo, and D. Acharya, “Block diffusion adaptation over distributed adaptive networks under imperfect data transmission,” in India Conference (INDICON), 2014 Annual IEEE, Dec 2014, pp. 1–5.
[30] R. Arablouei, S. Werner, Y.-F. Huang, and K. Dogancay, “Distributed least mean-square estimation with partial diffusion,” Signal Processing, IEEE Transactions on, vol. 62, no. 2, pp. 472–484, Jan 2014.
[31] P. Di Lorenzo and S. Barbarossa, “Distributed least mean squares strategies for sparsity-aware estimation over gaussian markov random fields,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, May 2014, pp. 5472–5476.
[32] F. Wen, “Diffusion lmp algorithm with adaptive variable power,” Electronics Letters, vol. 50, no. 5, pp. 374–376, Feb 2014.
[33] Y. Zhu, A. Jiang, H. K. Kwan, and K. He, “Distributed sensor network localization using combination and diffusion scheme,” in Digital Signal Processing (DSP), 2015 IEEE International Conference on, July 2015, pp. 1156–1160.
[34] R. Arablouei, S. Werner, K. Doanay, and Y.-F. Huang, “Analysis of a reduced-communication diffusion {LMS} algorithm,” Signal Processing, vol. 117, pp. 355 – 361, 2015.
[35] J. Fernandez-Bes, L. A. Azpicueta-Ruiz, J. Arenas-Garca, and M. T. Silva, “Distributed estimation in diffusion networks using affine least-squares combiners,” Digital Signal Processing, vol. 36, pp. 1 – 14, 2015.
[36] X. Zhao, S-Y. Tu, and A. H. Sayed, “Diffusion adaptation over networks under imperfect information exchange and non-stationary data” IEEE Transactions on Signal Processing, vol. 60, no. 7,
pp. 34603475, July 2012.
[37] S.-Y. Tu and A. Sayed, “Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks,” Signal Processing, IEEE Transactions on, vol. 60, no. 12, pp. 6217–6234, 2012.
[38] X. Zhao and A. Sayed, “Performance limits for distributed estimation over lms adaptive networks,” Signal Processing, IEEE Transactions on, vol. 60, no. 10, pp. 5107–5124, Oct 2012.
[39] F. Cattivelli and A. Sayed, “Diffusion lms strategies for distributed estimation,” Signal Processing, IEEE Transactions on, vol. 58, no. 3, pp. 1035–1048, March 2010.
[40] S. Y. Tu and A. H. Sayed, “Mobile adaptive networks,” Selected Topics on Signal Processing, vol. 5, no. 4, pp. 649–664, August 2011.
[41] M. Lin, M. Murthi, and K. Premaratne, “Mobile adaptive networks for pursuing multiple targets,” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, April 2015, pp. 3217–3221.
[42] F. Cattivelli and A. H. Sayed, “Modeling bird flight formations using diffusion adaptation,” IEEE Trans. Signal Process, vol. 59, no. 5, pp. 2038–2051, May 2011.
[43] J. Li and A. H. Sayed, “Modeling bee swarming behavior through diffusion adaptation with asymmetric information sharing,” EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 18, January 2012.
[44] W. Ma, Z. Sun, J. Li, M. Song, and X. Lang, “An improved artificial bee colony algorithm based on the strategy of global reconnaissance,” Soft Computing, pp. 1–33, 2015.
[45] J.-G. Li, Q.-H. Meng, Y. Wang, and M. Zeng, “Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm,” Autonomous Robots, vol. 30, no. 3, pp. 281–292, 2011.
[46] A. Khalili, A. Rastegarnia, M. Islam, and Z. Yang, “A bio-inspired cooperative algorithm for distributed source localization with mobile nodes,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, July 2013, pp. 3515–3518.
[47] A. Pak, S. Aghazadeh, A. Rastegarnia, and A. Khalili, “Target position estimation with mobile adaptive network with selective cooperation,” in Computer Architecture and Digital Systems (CADS), 2013 17th CSI International Symposium on, Oct 2013, pp. 107–110.