Volume 11, Issue 3 (Journal of Control, V.11, N.3 Fall 2017)
JoC 2017, 11(3): 1-12 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Keshavarz-Mohammadiyan A, Khaloozadeh H. Distributed Estimation and Sensor Selection in Wireless Sensor Network in the Presence of State-Dependent Noise. JoC 2017; 11 (3) :1-12
URL: http://joc.kntu.ac.ir/article-1-394-en.html
URL: http://joc.kntu.ac.ir/article-1-394-en.html
1- K.N. Toosi University of Technology
Abstract: (25142 Views)
In this paper, the problem of distributed state estimation of a nonlinear dynamical system in a decentralized Wireless Sensor Network (WSN) in the presence of state-dependent observation noise is considered. Some bearings or ranging devices, such as ultrasonic sensors, have distance-dependent measurement error and their measurement noise variance grows as their relative distance to the target increases. This state-dependent measurement error leads to poor performance of estimation algorithm. To solve this problem, a consensus-based distributed state estimation methodology is presented in this paper by reaching a consensus on likelihood functions in the presence of state-dependent observation noise of bearings sensors. To reduce energy consumption in WSN, a distributed sensor selection algorithm is proposed. Unlike centralized networks, no fusion center is deployed in decentralized networks to gather and process the collected data, globally. Moreover, there is no global knowledge of the network topology in decentralized networks. Therefore, the Posterior Cramér-Rao Lower Bound (PCRLB) is derived in a distributed fashion in the presence of state-dependent noise of bearings sensors, to perform an adaptive sensor selection algorithm. Simulation results demonstrate the effectiveness of the proposed state estimation and sensor selection algorithms for a target tracking problem
Keywords: Distributed state estimation, wireless sensor network, state-dependent noise, sensor selection, consensus algorithm
Type of Article: Research paper |
Subject:
Special
Received: 2016/08/2 | Accepted: 2017/07/17 | Published: 2017/09/23
Received: 2016/08/2 | Accepted: 2017/07/17 | Published: 2017/09/23
References
1. Chen J., Zhang D., Yu L., 2015, "Distributed non-fragile filtering for sensor networks with randomly occurring filter gain variations", International Journal of General Systems, 44 (7–8), pp. 778-790. [DOI:10.1080/03081079.2015.1010247]
2. Yick J., Mukherjee B., Ghosal D., 2008, "Wireless sensor network survey", Computer Networks, 52, pp. 2292-2330. [DOI:10.1016/j.comnet.2008.04.002]
3. Othman M.F., Shazali K., 2012, "Wireless sensor network applications: a study in environment monitoring system", Procedia Engineering, 41, pp. 1204-1210. [DOI:10.1016/j.proeng.2012.07.302]
4. Fayyaz M., 2011, "Classification of object tracking techniques in wireless sensor networks", Wireless Sensor Network, 3, pp. 121-124. [DOI:10.4236/wsn.2011.34014]
5. Ramya K., Praveen Kumar K., Srinivas Rao V., 2012, "A survey on target tracking techniques in wireless sensor networks", International Journal of Computer Science & Engineering Survey, 3 (4), pp. 93-108. [DOI:10.5121/ijcses.2012.3408]
6. Dong H., Wang Z., Alsaadi F.E., Ahmad B., 2015, "Event-triggered robust distributed state estimation for sensor networks with state-dependent noises", International Journal of General Systems, 44 (2), pp.254-266. [DOI:10.1080/03081079.2014.973726]
7. Read J., Achutegui K., Míguez J., 2014, "A distributed particle filter for nonlinear tracking in wireless sensor networks", Signal Processing, 98, pp. 121-134. [DOI:10.1016/j.sigpro.2013.11.020]
8. Yu C.H., Choi J.W., 2014, "Interacting multiple model filter-based distributed target tracking algorithm in underwater wireless sensor networks", International Journal of Control, Automation, and Systems, 12, pp. 618-627. [DOI:10.1007/s12555-013-0238-y]
9. Chen Cailian, Zhu Shanying, Guan Xinping, Shen Xuemin, Wireless sensor networks: distributed consensus estimation, first, ed., Springer, 2014.
10. Mohammadi A., Asif A., 2015, "Distributed consensus + innovation particle filtering for bearing/range tracking with communication constraints", IEEE Transactions on Signal Processing, 63 (3), pp. 620-635. [DOI:10.1109/TSP.2014.2367468]
11. Li W., Jia Y., 2012, "Consensus-based distributed multiple model UKF for jump Markov nonlinear systems", IEEE Transactions on Automatic Control, 57 (1), pp. 227–233. [DOI:10.1109/TAC.2011.2161838]
12. Açikmeşe B., Mandić M., Speyer J.L., 2014, "Decentralized observers with consensus filters for distributed discrete-time linear systems", Automatica, 50 (4), pp. 1037–1052. [DOI:10.1016/j.automatica.2014.02.008]
13. Hlinka O., Slučiak O., Hlawatsch F., Djurić P.M., Rupp M., 2012, "Likelihood consensus and its application to distributed particle filtering", IEEE Transactions on Signal Processing, 60 (8), pp. 4334-4349. [DOI:10.1109/TSP.2012.2196697]
14. Battistelli G., Chisci L., 2014, "Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability", Automatica, 50 (3), pp. 707-718. [DOI:10.1016/j.automatica.2013.11.042]
15. Battistelli G., Chisci L., Mugnai G., Farina A., Graziano A., 2015, "Consensus-based linear and nonlinear filtering", IEEE Transactions on Automatic Control, 60 (5), pp. 1410-1415. [DOI:10.1109/TAC.2014.2357135]
16. Olfati-Saber R., Murray R.M., 2004, "Consensus problems in networks of agents with switching topology and time-delays", IEEE Transactions on Automatic Control, 49 (9), pp. 1520-1533. [DOI:10.1109/TAC.2004.834113]
17. Hu X., Bao M., Zhang X.P., Guan L., Hu Y.H., 2015, "Generalized iterated Kalman filter and its performance evaluation", IEEE Transactions on Signal Processing, 63 (12), pp. 3204-3217. [DOI:10.1109/TSP.2015.2423266]
18. Harlim J., Mahdi A., Majda A.J., 2014, "An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models", Journal of Computational Physics, 25, pp. 782-812. [DOI:10.1016/j.jcp.2013.10.025]
19. Zhen Y., Harlim J., 2015, "Adaptive error covariances estimation methods for ensemble Kalman filters", Journal of Computational Physics, 294, pp. 619-638. [DOI:10.1016/j.jcp.2015.03.061]
20. Wang X., Fu M., Zhang H., 2012, "Target tracking in wireless sensor networks based on the combination of KF and MLE using distance measurements", IEEE Transactions on Mobile Computing, 11 (4), pp. 567-576. [DOI:10.1109/TMC.2011.59]
21. Hu X., Hu Y., Xu B., 2014, "Generalized Kalman filter tracking with multiplicative measurement noise in a wireless sensor network", IET Signal Processing, 8 (5), pp. 467-474. [DOI:10.1049/iet-spr.2013.0161]
22. Keshavarz-Mohammadiyan A., Khaloozadeh H., 2016, "Interacting multiple model and sensor selection algorithms for maneuvering target tracking in wireless sensor networks with multiplicative noise", International journal of systems science, doi 10.1080/00207721.2016.1177128. [DOI:10.1080/00207721.2016.1177128]
23. Zhang Q., Zhang C., Liua M., Zhang S., 2014, "Local node selection for target tracking based on underwater wireless sensor networks", International Journal of Systems Science, 46 (16), pp. 2918-2927. [DOI:10.1080/00207721.2014.880199]
24. Mohammadi A., Asif A., 2015, "Consensus-based distributed dynamic sensor selection in decentralized sensor networks using the posterior Cramér–Rao lower bound", Signal Processing, 108, pp. 558-575. [DOI:10.1016/j.sigpro.2014.10.005]
25. Liu Z., Wang J., Zue Y., 2013, "PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks", Future Generation Computer Systems, 29, pp. 1751-1757. [DOI:10.1016/j.future.2012.01.003]
26. Arulampalam M.S., Maskell S., Gordosn, N., Clapp, T., 2002, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking", IEEE Transactions on Signal Processing, 50, (2), pp. 174-188. [DOI:10.1109/78.978374]
27. Fu X., Jia Y., 2010, "An Improvement on Resampling Algorithm of Particle Filters", IEEE Transactions on Signal Processing, 58 (10), pp. 5414-5420. [DOI:10.1109/TSP.2010.2053031]
28. Steven Kay M., Fundamentals of statistical signal processing: estimation theory, Prentice-Hall, (1993).
29. Calafiore G.C., Abrate F., 2009, "Distributed linear estimation over sensor networks, International Journal of Control", 82 (5), pp. 868-882. [DOI:10.1080/00207170802350662]
30. Zhang S., Xiao W., Ang V.M.H., Tham C.K., 2007, "IMM filter based sensor scheduling for maneuvering target tracking in wireless sensor networks", International Conference on Intelligent Sensors, Sensor Networks and Information, pp. 287 – 292. [DOI:10.1109/ISSNIP.2007.4496858]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
