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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

Self Recurrent Neural Network Based Target Tracking in Wireless Sensor Network using State Observer

Author(s): Satish R. Jondhale* and Rajkumar S. Deshpande

Volume 9, Issue 2, 2019

Page: [165 - 178] Pages: 14

DOI: 10.2174/2210327908666181029103202

Price: $65

Abstract

Background & Objective: Mobile target tracking based on data from wireless sensor networks (WSN) is a hot topic that has been investigated both from a theoretical and practical point of view in the literature. Tracking the position and velocity of a target moving in WSN (especially in the context of uncertain noisy measurement channel) is a very challenging task. To deal with the uncertainty in system dynamics as well as uncertainty in target states, an Observer Based Self Recurrent Neural Network (OBSRNN) is proposed in this paper.

Methods: The proposed algorithm employs a state observer based tracking control strategy and thereby allows for accurate estimation of mobile target moving along a predefined route in WSN. The Self Recurrent Neural Network (SRNN) framework is used to approximate the uncertainty in the system dynamics, while a full-order state observer is used to estimate the unknown state vector.

Conclusion: The simulation analysis is performed to evaluate the efficacy of the proposed work.

Keywords: Observer, root mean square error, self recurrent neural network, target tracking, uncertain system, wireless sensor networks.

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[1]
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. Wireless sensor networks: a survey. Comput Netw 2002; 38(4): 393-422.
[2]
Correal NS, Patwari N. Wireless sensor networks: Challenges and opportunities. In: Proc. 2001 Virginia tech symp. Wireless Personal Communications 2001; 1-9.
[3]
Perkins M, Correal N, O’Dea B. Emergent wireless sensor network limitations: A plea for advancement in core technologies. In: Sensors Orlando, FL, USA: IEEE. 2002; 2: pp. 1505-9.
[4]
Viani F, Rocca P, Oliveri G, Trinchero D, Massa A. Localization, tracking, and imaging of targets in wireless sensor networks: An invited review. Radio Sci 2011; 46(5): 1-2.
[5]
Klančar G, Škrjanc I. Tracking-error model-based predictive control for mobile robots in real time. Robot Auton Syst 2007; 55(6): 460-9.
[6]
Zdešar A, Škrjanc I, Klančar G. Visual trajectory-tracking model-based control for mobile robots. Int J Adv Robot Syst 2013; 10(9): 323.
[7]
Kalman RE. A new approach to linear filtering and prediction problems. T ASME J Basic Eng 1960; 82: 35-45.
[8]
Lingqun W, Shizhu P, Yingping Z. Moving vehicle tracking based on unscented Kalman filter algorithm. In: 2009 WRI world congress on computer science and information engineering Los Angeles, CA, USA: IEEE. 2009; 2: pp. 33-8.
[9]
Van der Merwe R, Wan EA. The square-root unscented Kalman filter for state and parameterestimation. In: 2001 IEEE international conference on acoustics, speech, and signal processing Salt Lake City, UT, USA: IEEE . 2001; 6: pp. 3461-4.
[10]
Gustafsson F, Gunnarsson F, Bergman N, et al. Particle filters for positioning, navigation, and tracking. IEEE Trans Signal Process 2002; 50(2): 425-37.
[11]
Mihaylova L, Angelova D, Bull D, Canagarajah N. Localization of mobile nodes in wireless networks with correlated in time measurement noise. IEEE Trans Mobile Comput 2011; 10(1): 44-53.
[12]
Lin CM, Hsu CF. Recurrent-neural-network-based adaptive-backstepping control for induction servomotors. IEEE Trans Ind Electron 2005; 52(6): 1677-84.
[13]
Qi J, Taha AF, Wang J. Comparing Kalman filters and observers for power system dynamic state estimation with model uncertainty and malicious cyber-attacks EEE Access 2018; 6: 77155-68.
[14]
Chenna SK, Jain YK, Kapoor H, et al. State estimation and tracking problems: A comparison between Kalman filter and recurrent neural networks. In: International conference on neural information processing Berlin, Heidelberg: Springer . 2004; 8: pp. 275-81.
[15]
Klančar G, Škrjanc I. Tracking-error model-based predictive control for mobile robots in real time. Robot Auton Syst 2007; 55(6): 460-9.
[16]
Blažič S. A novel trajectory-tracking control law for wheeled mobile robots. Robot Auton Syst 2011; 59(11): 1001-7.
[17]
Škrjanc I, Klančar G. Optimal cooperative collision avoidance between multiple robots based on Bernstein-Bézier curves. Robot Auton Syst 2010; 58(1): 1-9.
[18]
LaValle SM. Planning algorithms London: Cambridge university press, 2006 May 29
[19]
Jondhale SR, Deshpande RS. Modified kalman filtering framework based real time target tracking against environmental dynamicity in wireless sensor networks. Ad Hoc Sens Wirel Netw 2018; 40: 224-33.
[20]
Kaaniche H, Kamoun F. Mobility prediction in wireless ad hoc networks using neural networks. J Telecommun 2010; 2(1): 1004-10.
[21]
Mahfouz S, Mourad-Chehade F, Honeine P, Farah J, Snoussi H. Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sens J 2014; 14(10): 3715-25.
[22]
Chenna SK, Jain YK, Kapoor H, et al. State estimation and tracking problems: A comparison between Kalman filter and recurrent neural networks. In: International conference on neural information processing Berlin, Heidelberg; Springer. 2004; pp. 275-81.
[23]
Fiaz M, Mahmood A, Jung SK. Tracking noisy targets: A review of recent object tracking approaches. arXiv:180203098 2018.
[24]
Webster JG. Wiley encyclopedia of electrical and electronics engineering New York: John Wiley 1999 Mar 25
[25]
Ondruska P, Posner I. deep tracking: seeing beyond seeing using recurrent neural networks. In: Thirtieth Association for the Advancement of Artificial Intelligence Conference, AAAI Publications 2016.
[26]
Chow TW, Fang Y. A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics. IEEE Trans Ind Electron 1998; 45(1): 151-61.
[27]
Sivakumar SC, Robertson W, Phillips WJ. Online stabilization of block-diagonal recurrent neural networks. IEEE Trans Neural Netw 1999; 10(1): 167-75.
[28]
Fang Y, Chow TW, Li XD. Use of a recurrent neural network in discrete sliding-mode control. IEEE P-Contr Theor 1999; 146(1): 84-90.
[29]
Valcher ME. State observers for discrete-time linear systems with unknown inputs. IEEE Trans Automat Contr 1999; 44(2): 397-401.

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