Interacting multiple model based distributed target tracking algorithm in UWSNs

This paper deals with the problem of accurately tracking a single target, which has various trajectories, moving through the environment of underwater wireless sensor networks (UWSNs). This paper addresses the issues of estimating the states of the target, improving energy efficiency by using a distributed architecture. Each underwater wireless sensor node composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides algorithms increasing the energy efficiency of each sensor node by using the proposed Wake-up/Sleep (WuS) and Valid Measurement Selecting (VMS) schemes. An interacting multiple model (IMM) filter is applied to the proposed distributed architecture in order to cope with a target maneuver. Simulation results illustrate the performance of the proposed tracking filter according to the various target maneuver patterns.

[1]  Hitoshi Katayama Design of reduced order observer-based output feedback stabilizing controllers for dynamically positioned ships , 2009, 2009 ICCAS-SICE.

[2]  Wendong Xiao,et al.  IMM Filter based Sensor Scheduling for Maneuvering Target Tracking in Wireless Sensor Networks , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[3]  J.H. McClellan,et al.  Multiple-mode Kalman filtering with node selection using bearings-only measurements , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

[4]  J. Heidemann,et al.  Underwater Sensor Networking : Research Challenges and Potential Applications , 2006 .

[5]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[6]  Barbara F. La Scala,et al.  IMM Estimator for Ground Target Tracking with Variable Measurement Sampling Intervals , 2006, 2006 9th International Conference on Information Fusion.

[7]  J. B. Park,et al.  Robust state estimation approach to target localization using range difference of arrival data , 2009, 2009 ICCAS-SICE.

[8]  Jae Weon Choi,et al.  Distributed single target tracking in underwater wireless sensor networks , 2008, 2008 SICE Annual Conference.

[9]  Yao Yu,et al.  Sensor management based on cross-entropy in interacting multiple model Kalman filter , 2004, Proceedings of the 2004 American Control Conference.

[10]  Feng Zhao,et al.  Distributed tracking in wireless ad hoc sensor networks , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[11]  Lihua Xie,et al.  Multi-Sensor Scheduling for Reliable Target Tracking in Wireless Sensor Networks , 2006, 2006 6th International Conference on ITS Telecommunications.

[12]  Lihua Xie,et al.  Multi-Step Adaptive Sensor Scheduling for Target Tracking in Wireless Sensor Networks , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[13]  Parameswaran Ramanathan,et al.  Distributed target classification and tracking in sensor networks , 2003 .

[14]  Jae Weon Choi,et al.  Distributed Target Tracking Algorithm in Underwater Wireless Sensor Networks , 2008 .

[15]  Lihua Xie,et al.  Adaptive sensor scheduling for target tracking in wireless sensor network , 2005, SPIE Optics + Photonics.

[16]  P. Maybeck,et al.  Multiple model tracker based on Gaussian mixture reduction for maneuvering targets in clutter , 2005, 2005 7th International Conference on Information Fusion.

[17]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[18]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[19]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .