JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2018 Vol.19 No.8 P.999-1012

Energy-efficient localization and target tracking via underwater mobile sensor networks

Author(s): Hua-yan Chen, Mei-qin Liu, Sen-lin Zhang
Affiliation(s): State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s): chenhuayan@zju.edu.cn, liumeiqin@zju.edu.cn, slzhang@zju.edu.cn
Key Words: Underwater mobile sensor networks, Energy-efficient, Sensor localization, Target tracking

Share this article to： More <<< Previous Article \|Next Article >>>

Abstract: underwater mobile sensor networks (UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces, so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction (HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multi-step Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking (SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.

水下移动传感器网络高能效节点定位和目标跟踪

概要：水下移动传感器网络（UMSNs）不需要固定，易实现快速部署，其节点可随洋流飘动扩散，易实现大范围监测。因此，UMSNs是远海环境下实现短期、迅速、大范围组网监测跟踪的有效手段。UMSNs目标跟踪的挑战在于高精度的目标跟踪效果需要以高精度的实时节点定位为前提，而UMSNs的节点位置是不断变化的。为获得高精度的实时节点定位结果，运用传统定位方案对其连续定位会消耗大量能量。为减少UMSNs目标跟踪过程中节点定位所消耗的能量，利用节点移动在时间空间的相关性，结合多步Levinson-Durbin线性预测算法，提出高精度的长周期节点位置预测算法（HLMP）。通过高精度位置预测减少节点对实时通信定位的依赖，进而减少网络能耗。根据目标跟踪过程中节点与目标的相关性，提出同时定位跟踪算法（SLAT），进一步提高节点定位精度。仿真结果表明，HLMP算法能够在低能耗情况下显著提高定位精度，SLAT算法进一步降低节点定位误差。更高定位精度将同步提高目标跟踪性能。

关键词：水下移动传感器网络；高能效；节点定位；目标跟踪

Reference

[1]Aggarwal P, Wang X, 2011. Joint sensor localisation and target tracking in sensor networks. IET Radar Sonar Navig, 5(3):225-233.

[2]Arasaratnam I, Haykin S, 2009. Cubature Kalman filters. IEEE Trans Autom Contr, 54(6):1254-1269.

[3]Austin TC, Stokey RP, Sharp KM, 2000. Paradigm: a buoy-based system for AUV navigation and tracking. OCEANS MTS/IEEE Conf and Exhibition, p.935-938.

[4]Beerens SP, Ridderinkhof H, Zimmerman JTF, 1994. An analytical study of chaotic stirring in tidal areas. Chaos Sol Fract, 4(6):1011-1029.

[5]Bhardwaj M, Chandrakasan AP, 2002. Bounding the lifetime of sensor networks via optimal role assignments. 21^st Annual Joint Conf of the IEEE Computer and Communications Societies, p.1587-1596.

[6]Brockwell PJ, Dahlhaus R, 2004. Generalized Levinson-Durbin and Burg algorithms. J Econom, 118(1-2):129-149.

[7]Calafate CT, Lino C, Diaz-Ramirez A, et al., 2013. An integral model for target tracking based on the use of a WSN. Sensors, 13(6):7250-7278.

[8]Chen HY, Zhang SL, Liu MQ, et al., 2017. An artificial measurements-based adaptive filter for energy-efficient target tracking via underwater wireless sensor networks. Sensors, 17(5):971.

[9]Cui JH, Kong JJ, Gerla M, et al., 2006. The challenges of building mobile underwater wireless networks for aquatic applications. IEEE Netw, 20(3):12-18.

[10]Del Moral P, 1997. Nonlinear filering: interacting particle resolution. Compt Rend Acad Sci Ser I-Math, 325(6):653-658.

[11]Guo Y, Liu YT, 2013. Localization for anchor-free underwater sensor networks. Comput Electr Eng, 39(6):1812-1821.

[12]Isbitiren G, Akan OB, 2011. Three-dimensional underwater target tracking with acoustic sensor networks. IEEE Trans Veh Technol, 60(8):3897-3906.

[13]Kantas N, Singh SS, Doucet A, 2012. Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks. IEEE Trans Signal Process, 60(10):5038-5047.

[14]Kim S, Yoo Y, 2013. High-precision and practical localization using seawater movement pattern and filters in underwater wireless networks. IEEE 16^th Int Conf on Computational Science and Engineering, p.374-381.

[15]Kussat NH, Chadwell CD, Zimmerman R, 2005. Absolute positioning of an autonomous underwater vehicle using GPS and acoustic measurements. IEEE J Ocean Eng, 30(1):153-164.

[16]Li WL, Jia YM, Du JP, et al., 2013. Distributed multiple-model estimation for simultaneous localization and tracking with NOLS mitigation. IEEE Trans Veh Technol, 62(6):2824-2830.

[17]Lloret J, 2013. Underwater sensor nodes and networks. Sensors, 13(9):11782-11796.

[18]Mandal AK, Misra S, Ojha T, et al., 2017. Oceanic forces and their impact on the performance of mobile underwater acoustic sensor networks. Int J Commun Syst, 30(1):e2882.

[19]Pardey J, Roberts S, Tarassenko L, 1996. A review of parametric modelling techniques for EEG analysis. Med Eng Phys, 18(1):2-11.

[20]Sozer EM, Stojanovic M, Proakis JG, 2000. Underwater acoustic networks. IEEE J Ocean Eng, 25(1):72-83.

[21]Teng J, Snoussi H, Richard C, et al., 2012. Distributed variational filtering for simultaneous sensor localization and target tracking in wireless sensor networks. IEEE Trans Veh Technol, 61(5):2305-2318.

[22]Wang X, Xu MX, Wang HB, et al., 2012. Combination of interacting multiple models with the particle filter for three-dimensional target tracking in underwater wireless sensor networks. Math Probl Eng, 2012:829451.

[23]Yu CH, Choi JW, 2014. Interacting multiple model filter-based distributed target tracking algorithm in underwater wireless sensor networks. Int J Contr Autom Syst, 12(3):618-627.

[24]Zhang Q, Liu MQ, Zhang SL, 2015. Node topology effect on target tracking based on UWSNs using quantized measurements. IEEE Trans Cybern, 45(10):2323-2335.

[25]Zhou XC, Shen HB, Ye JP, 2011. Integrating outlier filtering in large margin training. J Zhejiang Univ-Sci C (Comput & Electron), 12(5):362-370.

[26]Zhu YM, You ZS, Zhao J, et al., 2001. The optimality for the distributed Kalman filtering fusion with feedback. Automatica, 37(9):1489-1493.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Similar articles

- Go to

水下移动传感器网络高能效节点定位和目标跟踪

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference