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

Hua-yan CHEN; Mei-qin LIU; Sen-lin ZHANG

doi:10.1631/FITEE.1700598

Your Location：

Home >

Browse articles >

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

Special Issue on Marine Information Technology | Updated：2022-05-19

- Energy-efficient localization and target tracking via underwater mobile sensor networks
- 水下移动传感器网络高能效节点定位和目标跟踪
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 8, Pages: 999-1012(2018)
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Hua-yan CHEN, E-mail:chenhuayan@zju.edu.cn" ]
  Mei-qin LIU, E-mail:liumeiqin@zju.edu.cn
  [ "Sen-lin ZHANG, E-mail:slzhang@zju.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1700598
  CLC： TP274
- Received：11 September 2017，
  
  Revised：2018-;8-15，
  
  Published：2018-08
- Accepted：
Scan QR Code
Hua-yan CHEN, Mei-qin LIU, Sen-lin ZHANG. Energy-efficient localization and target tracking via underwater mobile sensor networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(8): 999-1012.
DOI：

Hua-yan CHEN, Mei-qin LIU, Sen-lin ZHANG. Energy-efficient localization and target tracking via underwater mobile sensor networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(8): 999-1012. DOI： 10.1631/FITEE.1700598.

摘要

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。

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 multistep 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.

关键词

Keywords

references

P Aggarwal , , , X Wang . . Joint sensor localisation and target tracking in sensor networks . . IET Radar Sonar Navig , , 2011 . . 5 ( ( 3 ): ): 225 - - 233 . . DOI: 10.1049/iet-rsn.2010.0118 http://doi.org/10.1049/iet-rsn.2010.0118 . .

I Arasaratnam , , , S Haykin . . Cubature Kalman filters . . IEEE Trans Autom Contr , , 2009 . . 54 ( ( 6 ): ): 1254 - - 1269 . . DOI: 10.1109/TAC.2009.2019800 http://doi.org/10.1109/TAC.2009.2019800 . .

TC Austin , , , RP Stokey , , , KM Sharp . . Paradigm: a buoy-based system for AUV navigation and tracking . . OCEANS MTS/IEEE Conf and Exhibition , , 2000 . . p.935 - - 938 . . DOI: 10.1109/OCEANS.2000.881376 http://doi.org/10.1109/OCEANS.2000.881376 . .

SP Beerens , , , H Ridderinkhof , , , JTF Zimmerman . . An analytical study of chaotic stirring in tidal areas . . Chaos Sol Fract , , 1994 . . 4 ( ( 6 ): ): 1011 - - 1029 . . DOI: 10.1016/0960-0779(94)90136-8 http://doi.org/10.1016/0960-0779(94)90136-8 . .

M Bhardwaj , , , AP Chandrakasan . . Bounding the lifetime of sensor networks via optimal role assignments . . 21st Annual Joint Conf of the IEEE Computer and Communications Societies , , 2002 . . p.1587 - - 1596 . . DOI: 10.1109/INFCOM.2002.1019410 http://doi.org/10.1109/INFCOM.2002.1019410 . .

PJ Brockwell , , , R Dahlhaus . . Generalized LevinsonDurbin and Burg algorithms . . J Econom , , 2004 . . 118 ( ( 1-2 ): ): 129 - - 149 . . DOI: 10.1016/S0304-4076(03)00138-6 http://doi.org/10.1016/S0304-4076(03)00138-6 . .

CT Calafate , , , C Lino , , , A Diaz-Ramirez , , , 等 . . An integral model for target tracking based on the use of a WSN . . Sensors , , 2013 . . 13 ( ( 6 ): ): 7250 - - 7278 . . DOI: 10.3390/s130607250 http://doi.org/10.3390/s130607250 . .

HY Chen , , , SL Zhang , , , MQ Liu , , , 等 . . An artificial measurements-based adaptive filter for energy-efficient target tracking via underwater wireless sensor networks . . Sensors , , 2017 . . 17 ( ( 5 ): ): 971 DOI: 10.3390/s17050971 http://doi.org/10.3390/s17050971 . .

JH Cui , , , JJ Kong , , , M Gerla , , , 等 . . The challenges of building mobile underwater wireless networks for aquatic applications . . IEEE Netw , , 2006 . . 20 ( ( 3 ): ): 12 - - 18 . . DOI: 10.1109/MNET.2006.1637927 http://doi.org/10.1109/MNET.2006.1637927 . .

P Del Moral . . Nonlinear filering: interacting particle resolution . . Compt Rend Acad Sci Ser Ⅰ-Math , , 1997 . . 325 ( ( 6 ): ): 653 - - 658 . . DOI: 10.1016/S0764-4442(97)84778-7 http://doi.org/10.1016/S0764-4442(97)84778-7 . .

Y Guo , , , YT Liu . . Localization for anchor-free underwater sensor networks . . Comput Electr Eng , , 2013 . . 39 ( ( 6 ): ): 1812 - - 1821 . . DOI: 10.1016/j.compeleceng.2013.02.001 http://doi.org/10.1016/j.compeleceng.2013.02.001 . .

G Isbitiren , , , OB Akan . . Three-dimensional underwater target tracking with acoustic sensor networks . . IEEE Trans Veh Technol , , 2011 . . 60 ( ( 8 ): ): 3897 - - 3906 . . DOI: 10.1109/TVT.2011.2163538 http://doi.org/10.1109/TVT.2011.2163538 . .

N Kantas , , , SS Singh , , , A Doucet . . Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks . . IEEE Trans Signal Process , , 2012 . . 60 ( ( 10 ): ): 5038 - - 5047 . . DOI: 10.1109/TSP.2012.2205923 http://doi.org/10.1109/TSP.2012.2205923 . .

S Kim , , , Y Yoo . . 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 , , 2013 . . p.374 - - 381 . . DOI: 10.1109/CSE.2013.64 http://doi.org/10.1109/CSE.2013.64 . .

NH Kussat , , , CD Chadwell , , , R Zimmerman . . Absolute positioning of an autonomous underwater vehicle using GPS and acoustic measurements . . IEEE J Ocean Eng , , 2005 . . 30 ( ( 1 ): ): 153 - - 164 . . DOI: 10.1109/JOE.2004.835249 http://doi.org/10.1109/JOE.2004.835249 . .

WL Li , , , YM Jia , , , JP Du , , , 等 . . Distributed multiplemodel estimation for simultaneous localization and tracking with NOLS mitigation . . IEEE Trans Veh Technol , , 2013 . . 62 ( ( 6 ): ): 2824 - - 2830 . . DOI: 10.1109/TVT.2013.2247073 http://doi.org/10.1109/TVT.2013.2247073 . .

J Lloret . . Underwater sensor nodes and networks . . Sensors , , 2013 . . 13 ( ( 9 ): ): 11782 - - 11796 . . DOI: 10.3390/s130911782 http://doi.org/10.3390/s130911782 . .

AK Mandal , , , S Misra , , , T Ojha , , , 等 . . Oceanic forces and their impact on the performance of mobile underwater acoustic sensor networks . . Int J Commun Syst , , 2017 . . 30 ( ( 1 ): ): e2882 DOI: 10.1002/dac.2882 http://doi.org/10.1002/dac.2882 . .

J Pardey , , , S Roberts , , , L Tarassenko . . A review of parametric modelling techniques for EEG analysis . . Med Eng Phys , , 1996 . . 18 ( ( 1 ): ): 2 - - 11 . . DOI: 10.1016/1350-4533(95)00024-0 http://doi.org/10.1016/1350-4533(95)00024-0 . .

EM Sozer , , , M Stojanovic , , , JG Proakis . . Underwater acoustic networks . . IEEE J Ocean Eng , , 2000 . . 25 ( ( 1 ): ): 72 - - 83 . . DOI: 10.1109/48.820738 http://doi.org/10.1109/48.820738 . .

J Teng , , , H Snoussi , , , C Richard , , , 等 . . Distributed variational filtering for simultaneous sensor localization and target tracking in wireless sensor networks . . IEEE Trans Veh Technol , , 2012 . . 61 ( ( 5 ): ): 2305 - - 2318 . . DOI: 10.1109/TVT.2012.2190631 http://doi.org/10.1109/TVT.2012.2190631 . .

X Wang , , , MX Xu , , , HB Wang , , , 等 . . Combination of interacting multiple models with the particle filter for three-dimensional target tracking in underwater wireless sensor networks . . Math Probl Eng , , 2012 . . 2012 829451 DOI: 10.1155/2012/829451 http://doi.org/10.1155/2012/829451 . .

CH Yu , , , JW Choi . . Interacting multiple model filterbased distributed target tracking algorithm in underwater wireless sensor networks . . Int J Contr Autom Syst , , 2014 . . 12 ( ( 3 ): ): 618 - - 627 . . DOI: 10.1007/s12555-013-0238-y http://doi.org/10.1007/s12555-013-0238-y . .

Q Zhang , , , MQ Liu , , , SL Zhang . . Node topology effect on target tracking based on UWSNs using quantized measurements . . IEEE Trans Cybern , , 2015 . . 45 ( ( 10 ): ): 2323 - - 2335 . . DOI: 10.1109/TCYB.2014.2371232 http://doi.org/10.1109/TCYB.2014.2371232 . .

XC Zhou , , , HB Shen , , , JP Ye . . Integrating outlier filtering in large margin training . . J Zhejiang Univ-Sci C (Comput & Electron) , , 2011 . . 12 ( ( 5 ): ): 362 - - 370 . . DOI: 10.1631/jzus.C1000361 http://doi.org/10.1631/jzus.C1000361 . .

YM Zhu , , , ZS You , , , J Zhao , , , 等 . . The optimality for the distributed Kalman filtering fusion with feedback . . Automatica , , 2001 . . 37 ( ( 9 ): ): 1489 - - 1493 . . DOI: 10.1016/S0005-1098(01)00074-7 http://doi.org/10.1016/S0005-1098(01)00074-7 . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Joint target tracking using an autonomous underwater vehicle and underwater sensor networks for underwater applications

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking

High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain

Related Author

Yi YANG

Boon-Chong SEET

Zhenkai ZHANG

Zhaohong LV

Long TENG

Qiang GUO

Tianxiang YIN

Yunfei GUO

Related Institution

Ocean College, Jiangsu University of Science and Technology

Electrical and Electronic Engineering Department, Auckland University of Technology

Wuhan Maritime Communication Research Institute

College of Information and Communication Engineering, Harbin Engineering University

China National Aeronautical Radio Electronics Research Institute

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰