A Novel Statistical Manifold Algorithm for Position Estimation

Bin Xia; Wenhao Yuan; Nan Xie; Caihong Li

doi:10.1109/JAS.2019.1911771

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(6): 1513-1518

Bin Xia, Wenhao Yuan, Nan Xie and Caihong Li, "A Novel Statistical Manifold Algorithm for Position Estimation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1513-1518, Nov. 2019. doi: 10.1109/JAS.2019.1911771

Citation:

Bin Xia, Wenhao Yuan, Nan Xie and Caihong Li, "A Novel Statistical Manifold Algorithm for Position Estimation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1513-1518, Nov. 2019. doi: 10.1109/JAS.2019.1911771

Citation:

PDF( 1217 KB)

A Novel Statistical Manifold Algorithm for Position Estimation

doi: 10.1109/JAS.2019.1911771

Funds: This work was supported by the National Natural Science Foundation of China (61701286, 61473179) and Shandong Provincial Natural Science Foundation of China (ZR2017MF047)

More Information

Author Bio:
Bin Xia graduated from Shandong Polytechnic University, China, in 1994. He received the M.S. degree from Shandong University, China, in 2003 and the Ph.D. degree from Beijing University of Posts and Telecommunications, China, in 2006. He is currently an Associate Professor at the School of Computer Science and Technology, Shandong University of Technology, China. His research interests include signal processing and artificial intelligence

Wenhao Yuan graduated from East China University of Science and Technology, China, in 2008. He received the Ph.D. degree from East China University of Science and Technology, China, in 2013. He is currently a Lecturer at the School of Computer Science and Technology, Shandong University of Technology, China. His research interests include signal processing and machine learning, especially speech enhancement based on deep learning

Nan Xie graduated from Shandong University, China, in 1999. He received the M.S. degree from Shandong University, China, in 2002 and the Ph.D. degree from Ocean University of China, China, in 2005. He is currently an Associate Professor at the School of Computer Science and Technology, Shandong University of Technology, China. His research interests include automation and artificial intelligence, especially networked control systems, and intelligent control

Caihong Li graduated from the School of Information Engineering, Southwest University of Science and Technology, China, in 1993. She received the M.S. degree from Shandong University of Science and Technology, China, in 2000, and the Ph.D. degree from Shandong University, China, in 2007. She is currently a Professor at the School of Computer Science and Technology, Shandong University of Technology, China. Her research interests include artificial intelligence, robotics, and machine learning
Corresponding author: All the authors are with the School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China (e-mail: xiabin@sdut.edu.cn; yuanwenhao@sdut.edu.cn; xienan@sdut.edu.cn; lich@sdut.edu.cn)
Received Date: 2018-09-12
Revised Date: 2018-12-05
Accepted Date: 2019-03-02

Abstract

Abstract

In this paper, a novel statistical manifold algorithm is proposed for position estimation of sensor nodes in a wireless network, making full use of distance information available among unknown nodes and simultaneous localization of multiple unknown nodes. To begin, a ranging model including the distance information among unknown nodes is established. With the reparameterization of the natural parameter and natural statistic, the solution problem of the ranging model is transformed into a parameter estimation problem of the curved exponential family. Then, a natural gradient method is adopted to deal with the parameter estimation problem of the curved exponential family. To ensure the convergence of the proposed algorithm, a particle swarm optimization method is utilized to obtain initial values of the unknown nodes. Experimental results indicate that the proposed algorithm can improve the positioning accuracy, compared with the traditional algorithm.
- Natural gradient,
- particle swarm optimization (PSO),
- ranging model,
- statistical manifold (SM)

FullText(HTML)

References(20)

References

[1]	F. Viani, M. Bertolli, and A. Polo, " Low-cost wireless system for agrochemical dosage reduction in precision farming,” IEEE Sensors J., vol. 17, no. 1, pp. 5–6, 2017. doi: 10.1109/JSEN.2016.2622244
[2]	X. Li, X. H. Wei, X. Chen, X. L. Tang, T. M. Xie, and L. Jia, " Construction and specification of complex events from sensor network,” Int. Agricultural Engineering J., vol. 9, no. 8, pp. 3173–3180, 2013.
[3]	Y. Rao, W. J. Xu, J. Zhu, Z. H. Jiang, R. C. Wang, and S. W. Li, " Practical deployment of an in-field wireless sensor network in date palm orchard,” Int. J. Distributed Sensor Networks, vol. 13, no. 5, pp. 11, 2017.
[4]	M. Schwager, M. P. Vitus, S. Powers, D. Rus, and C. J. Tomlin, " Robust adaptive coverage control for robotic sensor networks,” IEEE Trans. Control of Network Systems, vol. 4, no. 3, pp. 462–476, Sep. 2017. doi: 10.1109/TCNS.2015.2512326
[5]	W. He, Z. J. Li, and C. L. P. Chen, " A survey of human-centered intelligent robots: issues and challenges,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 602–609, Oct. 2017. doi: 10.1109/JAS.2017.7510604
[6]	W. X. Xue, W. N. Qiu, X. H. Hua, and K. G. Yu, " Improved Wi-Fi RSSI measurement for indoor localization,” IEEE Sensors J., vol. 17, no. 7, pp. 2224–2230, Apr. 2017. doi: 10.1109/JSEN.2017.2660522
[7]	H. Sallouha, A. Chiumento, and S. Pollin, " Localization in long-range ultra narrow band IoT networks using RSSI”, in Proc. IEEE Int. Conf. Communications, pp. 1–6, Jul. 2017.
[8]	Z. D. Yin, K. Cui, Z. L. Wu, and L. Yin, " Entropy-based TOA estimation and SVM-based ranging error mitigation in UWB ranging systems,” Sensors (Switzerland), vol. 15, no. 5, pp. 11701–11724, May 2015. doi: 10.3390/s150511701
[9]	A. Alsmady and F. Awad, " Optimal Wi-Fi access point placement for RSSI-based indoor localization using genetic algorithm”, in Proc. 8th Int. Conf. Information and Communication Systems, pp. 287–291, May. 2017.
[10]	Z. Ansari, R. Ghazizadeh, and Z. Shokhmzan, " Gradient descent approach to secure localization for underwater wireless sensor networks”, in Proc. 24th Iranian Conf. Electrical Engineering, pp. 103–107, Oct. 2016.
[11]	H. B. Wang and H. B. Wang, " Research of localization algorithm based on the steepest descent method MDS in wireless sensor network,” WIT Trans. Modelling and Simulation, vol. 60, pp. 1249–1256, Jun. 2014.
[12]	M. Naraghi-Pour and G. C. Rojas, " A Novel algorithm for distributed localization in wireless sensor networks,” ACM Trans. Sensor Networks, vol. 11, no. 1, pp. 1–25, Aug. 2014.
[13]	Z. Yang and J. Laaksonen, " Principal whitened gradient for information geometry,” Neural Networks, vol. 21, no. 2–3, pp. 232–240, 2008. doi: 10.1016/j.neunet.2007.12.016
[14]	Y. Q. Cheng, X. Z. Wang, T. Caelli, X. Li, and B. Moran, " Optimal nonlinear estimation for localization of wireless sensor networks,” IEEE Trans. Signal Processing, vol. 59, no. 12, pp. 5674–5685, Dec. 2011. doi: 10.1109/TSP.2011.2166547
[15]	Y. Q. Cheng, " Information theory and geometric methods of radar signal processing”, Ph.D. dissertation, University of Defense Technology, Changsha, China, pp. 118–120, 2012.
[16]	X. Z. Wang, Y. Q. Cheng, and B. Moran, Nonlinear parameter estimation in statistical manifolds”, in Proc. IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 101–104, 2014.
[17]	J. Kennedy and R.C. Eberhart, " Particle swarm optimization”, in Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948, 1995.
[18]	W. Dong and M. C. Zhou, " A supervised learning and control method to improve particle swarm optimization algorithms,” IEEE Trans. Systems,Man and Cybernetics:Systems, vol. 47, no. 7, pp. 1149–1159, Jul. 2017. doi: 10.1109/TSMC.2016.2560147
[19]	J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, " Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evolutionary Computation, vol. 10, no. 3, pp. 281–295, Jun. 2006. doi: 10.1109/TEVC.2005.857610
[20]	K. Zhang, Q. J. Huang, and Y. M. Zhang, " Enhancing comprehensive learning particle swarm optimization with local optima topology,” Information Sciences, vol. 471, pp. 1–18, Jan. 2019. doi: 10.1016/j.ins.2018.08.049

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(8) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (1392) PDF downloads(163)

A Novel Statistical Manifold Algorithm for Position Estimation

doi: 10.1109/JAS.2019.1911771

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content