A Target Localization For Unknown Measurement Error Model
Pages 264 - 269
Abstract
In practical target localization, we cannot determine which type of disturbance the measurement error model is in. In this paper, the probability density function (PDF) of the measurement error is assumed to be unknown and a variational Bayesian (VB) iterative algorithm is proposed for target localization. For the unknown measurement error, we model it as a Mixture of Gaussian (MoG) and adjust its parameters to approximate the true error. With the introduction of VB, the estimation of unknown quantities is transformed into a approximation of posterior probability. Finally, the localization problem is transformed into a maximization problem of the posterior distribution of the target node. During the process of maximization, variational distributions and importance sampling are applied to approximate the true posterior distributions and estimate the target's location. Simulation results show that the proposed algorithm has great advantages in positioning accuracy.
References
[1]
A.H. Sayed, A. Tarighat, and N. Khajehnouri, “Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information,” IEEE Signal Process, vol. 22, no. 4, pp. 24-40, Jul.2005.
[2]
X. Bai, Z. Zhang, L. Liu, “Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications,” IEEE Access, vol. 7, pp. 28826-28834, 2019.
[3]
S. Astapov, J. Preden, J. Ehala, “Object detection for military surveillance using distributed multimodal smart sensors,” 2014 19th International Conference on Digital Signal Processing, pp. 366-371, 2014.
[4]
S. Gezici, Z. Tian, G.B. Giannakis, “Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 70-84, 2005.
[5]
Jon H. Wymeersch, J. Lien, M.Z. Win, “Cooperative localization in wireless networks,” Proceedings of the IEEE, vol. 97, no. 2, pp. 427-450, 2009.
[6]
J. Zheng, Y.C. Wu, “Joint time synchronization and localization of an unknown node in wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1309-1320, 2010.
[7]
L. Karim, N. Nasser, “Reliable location-aware routing protocol for mobile wireless sensor network,” Iet Communications, vol. 6, no. 14, pp. 2149-2158, 2012.
[8]
D N. Bulusu, John Heidenmann and Deborah Estrin, “GPS-less low-cost outdoor localization for very small devices,” IEEE Personal Communications, pp. 28-34. Oct. 2000.
[9]
Y. Zou and Q. Wan, “Asynchronous time-of-arrival-based source localization with sensor position uncertainties,” IEEE Communications Letters, vol. 20, no. 9, pp. 1860-1863, Sep. 2016.
[10]
Z. Wang, H. Zhang, T. Lu, and T. A. Gulliver, “Cooperative RSS-based localization in wireless sensor networks using relative error estimation and semidefinite programming,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 483–497, Jan. 2019.
[11]
H. C. So and L. Lin, “Linear least squares approach for accurate received signal strength based source localization,” IEEE Transactions on Signal Processing, vol. 59, no. 8, pp. 4035-4040, Aug. 2011.
[12]
K. C. Ho, “Bias reduction for an explicit solution of source localization using TDOA,” IEEE Transactions on Signal Processing, vol. 60, no. 5, pp.2101-2114, May. 2012.
[13]
P. G. Georgiou, P. Tsakalides, and C.Kyriakakis, “Alpha-stable modeling of noise and robust time-delay estimation in the presence of impulsive noise,” IEEE Transactions on Multimedia, vol.1, no. 3, pp. 291-301, 2002.
[14]
I. Guvenc, C.-C. Chong, and F. Watanabe, “NLOS identification and mitigation for UWB localization systems,” IEEE Wireless Commun. NetwConf., pp. 1571–1576, Mar. 2007.
[15]
Y. Liu, Y. H. Hu, Q. Pan, “Distributed, Robust Acoustic Source Localization in a Wireless Sensor Network,” IEEE Transactions on Signal Processing, vol. 60, no. 8, pp.4350-4359, 2012.
[16]
Y. Zhang, S. Xing, Y. Zhu, “RSS-Based Localization in WSNs Using Gaussian Mixture Model via Semidefinite Relaxation,” IEEE Communications Letters, vol. 21, no. 6, pp. 1329-1332, 2017.
[17]
C. Soltanpur, R. Paravi, M. Ghamari, and B. Adebisi, “Nonlinear MMSE equalizer for impulsive noise mitigation in OFDM-based communications,” IEEE Signal Processing Letters, vol. 26, no. 7, pp. 1016-1020, Jul. 2019.
[18]
C. Soltanpur, R. Paravi, M. Ghamari, and B. Adebisi, “Nonlinear MMSE equalizer for impulsive noise mitigation in OFDM-based communications,” IEEE Signal Processing Letters, vol. 26, no. 7, pp. 1016-1020, Jul. 2019.
[19]
F. Yin, C. Fritsche, F. Gustafsson, and A. M. Zoubir, “EM- and JMAPML based joint estimation algorithms for robust wireless geolocation in mixed LOS/NLOS environments,” IEEE Transactions on Signal Processing, vol. 62, no. 1, pp. 168-182, Jan. 2014.
[20]
S. Kiranyaz, T. Ince, A. Yildirim, and M. Gabbouj, “Fractional particle swarm optimization in multidimensional search space,” IEEE Trans. Syst., Man, Cybern. B. Cybern., vol. 40, no. 2, pp. 298–319, Apr. 2010.
Index Terms
- A Target Localization For Unknown Measurement Error Model
Recommendations
Distributed Localization of a RF Target in NLOS Environments
We propose a novel distributed expectation maximization (EM) method for non-cooperative RF target localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of ...
PMOG
We extend the mixtures of Gaussians (MOG) model to the projected mixture of Gaussians (PMOG) model. In the PMOG model, we assume that q dimensional input data points ...
A two-piece normal measurement error model
AbstractIn the context of measurement error models, the true unobservable covariates are commonly assumed to have a normal distribution. This assumption is replaced here by a more flexible two-piece normal distribution, which allows for ...
Comments
Information & Contributors
Information
Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 20Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)3
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format