An Improvement in Maximum Likelihood Location Estimation Algorithm in Sensor Networks

International Journal of Computer Science & Engineering Survey ◽

10.5121/ijcses.2011.2106 ◽

2011 ◽

Vol 2 (1) ◽

pp. 78-86 ◽

Cited By ~ 1

Author(s):

Zeinab Abbasi ◽

Ahmad Farahi ◽

Hamid Haj Seyed Javadi

Keyword(s):

Sensor Networks ◽

Maximum Likelihood ◽

Estimation Algorithm ◽

Location Estimation

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A Modification Strategy of Maximum Likelihood Method for Location Estimation Based on Received Signal Strength in Sensor Networks

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1093/ietfec/e90-a.5.1093 ◽

2007 ◽

Vol E90-A (5) ◽

pp. 1093-1104 ◽

Cited By ~ 1

Author(s):

J. TAKETSUGU ◽

J. YAMAKITA

Keyword(s):

Sensor Networks ◽

Maximum Likelihood ◽

Maximum Likelihood Method ◽

Signal Strength ◽

Received Signal Strength ◽

Location Estimation ◽

Likelihood Method

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The Cross-Entropy Method for Maximum Likelihood Location Estimation Based on IEEE 802.15.4 Radio Signals in Sensor Networks

IEICE Transactions on Communications ◽

10.1093/ietcom/e91-b.8.2724 ◽

2008 ◽

Vol E91-B (8) ◽

pp. 2724-2727 ◽

Cited By ~ 4

Author(s):

J.-C. CHEN

Keyword(s):

Sensor Networks ◽

Maximum Likelihood ◽

Ieee 802.15.4 ◽

Entropy Method ◽

Location Estimation ◽

Cross Entropy ◽

Cross Entropy Method ◽

Radio Signals ◽

The Cross

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Enhanced maximum likelihood location estimation in wireless sensor networks

IET International Conference on Wireless Sensor Network 2010 (IET-WSN 2010) ◽

10.1049/cp.2010.1060 ◽

2010 ◽

Author(s):

Shao-I Chu ◽

Kun-Ta Hsieh ◽

Yan-Haw Chen

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Maximum Likelihood ◽

Location Estimation ◽

Wireless Sensor

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Maximum Likelihood Estimation Algorithm Evaluation for Wireless Sensor Networks

2010 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing ◽

10.1109/synasc.2010.51 ◽

2010 ◽

Cited By ~ 1

Author(s):

Ruxandra-Ioana Rusnac ◽

Aurel Stefan Gontean

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Likelihood Estimation ◽

Estimation Algorithm ◽

Wireless Sensor ◽

Algorithm Evaluation

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Relative Location Estimation Algorithm Based on Coordinates Inner Product Matrix-Based Maximum Likelihood

2013 International Conference on Computer Sciences and Applications ◽

10.1109/csa.2013.54 ◽

2013 ◽

Author(s):

Huan Dai ◽

Keqing Li ◽

Wei Xiao

Keyword(s):

Maximum Likelihood ◽

Estimation Algorithm ◽

Location Estimation ◽

Inner Product ◽

Relative Location ◽

Product Matrix

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Improved maximum likelihood location estimation accuracy in wireless sensor networks using the Cross-Entropy method

2009 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2009.4959836 ◽

2009 ◽

Cited By ~ 6

Author(s):

Jung-Chieh Chen

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Maximum Likelihood ◽

Entropy Method ◽

Location Estimation ◽

Cross Entropy ◽

Wireless Sensor ◽

Estimation Accuracy ◽

Cross Entropy Method ◽

The Cross

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Location estimation using received signal power and maximum likelihood estimation in wireless sensor networks

Electronics and Communications in Japan (Part I Communications) ◽

10.1002/ecja.20359 ◽

2007 ◽

Vol 90 (12) ◽

pp. 62-72 ◽

Cited By ~ 11

Author(s):

Masahiro Takashima ◽

Dapeng Zhao ◽

Kentaro Yanagihara ◽

Kiyoshi Fukui ◽

Shigeru Fukunaga ◽

...

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Likelihood Estimation ◽

Location Estimation ◽

Wireless Sensor ◽

Signal Power

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Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing

IEICE Transactions on Communications ◽

10.1587/transcom.e96.b.1194 ◽

2013 ◽

Vol E96.B (5) ◽

pp. 1194-1201 ◽

Cited By ~ 2

Author(s):

Tetsuhiro OKANO ◽

Shouhei KIDERA ◽

Tetsuo KIRIMOTO

Keyword(s):

Maximum Likelihood ◽

Super Resolution ◽

Estimation Algorithm ◽

Toa Estimation

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Data Fusion of TOA and AOA Measurements for Target Location Estimation in Heterogeneous Wireless Sensor Networks Using Factor Graphs

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e92.a.2927 ◽

2009 ◽

Vol E92-A (11) ◽

pp. 2927-2931 ◽

Cited By ~ 1

Author(s):

Jung-Chieh CHEN

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Fusion ◽

Target Location ◽

Location Estimation ◽

Wireless Sensor ◽

Factor Graphs ◽

Heterogeneous Wireless Sensor Networks

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Multi-AUV Aided Cooperative 3D-localization for Underwater Sensor Networks

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096512666181219122909 ◽

2020 ◽

Vol 13 (1) ◽

pp. 80-90 ◽

Cited By ~ 1

Author(s):

Meiyan Zhang ◽

Wenyu Cai

Keyword(s):

Sensor Networks ◽

Mean Squared Error ◽

Autonomous Underwater Vehicles ◽

Estimation Method ◽

Location Estimation ◽

Underwater Sensor Networks ◽

Coverage Ratio ◽

Minimum Mean Squared Error ◽

3D Localization ◽

Localization Scheme

Background: Effective 3D-localization in mobile underwater sensor networks is still an active research topic. Due to the sparse characteristic of underwater sensor networks, AUVs (Autonomous Underwater Vehicles) with precise positioning abilities will benefit cooperative localization. It has important significance to study accurate localization methods. Methods: In this paper, a cooperative and distributed 3D-localization algorithm for sparse underwater sensor networks is proposed. The proposed algorithm combines with the advantages of both recursive location estimation of reference nodes and the outstanding self-positioning ability of mobile AUV. Moreover, our design utilizes MMSE (Minimum Mean Squared Error) based recursive location estimation method in 2D horizontal plane projected from 3D region and then revises positions of un-localized sensor nodes through multiple measurements of Time of Arrival (ToA) with mobile AUVs. Results: Simulation results verify that the proposed cooperative 3D-localization scheme can improve performance in terms of localization coverage ratio, average localization error and localization confidence level. Conclusion: The research can improve localization accuracy and coverage ratio for whole underwater sensor networks.

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