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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2017 - 5

A Novel Robust Interacting Multiple Model Algorithm for Maneuvering Target Tracking

GHAZAL, M. See more information about GHAZAL, M. on SCOPUS See more information about GHAZAL, M. on IEEExplore See more information about GHAZAL, M. on Web of Science, DOUSTMOHAMMADI, A. See more information about DOUSTMOHAMMADI, A. on SCOPUS See more information about DOUSTMOHAMMADI, A. on SCOPUS See more information about DOUSTMOHAMMADI, A. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,586 KB) | Citation | Downloads: 118 | Views: 129

Author keywords
markov processes, infrared sensor, radar, state estimation, filtering algorithm

References keywords
tracking(16), systems(10), control(7), model(6), transaction(5), theory(5), multiple(5), maneuvering(5), estimation(5), algorithm(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 35 - 42
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03005
Web of Science Accession Number: 000410369500005
SCOPUS ID: 85028542876

Abstract
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In this paper, the state estimation problem for discrete-time jump Markov systems is considered. A minimax filtering technique, interacting multiple model algorithm based on game theory, is developed for discrete-time stochastic systems. Filter performance improvement in presence of model uncertainties, measurement noise, and unknown steering command of the maneuvering target is illustrated. It is shown that the technique presented in this paper has a better performance in comparison with the traditional Kalman filter with minimum estimation error criterion for the case of worst possible steering command of target. In particular, simulation results illustrate the improved performance of the proposed filter compared to Interacting Multiple Model (IMM), diagonal-matrix-weight IMM (DIMM), and IMM based on (IMMH) filters.


References | Cited By  «-- Click to see who has cited this paper

[1] C. J. Lee, J. M. Pak, C. K. Ahn, Min, K. M., P. Shi, M. T. Lim, "Multi-Target FIR Tracking Algorithm for Markov Jump Linear Systems Based on True-Target Decision-Making," Neurocomputing, vol. 168, no. 11, pp. 298-307, 2015.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 9]


[2] Q. Zhang, L. Li, X.G. Yan, S.K. Spurgeon, "Sliding Mode Control for Singular Stochastic Markovian Jump Systems with Uncertainties," Automatica, vol. 79, no. 5, pp. 27-34, 2017.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[3] H. Habibi, I. Howard, R. Habibi, "Bayesian Sensor Fault Detection in a Markov Jump System," Asian Journal of Control, In press, 2017.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[4] X. Li, J. Lam, H. Gao, J. Xiong, "H8 and H2 Filtering for Linear Systems with Uncertain Markov Transitions," Automatica, vol. 67, no. 5, pp. 252-266, 2016.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 16]


[5] X. Fu, Y. Jia, J. Du, F. Yu, "New Interacting Multiple Model Algorithms for the Tracking of the Manoeuvring Target," IET Control Theory & Applications, vol. 4, no. 10,p. 2184-2194, 2010.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 37]


[6] X. Fu,Y. Shang, H. Yuan, "Improved Diagonal Interacting Multiple Model Algorithm for Manoeuvering Target Tracking Based on H8 Filter," IET Control Theory & Applications, vol. 9, no. 12, pp. 1887-1892, 2015.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 6]


[7] W. Zhu, W. Wang, G. Yuan, "An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking," Sensors, vol. 16, no. 6, pp. 805-907, 2016.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 8]


[8] S. Vasuhi, V. Vaidehi, "Target Tracking Using Interactive Multiple Model for Wireless Sensor Network," Information Fusion, vol. 27, no. 1, pp. 41-53, 2016.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 14]


[9] F. Dufour, M. Mariton, "Tracking a 3D Maneuvering Target with Passive Sensors," IEEE Transactions on Aerospace Electronic Systems, vol. 27, no. 4, pp. 725–739, 1991.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 66]


[10] J.H. Yin, B. Z. Cui, Y. F. Wang, "A Novel Maneuvering Target Tracking Algorithm for Radar/Infrared Sensors," Chin. J. Electron, vol. 19, no. 4, pp. 752-756, 2010.

[11] Q.C. Wang, W. F. Wang, "Tracking Method Based on Separation and Combination of the Measurements for Radar and IR Fusion System," Journal of Systems Engineering and Electronics, vol. 20, no. 2, pp. 241-246, 2009.

[12] W. Li, Y. Jia, J. Du, J. Zhang, "Robust State Estimation for Jump Markov Linear Systems with Missing Measurements," Journal of the Franklin Institute, vol. 350, no. 6, pp. 1476-87, 2013.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 13]


[13] Y. J. He, M. Li, J. L. Zhang, J. P. Yao, "Infrared Target Tracking via Weighted Correlation Filter," Infrared Physics & Technology, vol. 73, pp. 103-114, 2015.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 10]


[14] M. Ghazal, A. Doustmohammadi, "Multi-sensor Target Tracking in the Presence of Clutter Using Game Theory," Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, First Published, 2017.
[CrossRef]


[15] Y. Ulker, B. Gunsel, "Multiple Model Target Tracking with Variable Rate Particle Filters," Digital Signal Processing, vol. 22, no. 3, pp. 417-429, 2012.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 16]


[16] A. Houles, Y. Bar-Shalom, "Multisensor Tracking of A Maneuvering Target in Clutter," IEEE Transaction on Aerospace Electronic Systems, vol. 25, no. 2, pp. 176-189, 1989.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]


[17] B. Mocanu, T. Ruxandra, T. Zaharia, "3D Object Metamorphosis with Pseudo Metameshes," Advances in Electrical and Computer Engineering, vol. 15, no. 1, pp. 115-122, 2015.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[18] G. Zames, "Feedback and Optimal Sensitivity: Model Reference Transformations Multiplicative Seminorms, and Approximate Inverses," IEEE Transaction on Automatic Control, vol. 26, no. 2, pp. 301-320, 1981.
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[CrossRef] [Web of Science Times Cited 81]


[20] D. Gu, "A Game Theory Approach To Target Tracking in Sensor Networks," IEEE Transaction on System Man and Cybernetics Part B, vol. 41, no. 1, pp. 2-13, 2011.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 42]


[21] J. C. Preisig, "Optimal Minmax Estimation and the Development of Minmax Estimation Error Bounds," IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, USA, 1992, pp. 285–288.
[CrossRef] [SCOPUS Times Cited 2]


[22] I. Yaesh, U. Shaked, "Min–Max Kalman filtering," Systems and Control Letters, vol. 53, no. 3, pp. 217-228. 2004.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 10]


[23] S. Zhuk, V. Mallet, "Reduced Minimax Filtering by Means of Differential-Algebraic Equations," 5th International Conference on Physics and Control, León, Spain, 2011, pp. 1-8.

[24] S. Zhuk, "Minimax State Estimation for Linear Stationary Differential-Algebraic Equations," in Proc. 16th IFAC Symposium on System Identification, Brussels, Belgium, 2012, pp. 143-148.
[CrossRef] [SCOPUS Times Cited 11]


[25] I. Yaesh, U. Shaked, "Discrete-Time Min-Max Tracking," IEEE Transaction on Aerospace Electronic Systems, vol. 42, no. 2, pp. 540-547, 2006.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]


[26] D. Simon, "A Game Theory Approach to Constrained Minimax State Estimation," IEEE Transaction on Signal Processing, vol. 54, no. 2, pp. 405-412.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 45]


[27] M. Ghazal, A. Doustmohammadi, "A Novel Target Tracking Algorithm For Simultaneous Measurements of Radar and Infrared Sensors," Advances in Electrical and Computer Engineering, vol. 16, n. 3, 2016.
[CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[28] X. R. Li, V. P. Jilkov, "Survey of Maneuvering Target Tracking. Part I. Dynamic Models," IEEE Transactions on Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1333-1364, 2003.
[CrossRef] [Web of Science Times Cited 723] [SCOPUS Times Cited 1155]




References Weight

Web of Science® Citations for all references: 2,152 TCR
SCOPUS® Citations for all references: 2,779 TCR

Web of Science® Average Citations per reference: 74 ACR
SCOPUS® Average Citations per reference: 96 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-11-17 02:12 in 157 seconds.




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