Applying neural network algorithm to data association technique

Y. N. Chung; H. T. Chen; D. J. Juang; J. Y. Chen; J. R. Lee

Applying neural network algorithm to data association technique

Y. N. Chung, H. T. Chen, D. J. Juang, J. Y. Chen, J. R. Lee

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Data association plays an important role in radar tracking algorithm. The problem of tracking multiple targets is studied in this paper. In order to solve the complicated situation and reduce computation burden because of the multiple tracking environment, an approach has been developed in this paper. This algorithm is implemented with an adaptive filter which consists of a data association technique denoted competitive Hopfield neural network and Kalman filtering to solve both data association and target tracking problems simultaneously. In order to prove the tracking performance, a computer simulation algorithm is proposed in this paper. Because of its computation capability of this algorithm, the radar measurement related to existed target tracks can be chosen optimally. Computer simulation results indicate that this approach successfully and optimally solves the data association problems.

Original language	English
Title of host publication	Proceedings - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2005
Editors	T. Roska, B.E. Shi, Z. Chen, C.-T. Lin, C. Rekeczky
Pages	114-117
Number of pages	4
Publication status	Published - 2005 Oct 31
Event	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA - Hsinchu, Taiwan Duration: 2005 May 28 → 2005 May 30

Other

Other	9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA
Country	Taiwan
City	Hsinchu
Period	05-05-28 → 05-05-30

All Science Journal Classification (ASJC) codes

Software

Access to Document

Fingerprint Dive into the research topics of 'Applying neural network algorithm to data association technique'. Together they form a unique fingerprint.

Cite this

Chung, Y. N., Chen, H. T., Juang, D. J., Chen, J. Y., & Lee, J. R. (2005). Applying neural network algorithm to data association technique. In T. Roska, B. E. Shi, Z. Chen, C-T. Lin, & C. Rekeczky (Eds.), Proceedings - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2005 (pp. 114-117)

@inproceedings{6b4bf97ad8904f258073a6cbec4af1e8,

title = "Applying neural network algorithm to data association technique",

abstract = "Data association plays an important role in radar tracking algorithm. The problem of tracking multiple targets is studied in this paper. In order to solve the complicated situation and reduce computation burden because of the multiple tracking environment, an approach has been developed in this paper. This algorithm is implemented with an adaptive filter which consists of a data association technique denoted competitive Hopfield neural network and Kalman filtering to solve both data association and target tracking problems simultaneously. In order to prove the tracking performance, a computer simulation algorithm is proposed in this paper. Because of its computation capability of this algorithm, the radar measurement related to existed target tracks can be chosen optimally. Computer simulation results indicate that this approach successfully and optimally solves the data association problems.",

author = "Chung, {Y. N.} and Chen, {H. T.} and Juang, {D. J.} and Chen, {J. Y.} and Lee, {J. R.}",

year = "2005",

month = oct,

day = "31",

language = "English",

pages = "114--117",

editor = "T. Roska and B.E. Shi and Z. Chen and C.-T. Lin and C. Rekeczky",

booktitle = "Proceedings - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2005",

note = "9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA ; Conference date: 28-05-2005 Through 30-05-2005",

}

Chung, YN, Chen, HT, Juang, DJ, Chen, JY & Lee, JR 2005, Applying neural network algorithm to data association technique. in T Roska, BE Shi, Z Chen, C-T Lin & C Rekeczky (eds), Proceedings - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2005. pp. 114-117, 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA, Hsinchu, Taiwan, 05-05-28.

Applying neural network algorithm to data association technique. / Chung, Y. N.; Chen, H. T.; Juang, D. J.; Chen, J. Y.; Lee, J. R.

Proceedings - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2005. ed. / T. Roska; B.E. Shi; Z. Chen; C.-T. Lin; C. Rekeczky. 2005. p. 114-117.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Applying neural network algorithm to data association technique

AU - Chung, Y. N.

AU - Chen, H. T.

AU - Juang, D. J.

AU - Chen, J. Y.

AU - Lee, J. R.

PY - 2005/10/31

Y1 - 2005/10/31

N2 - Data association plays an important role in radar tracking algorithm. The problem of tracking multiple targets is studied in this paper. In order to solve the complicated situation and reduce computation burden because of the multiple tracking environment, an approach has been developed in this paper. This algorithm is implemented with an adaptive filter which consists of a data association technique denoted competitive Hopfield neural network and Kalman filtering to solve both data association and target tracking problems simultaneously. In order to prove the tracking performance, a computer simulation algorithm is proposed in this paper. Because of its computation capability of this algorithm, the radar measurement related to existed target tracks can be chosen optimally. Computer simulation results indicate that this approach successfully and optimally solves the data association problems.

AB - Data association plays an important role in radar tracking algorithm. The problem of tracking multiple targets is studied in this paper. In order to solve the complicated situation and reduce computation burden because of the multiple tracking environment, an approach has been developed in this paper. This algorithm is implemented with an adaptive filter which consists of a data association technique denoted competitive Hopfield neural network and Kalman filtering to solve both data association and target tracking problems simultaneously. In order to prove the tracking performance, a computer simulation algorithm is proposed in this paper. Because of its computation capability of this algorithm, the radar measurement related to existed target tracks can be chosen optimally. Computer simulation results indicate that this approach successfully and optimally solves the data association problems.

UR - http://www.scopus.com/inward/record.url?scp=27144516736&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27144516736&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:27144516736

SP - 114

EP - 117

BT - Proceedings - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2005

A2 - Roska, T.

A2 - Shi, B.E.

A2 - Chen, Z.

A2 - Lin, C.-T.

A2 - Rekeczky, C.

T2 - 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA

Y2 - 28 May 2005 through 30 May 2005

ER -