Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF

Suyao Qi; Chundong Qi; Wenhua Wang

doi:10.1109/ISAPE.2018.8634247

Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF

Suyao Qi, Chundong Qi, Wenhua Wang

Beijing Institute of Technology

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

6 Citations (Scopus)

Abstract

This paper proposes a tracking algorithm for maneuvering targets based on the Interacting Multiple Model (IMM) and Modified Gain EKF (MGEKF) algorithm that can modify the Markov transition probability matrix in real time. The algorithm improves the error caused by the transition probability matrix determined by the prior information in the classical IMM algorithm that does not match the current model well. The simulation results show that the maneuvering target tracking performance of this algorithm is better than the conventional IMM-EKF algorithm.

Original language	English
Title of host publication	2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538673027
DOIs	https://doi.org/10.1109/ISAPE.2018.8634247
Publication status	Published - 2 Jul 2018
Event	12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Hangzhou, China Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings

Conference

Conference	12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018
Country/Territory	China
City	Hangzhou
Period	3/12/18 → 6/12/18

Keywords

Kalman filter
Time-varying Markov transition probability matrix
interactive multi-model algorithm

Access to Document

10.1109/ISAPE.2018.8634247

Cite this

Qi, S., Qi, C., & Wang, W. (2018). Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF. In 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings Article 8634247 (2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISAPE.2018.8634247

Qi, Suyao ; Qi, Chundong ; Wang, Wenhua. / Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF. 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings).

@inproceedings{d0888d1c23784addb7503909f5d62e9c,

title = "Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF",

abstract = "This paper proposes a tracking algorithm for maneuvering targets based on the Interacting Multiple Model (IMM) and Modified Gain EKF (MGEKF) algorithm that can modify the Markov transition probability matrix in real time. The algorithm improves the error caused by the transition probability matrix determined by the prior information in the classical IMM algorithm that does not match the current model well. The simulation results show that the maneuvering target tracking performance of this algorithm is better than the conventional IMM-EKF algorithm.",

keywords = "Kalman filter, Time-varying Markov transition probability matrix, interactive multi-model algorithm",

author = "Suyao Qi and Chundong Qi and Wenhua Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ISAPE.2018.8634247",

language = "English",

series = "2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings",

address = "United States",

}

Qi, S, Qi, C & Wang, W 2018, Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF. in 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings., 8634247, 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018, Hangzhou, China, 3/12/18. https://doi.org/10.1109/ISAPE.2018.8634247

Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF. / Qi, Suyao; Qi, Chundong; Wang, Wenhua.
2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8634247 (2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings).

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

TY - GEN

T1 - Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF

AU - Qi, Suyao

AU - Qi, Chundong

AU - Wang, Wenhua

PY - 2018/7/2

Y1 - 2018/7/2

N2 - This paper proposes a tracking algorithm for maneuvering targets based on the Interacting Multiple Model (IMM) and Modified Gain EKF (MGEKF) algorithm that can modify the Markov transition probability matrix in real time. The algorithm improves the error caused by the transition probability matrix determined by the prior information in the classical IMM algorithm that does not match the current model well. The simulation results show that the maneuvering target tracking performance of this algorithm is better than the conventional IMM-EKF algorithm.

AB - This paper proposes a tracking algorithm for maneuvering targets based on the Interacting Multiple Model (IMM) and Modified Gain EKF (MGEKF) algorithm that can modify the Markov transition probability matrix in real time. The algorithm improves the error caused by the transition probability matrix determined by the prior information in the classical IMM algorithm that does not match the current model well. The simulation results show that the maneuvering target tracking performance of this algorithm is better than the conventional IMM-EKF algorithm.

KW - Kalman filter

KW - Time-varying Markov transition probability matrix

KW - interactive multi-model algorithm

UR - http://www.scopus.com/inward/record.url?scp=85062821528&partnerID=8YFLogxK

U2 - 10.1109/ISAPE.2018.8634247

DO - 10.1109/ISAPE.2018.8634247

M3 - Conference contribution

AN - SCOPUS:85062821528

T3 - 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings

BT - 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Qi S, Qi C, Wang W. Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF. In 2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8634247. (2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings). doi: 10.1109/ISAPE.2018.8634247

Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this