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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publication2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538673027
DOIs
Publication statusPublished - 2 Jul 2018
Event12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Hangzhou, China
Duration: 3 Dec 20186 Dec 2018

Publication series

Name2018 12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018 - Proceedings

Conference

Conference12th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2018
Country/TerritoryChina
CityHangzhou
Period3/12/186/12/18

Keywords

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

Fingerprint

Dive into the research topics of 'Maneuvering target tracking algorithm based on adaptive markov transition probabilitiy matrix and IMM-MGEKF'. Together they form a unique fingerprint.

Cite this