Kalman filter and joint tracking and classification based on belief functions in the TBM framework-Reference-Cited by-同舟云学术

Kalman filter and joint tracking and classification based on belief functions in the TBM framework

Published:2007-01 Issue:1 Volume:8 Page:16-27
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Smets Philippe,Ristic Branko

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Signal Processing,Software

Reference24 articles.

1. P. Smets, B. Ristic, Kalman filters and joint tracking and classification in the TBM framework, in: Proceedings of the Seventh International Conference on Information Fusion, Stockholm, Sweden, 2004, pp. 46–53.

2. Joint target tracking and classification using radar and ESM sensors;Challa;IEEE Trans. Aerospace Electron. Syst.,2001

3. Joint tracking and identification algorithms for multisensor data;Farina;IEE Proc. Radar Sonar Navig.,2002

4. Efficient particle filters for joint tracking and classification;Gordon;Proc. SPIE,2002

5. On target classification using kinematic data;Ristic;Informat. Fus.,2004

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The motion model-based joint tracking and classification using TPHD and TCPHD filters;Signal Processing;2024-06

2. A Multi-Frame Joint Tracking and Classification Method for Weak Target in Radar System;2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS);2022-11-21

3. Integrated Kalman Filter of Accurate Ranging and Tracking With Wideband Radar;IEEE Transactions on Geoscience and Remote Sensing;2020-12

4. Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory;Sensors;2020-07-29

5. Extended Target Tracking and Classification Using Neural Networks;2019 22th International Conference on Information Fusion (FUSION);2019-07