Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/30146
Title: | Target Tracking Formulation of the SVSF With Data Association Techniques |
Authors: | Attari M Habibi S Gadsden SA |
Department: | Mechanical Engineering |
Keywords: | 40 Engineering;4001 Aerospace Engineering |
Publication Date: | 1-Feb-2017 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract: | An important area of study for aerospace and electronic systems involves target tracking applications. To successfully track a target, state and parameter estimation strategies are used in conjunction with data association techniques. Even after 50 years, the Kalman filter (KF) remains the most popular and well-studied estimation strategy in the field. However, the KF adheres to a number of strict assumptions that leads to instabilities in some cases. The smooth variable structure filter (SVSF) is a relatively new method, which is becoming increasingly popular due to its robustness to disturbances and uncertainties. This paper presents a new formulation of the SVSF. The probabilistic and joint probabilistic data association techniques are combined with the SVSF and applied on multitarget tracking scenarios. In addition, a new covariance formulation of the SVSF is presented based on improving the estimation results of nonmeasured states. The results are compared and discussed with the popular KF method. |
metadata.dc.rights.license: | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND |
URI: | http://hdl.handle.net/11375/30146 |
metadata.dc.identifier.doi: | https://doi.org/10.1109/taes.2017.2649138 |
ISSN: | 0018-9251 1557-9603 |
Appears in Collections: | Mechanical Engineering Publications |
Files in This Item:
File | Description | Size | Format | |
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018-Target_Tracking_Formulation_of_the_SVSF_With_Data_Association_Techniques.pdf | Published version | 1.62 MB | Adobe PDF | View/Open |
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