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Target Tracking Formulation of the SVSF With Data Association Techniques.
- Source :
-
IEEE Transactions on Aerospace & Electronic Systems . Feb2017, Vol. 53 Issue 1, p12-25. 14p. - Publication Year :
- 2017
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189251
- Volume :
- 53
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Aerospace & Electronic Systems
- Publication Type :
- Academic Journal
- Accession number :
- 122662450
- Full Text :
- https://doi.org/10.1109/TAES.2017.2649138