1. Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking.
- Author
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Liang-Qun Li, Xiao-Li Wang, Zong-Xiang Liu, and Wei-Xin Xie
- Subjects
- *
KALMAN filtering , *PROBABILITY density function , *REGRESSION analysis , *ALGORITHMS , *BEARINGS (Machinery) - Abstract
Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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