Multiellipsoidal extended target tracking with known extent using sequential Monte Carlo framework

Date

2019-01-01

Author

Kara, Suleyman Fatih
Özkan, Emre

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In this paper, we consider a variant of the extended target tracking (ETT) problem, namely the multiellipsoidal ETT problem. In multiellipsoidal ETT, target extent is represented by multiple ellipses, which correspond to the origin of the measurements on the target surface. The problem involves estimating the target's kinematic state and solving the association problem between the measurements and the ellipses. We cast the problem in a sequential Monte Carlo (SMC) framework and investigate different marginalization strategies to find an efficient particle filter. Under the known extent assumption, we define association variables to find the correct association between the measurements and the ellipses; hence, the posterior involves both discrete and continuous random variables. By expressing the measurement likelihood as a mixture of Gaussians we derive and employ a marginalized particle filter for the independent association variables without sampling the discrete states. We compare the performance of the method with its alternatives and illustrate the gain in nonstandard marginalization.

Subject Keywords

Electrical and Electronic Engineering, General Computer Science

URI

https://hdl.handle.net/11511/48118

Journal

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES

DOI

https://doi.org/10.3906/elk-1811-52

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

S. F. Kara and E. Özkan, “Multiellipsoidal extended target tracking with known extent using sequential Monte Carlo framework,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, pp. 1546–1558, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48118.