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Gaussian Sum FIR Filtering for 2D Target Tracking

  • Control Theory and Applications
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Abstract

The constant velocity (CV) motion model has been typically used in 2-dimensional (2D) target tracking problems, but it has an uncertain process noise covariance problem. Unlike the Kalman filter (KF), the least square finite impulse response filter (LSFF) does not require noise covariance information and can overcome the uncertain process noise covariance problem. However, the LSFF has a cumbersome problem that is to select a suitable value of design parameter called the horizon size. This paper proposes a Gaussian sum FIR filter (GSFF), where the Gaussian sum method is used to deal with the horizon size in LSFFs. The GSFF overcomes the uncertain process noise covariance problem and can be alternative to existing filters in 2D target tracking. Superior performance of GSFF is demonstrated by comparison with the Gaussian sum KF (GSKF) that is an existing filter to solve the uncertain process noise covariance problem.

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Authors and Affiliations

  1. Department of Electrical Engineering, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, 54538, Korea

    Jung Min Pak

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  1. Jung Min Pak
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Correspondence to Jung Min Pak.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Young Soo Suh under the direction of Editor Chan Gook Park. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2017R1C1B5017021).

Jung Min Pak received his B.S., M.S., and Ph.D. degrees in electrical engineering from Korea University, Seoul, Korea, in 2006, 2008, and 2015, respectively. He was a Research Professor of the Brain Korea 21 Plus Humanware IT Program at Korea University from 2015 to 2016. He is currently a Professor in the Department of Electrical Engineering, Wonkwang University, Iksan, Korea. His research interests include state estimation, localization, and visual object tracking.

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Pak, J.M. Gaussian Sum FIR Filtering for 2D Target Tracking. Int. J. Control Autom. Syst. 18, 643–649 (2020). https://doi.org/10.1007/s12555-018-0938-4

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  • DOI: https://doi.org/10.1007/s12555-018-0938-4

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