A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors

Abstract:

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When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian filter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.

Info:

Periodical:

Key Engineering Materials (Volumes 467-469)

Edited by:

Dehuai Zeng

Pages:

447-452

DOI:

10.4028/www.scientific.net/KEM.467-469.447

Citation:

J. L. Yang et al., "A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors", Key Engineering Materials, Vols. 467-469, pp. 447-452, 2011

Online since:

February 2011

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Price:

$35.00

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