Paper Title:
A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors
  Abstract

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, H. B. Ji, J. M. Liu, "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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