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.

• Periodical: Key Engineering Materials (Volumes 467-469)
• Pages: 447-452
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.467-469.447
• Online since: February 2011
• Authors: Jin Long Yang, Hong Bing Ji, Jin Mang Liu
• Keywords: Centralized Fusion, Gaussian Filter, Maneuvering Target Tracking, Multiple Passive Sensors

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