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IMM Iterated Extended Kalman Particle Filter Based Target Tracking |
Zhang Jun-gen; Ji Hong-bing |
School of Electronic Engineering, Xidian University, Xi’an 710071, China |
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Abstract A new algorithm based on Interacting Multiple Model (IMM) iterated extended Kalman particle filter is proposed for maneuvering target tracking, which uses an improved Particle Filter (PF) in multiple model. First, the Iterated Extended Kalman Filter (IEKF) is modified by providing a new measurement update based on Gauss-Newton iteration, thus the linearity error is reduced. Then the modified IEKF is used to generate the importance density function of PF, which integrates the latest observation into system state transition density. Finally, simulation results are presented to demonstrate the improved performance of the proposed method over Interacting Multiple Model Particle Filter (IMMPF).
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Received: 09 March 2009
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Corresponding Authors:
Zhang Jun-gen
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