JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 7    Date : July 2008

Interacting Multiple Model Particle-type Filtering Approaches to Ground Target Tracking
Ronghua Guo, Zheng Qin, Xiangnan Li, and Junliang Chen
Page(s): 23-30
Full Text:
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Abstract
Ground maneuvering target tracking is a class of nonlinear and/or no-Gaussian filtering problem. A
new interacting multiple model unscented particle filter (IMMUPF) is presented to deal with the
problem. A bank of unscented particle filters is used in the interacting multiple model (IMM)
framework for updating the state of moving target. To validate the algorithm, two groups of multiple
model filters: IMM-type filters and particle-type multiple model filters, are compared for their
capability in dealing with ground maneuvering target tracking problem. Simulation shows that
particle-type filters outperform IMM-type filters in the estimate accuracy and the IMMUPF method
relatively has much better performance than the IMMPF method.

Index Terms
particle filter, unscented particle filter (UPF), interacting multiple model (IMM), ground target tracking