Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

Tian Lai Xu

doi:10.4028/www.scientific.net/AMR.753-755.2117

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Federated IMM-UKF Algorithm for Multi-Sensor Data...

Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

Abstract:

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

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Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

2117-2120

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.2117

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Online since:

August 2013

Authors:

Tian Lai Xu

Keywords:

Data Fusion, Federated Filter, Interacting Multiple Model, UKF

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