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Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

Chien-Hao Tseng¹, Chih-Wen Chang, Dah-Jing Jwo

¹National Applied Research Laboratories, National Center for High-Performance Computing, 22 Keyuan Rd., Central Taiwan Science Park, Taichung City 40763, Taiwan. c00how00@nchc.org.tw

Sensors (Basel, Switzerland)

|February 10, 2012

Summary

This summary is machine-generated.

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This study introduces the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) for improved vehicle navigation. The FUZZY-IMMUKF enhances accuracy by adaptively managing noise in nonlinear systems.

Area of Science:

Navigation Systems
Control Theory
Signal Processing

Background:

Traditional Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods face performance degradation due to noise uncertainty in nonlinear systems.
Interacting Multiple Model (IMM) provides model switching for process noise covariance estimation.
Fuzzy logic adaptive systems (FLAS) can determine system noise bounds using fuzzy inference systems (FIS).

Purpose of the Study:

To present the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach for integrated navigation processing in maneuvering vehicles.
To enhance navigation estimation accuracy by addressing nonlinearities and noise uncertainties.
To develop a robust sensor fusion strategy for vehicle navigation.

Main Methods:

Keywords:

fuzzy logic integrated navigation interacting multiple model unscented Kalman filter

Related Experiment Videos

Application of the unscented Kalman filter (UKF) using sigma points to avoid linearization errors.
Integration of the interacting multiple model (IMM) for adaptive process noise covariance determination.
Utilization of a fuzzy logic adaptive system (FLAS) with a fuzzy inference system (FIS) to manage system noise bounds.

Main Results:

The FUZZY-IMMUKF approach effectively handles nonlinear problems in vehicle navigation.
Demonstrated remarkable improvement in navigation estimation accuracy compared to conventional UKF and IMMUKF methods.
The algorithm shows enhanced robustness against process and measurement noise uncertainties.

Conclusions:

The proposed FUZZY-IMMUKF algorithm offers a superior solution for integrated navigation in maneuvering vehicles.
Adaptive noise management through fuzzy logic and IMM significantly boosts navigation accuracy.
This approach provides a more reliable and accurate sensor fusion strategy for complex navigation tasks.