|Enhanced Multi-sensor Data Fusion Methodology based on Multiple Model Estimation for Integrated Navigation System
Lei Wang* and Shuangxi Li
International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp.295-305, 2018
Abstract : "A novel multi-sensor data fusion methodology is presented in this paper with respect to noise with
unknown or randomly varying statistics properties and outliers in the SINS/GPS/Odometer integrated navigation
system. The proposed methodology combines an adaptive interacting multiple model filtering (AIMM) and federated
Kalman algorithm. The former implements dynamic interaction and dynamic change of multiple modes
based on the Markov chain process of system models. To achieve the adaptive outlier detection and processing in
the measurement signal, modified Kalman filter based on orthogonality of innovation serves as the parallel model
filters in the AIMM approach. The advantage of decentralized filter architecture of the latter federated algorithm
is flexibility and modularity. It has received considerable attention because of its outstanding fault detection and
isolation capability. Experiment results show that the proposed multi-sensor data fusion methodology significantly
improves the navigation estimation accuracy and reliability as compared to the federated extend Kalman filter and
federated IMM filter approaches."
Interacting multiple model, innovation correction, multi-sensor data fusion, SINS/GPS/odometer.