Abstract
This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.
Highlights
With the rapid development of electronics technologies, various sensors have been developed and applied to many engineering fields such as integrated navigation, target tracking, signal processing, and networked communications [1,2,3,4]
This methodology is in a two-level fusion structure: at the bottom level, an adaptive fading UKF (AFUKF) based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation (UT)-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation via fusion of local estimations
It has a two-level fusion structure: at the bottom level, an AFUKF based on the Mahalanobis distance is developed and serves as local filters to improve the robustness of local state estimations against process-modeling error; at the top level, according to the principle of linear minimum variance, the UT-based multi-sensor optimal data fusion for the case of N local filters is established to calculate the globally optimal state estimation by fusion of local estimations
Summary
With the rapid development of electronics technologies, various sensors have been developed and applied to many engineering fields such as integrated navigation, target tracking, signal processing, and networked communications [1,2,3,4]. It can achieve the globally optimal state estimation for multi-sensor nonlinear stochastic systems. It presents a novel multi-sensor optimal data fusion methodology based on AFUKF to address the UKF-MODF problems, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems This methodology is in a two-level fusion structure: at the bottom level, an AFUKF based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an UT-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation via fusion of local estimations. Simulations and practical experiments as well as comparison analysis with FKF, UKF-FKF and UKF-MODF have been conducted to comprehensively evaluate the performance of the proposed multi-sensor optimal fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation system
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