Multi-Source Fusion Adaptive Filtering Method Based on Quantitative Characterization of Observability

Abstract

A multi-source fusion adaptive filtering method based on the quantitative characterization of observability was proposed to address the problem of severe resource constraints of deep space probes and the difficulty of realizing autonomous navigation with multi-source heterogeneous data fusion. By constructing a variable-channel adaptive fusion structure，evaluating the observability degrees of the filter channel subsystems of each sensitizer on-line based on the quantitative characterization of system observability analysis，and flexibly configuring and dynamically adjusting the number of channels and weights of the source channels，adaptive fusion of multi-source heterogeneous information for autonomous operation of deep space probes was realized. Compared with the traditional pre-fusion structure，the dynamic adjustment of the filter structure with the change of the observability degrees of the measurement data not only solves the computational burden of the spatial-temporal alignment of heterogeneous information，but also avoids the influence of the degradation of the performance of a single sensitizer or subsystem on the fusion accuracy，and minimizes the complexity and redundancy of the filter structure. Through mathematical simulations，it has been verified that in the navigation processes of the long-distance approaching phase and the close-range landing phase，this method has basically the same navigation accuracy as traditional fusion methods. However，due to its ability to adaptively optimize the selection of measurement data and the filtering structure，the computational load is significantly reduced. This method can provide theoretical and technical support for autonomous navigation in deep space exploration.

Keywords

observability / multi-source fusion / autonomous navigation / adaptive filtering