Adaptive model online reconstruction robust filtering method, device and system

Abstract

The embodiment of the invention discloses an adaptive model online reconstruction robust filtering method, device and system. The method comprises the steps that a model set, a Markov probability transfer matrix and the model probability of each model in the model set are initialized; obtaining an input state quantity and a state covariance matrix of each model; filtering and fusing the data information acquired by the integrated navigation system by using a robust filter, and outputting a state vector corresponding to each model; updating the model probability of each model; and mixing the covariance matrix of each model, the state vector corresponding to each model and the updated model probability to obtain the state vector and the state covariance matrix of the carrier. According to the method, the robust filter is utilized to perform filtering fusion on the integrated navigation system, so that adverse effects caused by measurement of outliers can be effectively resisted, and the total size of the interactive multi-model preset model set is reduced.

Description

technical field [0001] The invention relates to the field of information fusion, in particular to a robust filtering method, device and system for online reconstruction of an adaptive model. Background technique [0002] The INS / DVL (Inertial Navigation System / Doppler Velocimeter) integrated navigation system can provide continuous navigation information for autonomous underwater vehicles. A reasonable and effective INS / DVL data fusion algorithm is of great significance to improve the navigation and positioning accuracy. [0003] At present, most of the data fusion algorithms of INS / DVL integrated navigation system are based on the traditional Kalman filter framework. Theoretically, the Kalman filter can obtain the optimal estimation of the fusion state only under the condition that the structural parameters and noise statistical characteristic parameters of the stochastic dynamic system are accurately known. However, the changeable marine environment, such as changes in te...

AI Technical Summary

Problems solved by technology

However, when using an interactive multi-model algorithm, it is necessary to introduce as many models as possible into the system model set to ensure that the real working conditions can be accurately described at any time, otherwise the accuracy will decrease

However, as the number of models in the model set increases, the competition among the models will become fierce, which will affect the computational efficiency.

At the same time, the interactive multi-model has weak resistance to measurement outliers caused by non-Gaussian noise, and its robustness still has a lot of room for improvement.

How to balance navigation accuracy, robustness, and computational efficiency is an important but undeveloped research direction at present, which restricts the long-term operation ability of underwater robots

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