Distributed seamless tight integration navigation method and system using LS-SVM-assisted EKF filtering method

Abstract

The invention discloses a distributed seamless tight integration navigation method and system using an LS-SVM-assisted EKF filtering method. The method comprises steps of obtaining a difference between the square of a distance between a reference node measured by INS and a target node and the square of a distance between a reference node measured by UWB and the target node as the observed quantityof a local filter; obtaining the local estimation of the target node by the local filter, and enabling a main filter to perform data fusion on a local estimation result, and finally obtaining the optimal state estimation of the target node. The method has the beneficial effects that the observed quantity of the local filter can also be correspondingly estimated in a UWB loss-locking process by the assistance of the LS-SVM, and the seamless estimation of a distributed filtering algorithm is realized.

Description

technical field [0001] The invention relates to the technical field of combined positioning in complex environments, in particular to a distributed seamless tight combined navigation method and system using LS-SVM (least square vector machine) assisted EKF (extended Kalman filter) filtering method. Background technique [0002] In recent years, Pedestrian Navigation (PN), as an emerging field of navigation technology application, is receiving more and more attention from scholars from all over the world, and has gradually become a research hotspot in this field. However, in indoor environments such as tunnels, large warehouses, and underground parking lots, factors such as weak external radio signals and strong electromagnetic interference will have a great impact on the accuracy, real-time performance, and robustness of target pedestrian navigation information acquisition. How to effectively integrate the limited information acquired in the indoor environment to eliminate t...

AI Technical Summary

Problems solved by technology

For example, UWB devices are required to provide pedestrian navigation information, which requires that the environment where the target pedestrian is located must be able to obtain at least three reference node information, which greatly reduces the application range of the integrated navigation model. The sub-technology completes the positioning independently, but also introduces new errors, which is not conducive to the improvement of the accuracy of integrated navigation technology

[0006] The existing technology proposes to apply the tight combination model to the field of indoor pedestrian navigation. The tight combination model directly applies the original sensor data of the sub-techniques involved in the combined navigation to the calculation of the final navigation information, reducing the new errors introduced by the sub-techniques’ self-calculation. However, it should be pointed out that the existing tight integrated navigation models all use a centralized model, which has poor system fault tolerance and is not conducive to the increasingly accurate and complex integrated navigation model.

In addition, the current research rarely considers the case where the observations of the distributed local filter are out of lock.

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