Self-adaptation pre-estimation Kalman filtering algorithm and system for INS/UWB pedestrian navigation with data missing

Abstract

The invention discloses a self-adaptation pre-estimation Kalman filtering algorithm for INS / UWB pedestrian navigation with data missing. According to the algorithm, a UWB system and an inertia navigation device INS system measure the distance between a reference node and a target node separately; on the basis, the difference is obtained from the distance information obtained through measurement ofthe two systems, and the difference is adopted as the observation amount of a filtering model adopted by a data fusion algorithm; on the basis, a traditional self-adaptation Kalman filtering algorithm is improved, a variable is introduced, the variable represents whether or not the distance information of an ith channel is available. Once the distance information is not available, the unavailabledistance information is estimated, the unavailable distance information is made up, and it is ensured that position errors are estimated by a filter; on the basis, the difference between the pedestrian position obtained by inertia navigation device INS measurement and error estimation of the position obtained by an EFIR filter, and finally the best pedestrian position estimation at the current moment is obtained.

Description

technical field [0001] The invention relates to the technical field of combined positioning in complex environments, in particular to an adaptive predictive Kalman filter algorithm and system for INS / UWB pedestrian navigation with missing data. 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 the influence of the indoor complex environment and ensu...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that UWB cannot obtain normal distance information due to the influence of the indoor environment in the real-time system, and proposes a kind of self-adaptive estimation Kalman filtering algorithm for INS / UWB pedestrian navigation with missing data and system, this method improves the traditional adaptive Kalman filter algorithm, and introduces variables Determine whether the i-th distance information is available, if the i-th distance information is not available, then Estimate the unavailable distance information to ensure the normal operation of the filter, and finally get the optimal pedestrian position estimation at the current moment

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