A Multi-Step Pseudo-Measurement Adaptive Kalman Filter Based on Filtering Performance Evaluation and Its Application in the INS/GNSS Navigation System

Author:

Wang Dapeng1ORCID,Zhang Hai12

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China

2. Science and Technology on Aircraft Control Laboratory, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China

Abstract

The objective of this paper is to tackle the issue of the degraded navigation accuracy of the inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system in urban applications, especially under complex environments. This study utilizes historical state estimates and proposes a multi-step pseudo-measurement adaptive Kalman filter (MPKF) algorithm based on the filter performance evaluation. First, taking advantage of the independence between INS and GNSS, the enhanced second-order mutual difference (SOMD) algorithm is utilized for estimating the noise variance of the GNSS, which is decoupled from the estimate error of state and used as a module for filter performance evaluation. Then, the construction of the proposed method is presented, together with the analysis of the noise variance of multi-step pseudo-measurement. Ultimately, the efficacy of the MPKF is confirmed through a real-world vehicle experiment involving a tightly-coupled INS/GNSS integrated navigation application, demonstrating a noteworthy enhancement in navigation precision within densely wooded and built-up areas. Compared to the standard EKF and enhanced redundant measurement-based adaptive Kalman filter (ERMAKF), the proposed algorithm improves the positioning accuracy by 48% and 34%, velocity accuracy by 50% and 35%, and attitude accuracy by 38% and 48%, respectively, in the urban building segment.

Funder

the National Key Research and Development Program of China

Publisher

MDPI AG

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