Enhanced Redundant Measurement-Based Kalman Filter for Measurement Noise Covariance Estimation in INS/GNSS Integration

Abstract

Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instability or covariance underestimation, especially when the GNSS measurement disturbances occur. In this paper, an enhanced redundant measurement-based AKF is developed to improve the filtering performance. The scheme is based on the mutual difference sequence derived from the redundant measurement of INS. By using the mutual difference sequence, the measurement noise covariance can be estimated without being affected by the inaccuracy estimates, hence avoiding the risk of filtering divergence. In addition, the kernel density estimation is used to estimate the GNSS measurement noise’s probability density to detect whether the Gaussian properties of the measurement noise are maintained. When the noise statistics are far from Gaussian distribution, the difference sequence will be modeled as an autoregressive process using the Burg’s method. The real variance of the difference sequence can then be updated relying on the autoregressive model in order to avoid the covariance underestimation. A field experiment was carried out to evaluate the performance of the proposed method. The test results demonstrate that the proposed method can effectively mitigate the GNSS measurement disturbances and improve the accuracy of the navigation solution.

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

“…The observation model is expressed by: ρ are the measurement noises. Usually, ∆ ion and ∆ trop can be calculated through the Klobuchar model and the troposphere delay model, respectively [28].…”

“…where P0 is calculated through (51) because, in this study, the state error model was adopted. We employed the standard extended KF (SKF), EIAKF [31], MCEKF [32], ERMAKF [28], and our proposed MPKF for comparative analysis. Among them, MCEKF is the improved EKF algorithm based on the maximum correntropy criterion.…”

“…For simplicity, please refer to [28] for the detailed change in GN R . The interval with the largest errors is between 40 s and 120 s. During this period, the GNSS signal quality sharply declines, and due to the limited robustness of the EKF and EIAKF against the outliers, the errors of these two algorithms are the highest.…”

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System

“…The rotating platform A rotates around the Y b axis at the angular velocity ω r1 and the rotating platform B rotates around the X S1 axis at the angular velocity ω r2 . Then the angular velocity output of the three-axes gyroscope without scale factor error coupling state is shown in Equation (8).…”

“…Among them, the redundant sensor-based random error measurement scheme and the RSSINS-based sensor constant error compensation scheme have significantly improved the navigation accuracy of guided artillery shells [5][6][7][8]. At the same time, considering the high impact and high rotary velocity of the shell, MEMS sensors have been widely used.…”