Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter

Abstract

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.

Conditions for Determining the Coordinates of a Moving Object on a Geophysical Landmark

“…В ряде работ, опубликованных в России и за рубежом, показано, что в навигации по геофизическим полям целесообразно применение фильтра Калмана с единственной точкой линеаризации [Степанов, 2003Sun et al, 2022]. В некоторых случаях это может оказаться неэффективным ввиду того, что апостериорная плотность оценки координат -полимодальна.…”

An Improved Gaussian Sum Extended Kalman Filter With Colored Noise for GNSS/SINS Tightly Coupled Positioning and Attitude Determination Systems

“…The fading filter employs a fading factor to optimize the filtering conditions. However, it can only handle colored process noise and cannot address colored measurement noise [36], [37]. A robust adaptive filter can effectively counteract the effects of colored process noise and colored measurement noise, but it requires redundant measurements [38].…”

A novel Gaussian sum quaternion constrained cubature Kalman filter algorithm for GNSS/SINS integrated attitude determination and positioning system

“…However, when the moving carrier generates a large disturbance, it is difficult for this kind of filter algorithm to distinguish between the model error and measurement noise, thus affecting the estimation results ( Song et al, 2022 ; Chen et al, 2023 ). The fading filter algorithm makes the algorithm meet the optimality through a fading factor, but this method is limited to dealing with non-Gaussian process noise only ( Sun et al, 2022 ; Wang et al, 2022 ). The robust adaptive filter algorithm can handle non-Gaussian noise in both process and measurement noise.…”