Abstract
In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the Gaussian distribution with time-varying mean value than to the Inv-Gamma or Inv-Wishart distribution found by Kullback–Leibler divergence. Therefore, we assumed the prior distribution of measurement noise covariance matrices as Gaussian, and calculated the Gaussian parameters by the black box variational inference method. Finally, we obtained the measurement noise covariance matrices by using the Gaussian parameters. The experimental results illustrate that the proposed algorithm performs better in resisting time-varying measurement noise than the existing Variational Bayesian adaptive filter.
Highlights
We have presented a novel anti-jamming technique for integration based on black box variational inference (BBVI)
Kullback–Leibler divergence (KLD) was used to prove that compared with Inv-Gamma or Inv-Wishart distributions, the Gaussian distribution with time-varying mean value is closer to the time-varying noise
To solve the problem that the VBAKF cannot be applied when the prior distribution and likelihood distribution are non-conjugate, we proposed the use of the BBVI method, which is based on stochastic optimization, instead of the variational inference (VI) method to estimate the time-varying measurement noise covariance matrix (MNCM)
Summary
The loosely integrated inertial navigation system (INS) and global navigation satellite system (GNSS) corrects INS errors and helps INS to complete navigation tasks by providing the velocity and position of the GNSS. Simo et al first proposed the variational Bayesian (VB) AKF (VBAKF) model to solve the time-varying noise problem [15]. In order to ensure the assumed approximate distribution was closer to the real distribution and estimate the MNCM more accurately, the Gaussian distribution was proposed as the prior distribution of MNCM in this paper This assumption leads to a non-conjugate problem between the prior distribution and likelihood distribution. For this reason, the VBAKF algorithm cannot be used to estimate MNCM. A novel anti-jamming technique for INS/GNSS integration based on black box variational inference was proposed. The proposed algorithm and existing VBAKF were applied to the problem of INS/GNSS integration with time-varying measurement noise.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
