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- Publisher
- Emerald Publishing
- Copyright
- © Emerald Publishing Limited
- ISSN
- 2049-6427
- DOI
- 10.1108/ijius-10-2019-0056
- Publisher site
- See Article on Publisher Site
Abstract
There are shortcomings of modern methods of ensuring the stability of Kalman filtration in unmanned vehicles’ (UVs) navigation systems under the condition of a priori uncertainty of the dispersion matrix of measurement interference. First, it is the absence of strict criteria for the selection of adaptation coefficients in the calculation of the a posteriori covariance matrix. Secondly, it is the impossibility of adaptive estimation in real time from the condition of minimum covariance of the updating sequence due to the necessity of its preliminary calculation.Design/methodology/approachThis paper considers a new approach to the construction of the Kalman filter adaptation algorithm. The algorithm implements the possibility of obtaining an accurate adaptive estimation of navigation parameters for integrated UVs inertial-satellite navigation systems, using the correction of non-periodic and unstable inertial estimates by high-precision satellite measurements. The problem of adaptive estimation of the noise dispersion matrix of the meter in the Kalman filter can be solved analytically using matrix methods of linear algebra. A numerical example illustrates the effectiveness of the procedure for estimating the state vector of the UVs’ navigation systems.FindingsAdaptive estimation errors are sharply reduced in comparison with the traditional scheme to the range from 2 to 7 m in latitude and from 1.5 to 4 m in longitude.Originality/valueThe simplicity and accuracy of the proposed algorithm provide the possibility of its effective application to the widest class of UVs’ navigation systems.
Journal
International Journal of Intelligent Unmanned Systems – Emerald Publishing
Published: Oct 12, 2021
Keywords: Unmanned vehicles; Adaptive estimation; Measurement noise dispersion matrix; Kalman filter; Inertial-satellite navigation systems; Non-periodic accurate observations