Version 1
: Received: 6 June 2024 / Approved: 7 June 2024 / Online: 7 June 2024 (14:37:47 CEST)
How to cite:
Ye, W.; Zhang, F.; Chen, H. Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application. Preprints2024, 2024060494. https://doi.org/10.20944/preprints202406.0494.v1
Ye, W.; Zhang, F.; Chen, H. Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application. Preprints 2024, 2024060494. https://doi.org/10.20944/preprints202406.0494.v1
Ye, W.; Zhang, F.; Chen, H. Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application. Preprints2024, 2024060494. https://doi.org/10.20944/preprints202406.0494.v1
APA Style
Ye, W., Zhang, F., & Chen, H. (2024). Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application. Preprints. https://doi.org/10.20944/preprints202406.0494.v1
Chicago/Turabian Style
Ye, W., Fubo Zhang and Hongmei Chen. 2024 "Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application" Preprints. https://doi.org/10.20944/preprints202406.0494.v1
Abstract
Airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units(IMUs), data-processing computer and Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing, such as array SAR, multinode imaging loads and so on. ADPOS can provide multinode high accuracy spatio-temporal reference information to realize multinode motion compensation with the various state estimation methods such as Central Difference Kalman Filtering (CDKF), modifed CDKF. However, these methods focus on noise optimization based on known nonlinear models and its linear minimum variance estimation criterion, which cannot gain better estimation performance for high-dimensional nonlinear systems. For this reason, the variational optimization process based on conjugate gradient algorithm is proposed for the estimation of filtering update step, which is more efficient than linear minimum variance estimation criterion. Therefore, aiming at ADPOS with high-dimensional and nonlinearity, Central Difference Variational Filtering (CDVF) using variational optimization based on conjugate gradient algorithm for mean correction in the filtering update stage is devoted to advancing the estimation accuracy of multinode motion parameters. A real ADPOS flight test has been conducted, the experimental results show that the accuracy of the slave motion parameters has a distinct progress than the up to date CDKF, and the compensation performance is distinct.
Keywords
Central difference Kalman filtering; Variational optimization; Conjugate gradient; Airborne distributed position and orientation system
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
