GNSS/low-cost MEMS-INS integration using variational Bayesian adaptive cubature Kalman smoother and ensemble regularized ELM
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Publication:1666326
DOI10.1155/2015/682907zbMath1395.94032OpenAlexW2105165858WikidataQ59118915 ScholiaQ59118915MaRDI QIDQ1666326
Publication date: 27 August 2018
Published in: Mathematical Problems in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2015/682907
Inference from stochastic processes and prediction (62M20) Applications of statistics in engineering and industry; control charts (62P30) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Cites Work
- Cubature Kalman smoothers
- Trends in extreme learning machines: a review
- Ensembling neural networks: Many could be better than all
- An adaptive unscented Kalman filtering algorithm for MEMS/GPS integrated navigation systems
- Study of robust \(H_{\infty}\) filtering application in loosely coupled INS/GPS system
- Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
- The elements of statistical learning. Data mining, inference, and prediction
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