The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Double exponential smoothing: an alternative to Kalman filter-based predictive tracking
Abstract
We present novel algorithms for predictive tracking of user position and orientation based on double exponential smoothing. These algorithms, when compared against Kalman and extended Kalman filter-based predictors with derivative free measurement models, run approximately 135 times faster with equivalent prediction performance and simpler implementations. This paper describes these algorithms in detail along with the Kalman and extended Kalman Filter predictors tested against. In addition, we describe the details of a predictor experiment and present empirical results supporting the validity of our claims that these predictors are faster, easier to implement, and perform equivalently to the Kalman and extended Kalman filtering predictors.
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Citations
Cites
Forecasting, Structural Time Series Models and the Kalman Filter
Harvey, A. C. (1990) · doi:10.1017/cbo9781107049994 · Cited by 4,195
Cited by
1 € filter: a simple speed-based low-pass filter for noisy input in interactive systems
Casiez, G., Roussel, N., & Vogel, D. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208639 · Cited by 422
Metadata
- Authors
- LaViola, J. J.
- Venue
- Proceedings of the workshop on Virtual environments 2003
- Publisher
- ACM
- Published
- 2003-05-22
- Pages
- 199-206
- OpenAlex
- W2118294504
- Cited by
- 186
- References
- 1
- FWCI
- 2.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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- LaViola, J. J. (2003). Double exponential smoothing: an alternative to Kalman filter-based predictive tracking. Proceedings of the workshop on Virtual environments 2003. https://doi.org/10.1145/769953.769976
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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