Abstract
In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper puts forth approaches to render tracking of large-scale dynamic processes affordable, by processing a reduced number of data. Two distinct methods are introduced for reducing the number of data involved per time step. The first method builds on reduction using low-complexity random projections, while the second performs censoring for data-adaptive measurement selection. Simulations on synthetic data, compare the proposed methods with competing alternatives, and corroborate their efficacy in terms of estimation accuracy over complexity reduction.
Original language | English (US) |
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Title of host publication | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6195-6199 |
Number of pages | 5 |
ISBN (Electronic) | 9781479999880 |
DOIs | |
State | Published - May 18 2016 |
Event | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: Mar 20 2016 → Mar 25 2016 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2016-May |
ISSN (Print) | 1520-6149 |
Other
Other | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
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Country/Territory | China |
City | Shanghai |
Period | 3/20/16 → 3/25/16 |
Bibliographical note
Funding Information:Work in this paper was supported by the MURI AFOSR FA9550-10-1-0567, NIH Grant No. 1R01GM104975-01, and NSF grants 1343860, 1442686, 1500713 and 1514056
Publisher Copyright:
© 2016 IEEE.
Keywords
- Kalman filter
- censoring
- dimensionality reduction
- random projections
- tracking