The Kalman Like Particle Filter: Optimal Estimation With Quantized Innovations/Measurements
- Creators
- Sukhavasi, Ravi Teja
- Hassibi, Babak
Abstract
We study the problem of optimal estimation using quantized innovations, with application to distributed estimation over sensor networks. We show that the state probability density conditioned on the quantized innovations can be expressed as the sum of a Gaussian random vector and a certain truncated Gaussian vector. This structure bears close resemblance to the full information Kalman filter and so allows us to effectively combine the Kalman structure with a particle filter to recursively compute the state estimate. We call the resulting filter the Kalman like particle filter (KLPF) and observe that it delivers close to optimal performance using far fewer particles than that of a particle filter directly applied to the original problem. We also note that the conditional state density follows a, so called, generalized closed skew-normal (GCSN) distribution.
Additional Information
© 2009 IEEE. This work was supported in part by the National Science Foundation under grants CCF-0729203, CNS-0932428 and CCF-1018927, by the Office of Naval Research under the MURI grant N00014-08-1-0747, and by Caltech's Lee Center for Advanced Networking.Attached Files
Published - 05400517.pdf
Submitted - 0909.0996.pdf
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Additional details
- Eprint ID
- 54346
- Resolver ID
- CaltechAUTHORS:20150204-072431118
- NSF
- CCF-0729203
- NSF
- CNS-0932428
- NSF
- CCF-1018927
- Office of Naval Research (ONR)
- N00014-08-1-0747
- Caltech Lee Center for Advanced Networking
- Created
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2015-02-05Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field