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Published January 2013 | public
Journal Article

The Kalman-Like Particle Filter: Optimal Estimation With Quantized Innovations/Measurements

Abstract

We study the problem of optimal estimation and control of linear systems using quantized measurements. We show that the state conditioned on a causal quantization of the measurements 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.

Additional Information

© 2012 IEEE. Manuscript received January 16, 2012; revised May 30, 2012 and September 03, 2012; accepted October 09, 2012. Date of publication October 23, 2012; date of current version December 11, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Minyue Fu. This work was supported in part by the National Science Foundation by 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.

Additional details

Created:
August 19, 2023
Modified:
March 5, 2024