Computer Science – Information Theory
Scientific paper
2009-09-05
Computer Science
Information Theory
Scientific paper
We study the problem of optimal estimation and control of linear systems using quantized measurements, with a focus on applications over sensor networks. 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. We show that the conditional state density follows a, so called, generalized closed skew-normal (GCSN) distribution. We further show that for such systems the classical separation property between control and estimation holds and that the certainty equivalent control law is LQG optimal.
Hassibi Babak
Sukhavasi Ravi Teja
No associations
LandOfFree
The Kalman Like Particle Filter : Optimal Estimation With Quantized Innovations/Measurements does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with The Kalman Like Particle Filter : Optimal Estimation With Quantized Innovations/Measurements, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and The Kalman Like Particle Filter : Optimal Estimation With Quantized Innovations/Measurements will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-161132