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Optimal State Estimation of a Levitated Nanoparticle
In this project, a Kalman filter will be implemented to estimate the time-varying state of a nanoparticle held in the focus of a laser beam. The state of the particle will then be used in a feedback loop to act back on the particle and to provide ultimate control over the nanoparticle's dynamics.
The position and velocity (the state X[k]) of the levitated nanoparticle are continuously being recorded using a combination of photodetectors. However, this measurement (Y[k]) is not ideal, mainly because of laser shot noise. Taking theoretical models of the particle’s motion into account we can improve our state estimation ( ^X[k]). Thus,
a measurement combined with a theoretical model can yield better information than a measurement alone. This is the essence of Kalman filtering.
The student(s) will implement and characterize a Kalman filter and integrate it into an existing vacuum-trapping experiment.
References:
[1] W. Wieczorek et al., Phys. Rev. Lett. 114, 223601 (2015).
The position and velocity (the state X[k]) of the levitated nanoparticle are continuously being recorded using a combination of photodetectors. However, this measurement (Y[k]) is not ideal, mainly because of laser shot noise. Taking theoretical models of the particle’s motion into account we can improve our state estimation ( ^X[k]). Thus, a measurement combined with a theoretical model can yield better information than a measurement alone. This is the essence of Kalman filtering. The student(s) will implement and characterize a Kalman filter and integrate it into an existing vacuum-trapping experiment. References: [1] W. Wieczorek et al., Phys. Rev. Lett. 114, 223601 (2015).
We are seeking highly-motivated individual with previous experience in electronics, control and information theory, and measurement techniques.
We are seeking highly-motivated individual with previous experience in electronics, control and information theory, and measurement techniques.
Martin Frimmer (mfrimmer@ethz.ch), Lukas Novotny (lnovotny@ethz.ch)
Martin Frimmer (mfrimmer@ethz.ch), Lukas Novotny (lnovotny@ethz.ch)