Recursive Sparse Estimation using a Gaussian Sum Filter

Lachlan Blackhall (The Australian National University)

DATE: 2009-11-13
TIME: 11:00:00 - 12:00:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU

ABSTRACT:
In many linear regression problems the number of possible explanatory parameters is often much greater than the number necessary to explain the observations. This corresponds to a sparse solution of the estimation problem and is of significant interest as a high degree of sparsity corresponds to simpler models. This seminar will detail recent investigations into a sparse estimator that can be implemented recursively, like the Kalman filter, while systematically producing appropriately sparse parameter estimates. The relationship between this work and compressive sensing will also be outlined.

BIO:
Lachlan Blackhall was born and grew up in Sydney, Australia. He received a Bachelor of Engineering (Aerospace) and a Bachelor of Science from the University of Sydney in 2007. He has previously worked for the Australian Centre for Field Robotics and as an electric propulsion engineer for Alta Space in Pisa, Italy. He is currently a PhD student in the Systems and Control Group in the Research School of Information Sciences and Engineering at the Australian National University where he is pursuing research on dynamical network theory, fault tolerance and diagnosis for networked systems and systematic complexity reduction.

MEDIA:
RSISE_Seminar_131109.pdf
video.html

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