On improving the performance of the Gauss-Newton filter

The Gauss-Newton filter is a tracking filter developed by Norman Morrison around the same time as the celebrated Kalman filter. It received little attention, primarily due to the computation requirements at the time. Today computers have vast processing capacity and computation is no-longer an issue. The filter finite memory length is identified as the key element in the Gauss-Newton filter adaptability and robustness. This thesis focuses on improving the performance of the Gauss-Newton. We incorporate the process noise statistics into the filter algorithm to obtain a filter which explains the error covariance inconsistency of the Kalaman filter. In addition, a biased version of the linear Gauss-Newton filter, with lower mean squared error than the unbiased filter, is proposed. Furthermore the Gauss-Newton filter is adapted using the Levenberg Marquardt method for improved convergence. In order to improve the computation requirements, a recursive version of the filter is obtained.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

2013

Author dc:creator

• Nadjiasngar, Roaldje

Advisor dc:contributor.advisor

• Inggs, Michael

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/5142

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/5142