Date of Award
12-2015
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Legacy Department
Electrical Engineering
Committee Member
Dr. Adam Hoover, Committee Chair
Committee Member
Dr. Ian Walker
Committee Member
Dr. Kumar Venayagamoorthy
Committee Member
Dr. Jacob Sorber
Abstract
This work considers the problem of filtering a system in which the dynamic noise occasionally has an impulse value that is an order of magnitude or more larger than its typical expected distribu-tion. This is particularly challenging when the ratio of measurement noise to typical dynamic noise is large enough that the impulse dynamic noise cannot be easily distinguished from a large random occurrence of measurement noise. A new filter model is proposed using a multiple model approach in which one of the models is an impulse. The implementation of the model is demonstrated in a Kalman filter framework. Simulation results show the improvement of the new filter over existing methods across a range of measurement, typical, and impulse dynamic noises. The filter is then ap-plied to three different problems: 2D human motion tracking using ultra-wideband (UWB) position measurements, power system state estimation on a coupled bus, and handling outlier measurement noise in UWB tracking. In each case the new filter demonstrates a 2-4% improvement over existing state-of-the-art techniques.
Recommended Citation
Kwon, Jungphil, "Filtering Impulses in Dynamic Noise in the Presence of Large Measurement Noise" (2015). All Dissertations. 1777.
https://tigerprints.clemson.edu/all_dissertations/1777