Date of Award
Doctor of Philosophy (PhD)
Dr. Adam Hoover, Committee Chair
Dr. Ian Walker
Dr. Kumar Venayagamoorthy
Dr. Jacob Sorber
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.
Kwon, Jungphil, "Filtering Impulses in Dynamic Noise in the Presence of Large Measurement Noise" (2015). All Dissertations. 1777.