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• W2597025528 abstract "The objective of this project is to improve the accuracy of the Global Positioning System (GPS) on the Brain Computer Interface (BCI) Wheelchair at California State University, Northridge. In optimal conditions, the GPS can have an accuracy of one or less meter radius around the wheelchair. These conditions require there to be no trees, buildings, or other objects obstructing the antennas reception of the signals from the GPS satellites. These conditions aren???t always available and there are times when there will be trees and buildings near the wheelchair. When the wheelchair is not in optimal conditions the accuracy degrades and goes above one meter radius around the wheelchair. This inaccuracy makes it harder for the navigation system to give the wheelchair good directions of where to go.The solution being proposed to improve the accuracy of the GPS is a Kalman filter. The other sensors available on the wheelchair that track the motion of the wheelchair are the motor encoders. The data from the motor encoders and the GPS data are inputted into the Kalman filter to output more accurate data for the location of the wheelchair. The motor encoders are not affected by the same elements as the GPS such as trees and buildings. The main element that will affect the motor encoders is the wheels loosing traction. This situation shouldn???t happen that often because the wheelchair is intended to stay on smooth ground such as cement sidewalks.A Kalman filter was developed to take GPS data and motor encoder data and calculate an estimated position. To test the Kalman filter the wheelchair was taken outside on predetermined routes to collect GPS data and motor encoder data. The data was then filtered by the Kalman filter and the data was analyzed. Several different methods of collecting data were taken. Some methods involved adding Gaussian white noise into the GPS data and using the Kalman filter to remove the added noise. Another method was to add noise in manually by blocking the GPS signal. The data analyzed in this report shows that the Kalman filter proposed is able to successfully filter out the noise in the position data from the GPS." @default.
• W2597025528 created "2017-03-23" @default.
• W2597025528 creator A5079277356 @default.
• W2597025528 date "2015-01-01" @default.
• W2597025528 modified "2023-09-24" @default.
• W2597025528 title "Localization using Kalman filter with GPS and wheel encoders on a BCI wheelchair" @default.
• W2597025528 hasPublicationYear "2015" @default.
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