@techreport{Liu2018a,
  address = {{Davis, CA, USA}},
  type = {Technical},
  title = {Rowbot {{Final Design Report}}},
  abstract = {Hegemony Technologies has built an iPhone app called SwingRow to track the motion of a competition rowboat in order to provide athletes with specific information about their performance. Our team was tasked with improving the accuracy of the position and velocity measurements using exclusively the hardware built into an iPhone. The GPS only samples at 1 Hz, causing a loss of resolution, but represents a stable average value. The accelerometer data samples at 100 Hz but experiences integration drift. We used two different sensor-fusion methods to combine the respective strengths of these signals. The first is a Kalman filter which uses Bayesian statistics to estimate more accurate values of desired states in real-time. The second is a complementary filter, which is a post-processing approach that combines high-frequency signals from the accelerometer with the low-frequency measurements from the GPS. The complementary filter is slightly more accurate than the Kalman filter, but is not the desired design because it cannot operate in real-time. For this reason, the Kalman filter is recommended where real-time results are desired.},
  language = {en},
  institution = {{University of California, Davis}},
  author = {Liu, Ada and Tarien, Britt and Cloud, Bryn and Wang, Li and Shedd, Thomas},
  year = {2018},
  pages = {26},
  file = {/home/moorepants/Zotero/storage/6QJ9GN39/Liu et al. - Rowbot Final Design Report.pdf}
}

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