The square-root unscented Kalman filter for state and parameter-estimation

@article{vanderMerwe2001TheSU,
  title={The square-root unscented Kalman filter for state and parameter-estimation},
  author={Rudolph van der Merwe and Eric A. Wan},
  journal={2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)},
  year={2001},
  volume={6},
  pages={3461-3464 vol.6}
}
  • Rudolph van der Merwe, E. Wan
  • Published 7 May 2001
  • Mathematics
  • 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (eg, learning the weights of a neural network). The EKF applies the standard linear Kalman filter methodology to a linearization of the true nonlinear system. This approach is sub-optimal, and can easily… 

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