State Estimation in Distributed Parameter Systems via Least Squares and Invariant Embedding
Estimation of states in noisy dynamical systems is a problem whose solution is of significant importance in various scientific disciplines. Algorithms for filtering, smoothing and prediction estimates of lumped parameter system states have been derived by Kalman and Bucy , Bryson and Frazier [l], Cox , and Detchmendy and Sridhar . The techniques utilized for generating these algorithms include orthogonal projection theory , maximum likelihood estimate , and the classical least squares error criterion combined with an invariant embedding technique .
Journal of Mathematical Analysis and Applications
Lamont, G. B., & Kumar, K. S. . (1972). State estimation in distributed parameter systems via least squares and invariant embedding. Journal of Mathematical Analysis and Applications, 38(3), 588–606. https://doi.org/10.1016/0022-247X(72)90070-4