ELEC-E8107 - Stochastic models, estimation and control D, Lecture, 6.9.2022-7.12.2022
Kurssiasetusten perusteella kurssi on päättynyt 07.12.2022 Etsi kursseja: ELEC-E8107
Lecture 4, Oct 4: COMPUTATIONAL ASPECTS OF ESTIMATION; INFORMATION FILTER; THE CONTINUOUS-TIME LINEAR STATE ESTIMATION FILTER
Suorituksen vaatimukset
COMPUTATIONAL ASPECTS OF ESTIMATION; Information Filter
COMPUTATIONAL ASPECTS OF ESTIMATION; Information Filter
Implementation of Linear Estimation
The Information filter implementation of Kalman filter
• carries out the recursive computation of the inverse of the covariance matrix.
• an alternative to the “standard“ Kalman filter formulation and is less demanding computationally for systems with dimension of
the measurement vector larger than that of the state.
• has the advantage that it allows the start-up of the estimation without an initial estimate, i.e., with a noniformative prior
• Suitable for distributed estimation for example in swarm robotics
THE CONTINUOUS-TIME LINEAR STATE ESTIMATION FILTER
The linear minimum mean square error (LMMSE) filter for this continuous time problem, known as the Kalman-Bucy filter
The duality of the LMMSE estimation with the linear-quadratic (LQ) control problem is discussed. These two problems have their
solutions determined by the same Riccati equation
The duality of the LMMSE estimation with the linear-quadratic (LQ) control problem is discussed. These two problems have their
solutions determined by the same Riccati equation