ML Estimation of Process Noise Variance in Dynamic Systems

Date

2011-08-28

Author

Axelsson, Patrik
Orguner, Umut
Gustafsson, Fredrik
Norrlöf, Mikael

Metadata

Show full item record

Item Usage Stats

159
views

0
downloads

The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance Q is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and Q based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.

Subject Keywords

Robotic manipulators, Extended Kalman filters, Smoothing filters, Identification, Maximum likelihood, Covariance matrices

URI

https://hdl.handle.net/11511/73788
https://www.sciencedirect.com/science/article/pii/S1474667016445003?via%3Dihub

DOI

https://doi.org/10.3182/20110828-6-IT-1002.00543

Conference Name

18th IFAC World Congress

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Non-linear filtering based on observations from Gaussian processes Gustafsson, Fredrik; Saha, Saikat; Orguner, Umut (2011-03-12) We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a fi...
Robust Attitude Estimation Using IMU-Only Measurements Candan, Batu; Söken, Halil Ersin (2021-01-01) © 1963-2012 IEEE.This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning proce...
MULTI MODE PROJECTILE TRACKING WITH MARGINALIZED PARTICLE FILTER Bilgin, Ozan Ozgun; Demirekler, Mübeccel (2015-10-30) In this study, dynamic models for thrusting and ballistic flight modes of multi mode projectile are obtained and Marginalization method is applied by separation of the linear and nonlinear parts of state space model. In Marginalized Particle Filter (MPF), dimension of the nonlinear system is reduced so that the model can be utilized to obtain better estimates of the state using the same number of particles as that of standard particle filter. The Extended Kalman Filter (EKF), the Particle Filter (PF) and th...
Dimensional Design of Dual Mode Waveguide Multiplexers Boyacioglu, Gokhan; Alicioglu, Bulent; Yıldırım, Nevzat (2018-11-02) In this paper, a systematic design procedure is described for manifold type multiplexers formed by the classical circular dual mode waveguide filters. The design procedure starts with cascade synthesis of lumped quadruplet version of the filters with inverter couplings. Then the LC resonators are converted into SC transmission line (SCTL) resonators. The whole circuit is then impedance scaled by inverters at ports for realization of resonators as waveguides with desired cross-sectional dimensions thus formi...
Design of attitude estimation algorithms for inertial sensors only measurement scenarios Candan, Batu; Söken, Halil Ersin; Department of Aerospace Engineering (2022-3-24) This thesis proposes four novel robust Kalman filter algorithms for attitude estimation using only the measurements of an inertial measurement unit. Efficiency and optimality of the Kalman filter based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the inertial measurement unit-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain ro...

Citation Formats

P. Axelsson, U. Orguner, F. Gustafsson, and M. Norrlöf, “ML Estimation of Process Noise Variance in Dynamic Systems,” Milan, İtalya, 2011, vol. 44, p. 5609, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73788.