A Student s t filter for heavy tailed process and measurement noise

Download

index.pdf

Date

2013-05-26

Author

Roth, Michael
Özkan, Emre
Gustafsson, Fredrik

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

172
views

172
downloads

We consider the filtering problem in linear state space models with heavy tailed process and measurement noise. Our work is based on Student's t distribution, for which we give a number of useful results. The derived filtering algorithm is a generalization of the ubiquitous Kalman filter, and reduces to it as special case. Both Kalman filter and the new algorithm are compared on a challenging tracking example where a maneuvering target is observed in clutter.

Subject Keywords

Student's t distribution, Kalman filter, Robustness, Outliers

URI

https://hdl.handle.net/11511/39196

DOI

https://doi.org/10.1109/icassp.2013.6638770

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...
An adaptive PHD filter for tracking with unknown sensor characteristics Zhao, Yuxin; Yin, Feng; Gunnarsson, Fredrik; Amirijoo, Mehdi; Özkan, Emre; Gustafsson, Fredrik (2013-07-09) The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach for the case of unknown noise distribution parameters that avoid both aforementioned problem. First, the standard approach of augmenting the state vector w...
Exact kalman filtering of respiratory motion Çetinkaya, Mehmet; Erkmen, Aydan Müşerref (2018-10-01) In this paper we propose a novel Exact Kalman Filter for state estimation of quasi-periodic signals such as respiratory motion. Nonlinear functions of interest are approximations as truncated Fourier series. Instead of relying on approximations provided by Extended Kalman Filter or Unscented Kalman Filter, our filter performs exact calculation of the mean and covariances of interest. We then compare, through simulations, the performance of our filter to the two. Our results show that the theoretically deriv...

Citation Formats

M. Roth, E. Özkan, and F. Gustafsson, “A Student s t filter for heavy tailed process and measurement noise,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39196.