Outlier robust filters and their multiple model extensions

Download

index.pdf

Date

2019

Author

Şahin Bozgan, İlknur

Metadata

Show full item record

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

Item Usage Stats

285
views

148
downloads

Kalman filter (KF), which is an algorithm that is utilized to estimate unknown variables based on noisy measurements, has been successfully employed in many applications such as navigation, control, signal processing and target tracking. It is the optimum Bayesian filter in terms of mean square error (MSE) for linear Gaussian state-space models (SSMs). However, in many real world applications, the performance of KF degrades due to the presence of outliers in noises. Motivated by this problem, several algorithms have been proposed to provide robustness towards outliers. In this thesis, existing outlier robust filters are investigated regarding their theoretical derivations, validity of their assumptions, and performances. Furthermore, multi-model extensions of the filters are derived and the merits of the algorithms are illustrated in simulations.

Subject Keywords

Algorithms., Keywords: Kalman Filter, Student’s-t Filter, Variational Bayesian, Interacting Multiple Model, Gaussian Distribution, Student’s-t Distribution, Heavy-Tailed Noise, Target Tracking, Inverse Wishart, Gamma-Gaussian, Outliers, Robustness.

URI

http://etd.lib.metu.edu.tr/upload/12624261/index.pdf
https://hdl.handle.net/11511/44185

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2012-05-17) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target s...
Multi-target tracking using passive doppler measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
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...
Multitarget tracking performance metric: deficiency aware subpattern assignment Oksuz, Kemal; CEMGİL, ALİ TAYLAN (2018-03-01) Multitarget tracking is a sequential estimation problem where conditioned on noisy sensor measurements, state variables of several targets need to be estimated recursively. In this study, the authors propose a novel performance measure for multitarget tracking named as Deficiency Aware Subpattern Assignment (DASA), that can be used to consistently compare algorithms in a broad spectrum of formulations ranging from conventional data association methods to random finite set based multitarget tracking algorith...

Citation Formats

İ. Şahin Bozgan, “Outlier robust filters and their multiple model extensions,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.