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Robust Particle Filter by Dynamic Averaging of Multiple Noise Models

Abstract: 

State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable. To this end, a robust particle filter (PF) algorithm is proposed, in which the heavier tailed Student’s t distributions are employed together with the Gaussian distribution to model the measurement noise. The effect of each model is automatically and
dynamically adjusted via a Bayesian model averaging mechanism. The validity of the proposed algorithm is evaluated by illustrative simulations.

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Paper Details

Authors:
Submitted On:
11 March 2017 - 11:15am
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Type:
Presentation Slides
Event:
Presenter's Name:
Bin Liu
Document Year:
2017
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ICASSP_Liu_Bin.pdf

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[1] , "Robust Particle Filter by Dynamic Averaging of Multiple Noise Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1743. Accessed: Oct. 22, 2017.
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url = {http://sigport.org/1743},
author = { },
publisher = {IEEE SigPort},
title = {Robust Particle Filter by Dynamic Averaging of Multiple Noise Models},
year = {2017} }
TY - EJOUR
T1 - Robust Particle Filter by Dynamic Averaging of Multiple Noise Models
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PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1743
ER -
. (2017). Robust Particle Filter by Dynamic Averaging of Multiple Noise Models. IEEE SigPort. http://sigport.org/1743
, 2017. Robust Particle Filter by Dynamic Averaging of Multiple Noise Models. Available at: http://sigport.org/1743.
. (2017). "Robust Particle Filter by Dynamic Averaging of Multiple Noise Models." Web.
1. . Robust Particle Filter by Dynamic Averaging of Multiple Noise Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1743