Sensor anomaly detection and recovery in a nonlinear autonomous ground vehicle model

Lampiri, E

Sensor anomaly detection and recovery in a nonlinear autonomous ground vehicle model

Lampiri, E

Permalink

Publication Type:: Conference Proceeding
Citation:: 2017 Asian Control Conference, ASCC 2017, 2018, 2018-January pp. 430 - 435
Issue Date:: 2018-02-07

Closed Access

	Filename	Description	Size
	08287208.pdf	Published version	517.85 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lampiri, E	en_US
dc.date.issued	2018-02-07	en_US
dc.identifier.citation	2017 Asian Control Conference, ASCC 2017, 2018, 2018-January pp. 430 - 435	en_US
dc.identifier.isbn	9781509015733	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126493
dc.description.abstract	© 2017 IEEE. This work considers the problem of sensor anomaly detection, estimation and recovery for an autonomous vehicle model (e.g. a self-driving car). The proposed algorithm employs particle filtering and maximum likelihood methods to detect and estimate the anomaly. The estimated anomaly is used to correct the sensor readings. The system is nonlinear and it is assumed that both the system model and sensor outputs are corrupted by noise (not necessarily Gaussian). Simulation results are presented highlighting the performance of the proposed method.	en_US
dc.relation.ispartof	2017 Asian Control Conference, ASCC 2017	en_US
dc.relation.isbasedon	10.1109/ASCC.2017.8287208	en_US
dc.title	Sensor anomaly detection and recovery in a nonlinear autonomous ground vehicle model	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. This work considers the problem of sensor anomaly detection, estimation and recovery for an autonomous vehicle model (e.g. a self-driving car). The proposed algorithm employs particle filtering and maximum likelihood methods to detect and estimate the anomaly. The estimated anomaly is used to correct the sensor readings. The system is nonlinear and it is assumed that both the system model and sensor outputs are corrupted by noise (not necessarily Gaussian). Simulation results are presented highlighting the performance of the proposed method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/126493