---
_id: '11723'
abstract:
- lang: eng
text: In this paper we present a novel vehicle tracking algorithm, which is based
on multi-level sensor fusion of GPS (global positioning system) with Inertial
Measurement Unit sensor data. It is shown that the robustness of the system to
temporary dropouts of the GPS signal, which may occur due to limited visibility
of satellites in narrow street canyons or tunnels, is greatly improved by sensor
fusion. We further demonstrate how the observation and state noise covariances
of the employed Kalman filters can be estimated alongside the filtering by an
application of the Expectation-Maximization algorithm. The proposed time-variant
multi-level Kalman filter is shown to outperform an Interacting Multiple Model
approach while at the same time being computationally less demanding.
author:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- first_name: Sven
full_name: Peschke, Sven
last_name: Peschke
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Bevermeier M, Peschke S, Haeb-Umbach R. Robust vehicle localization based
on multi-level sensor fusion and online parameter estimation. In: 6th Workshop
on Positioning Navigation and Communication (WPNC 2009). ; 2009:235-242. doi:10.1109/WPNC.2009.4907833'
apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Robust vehicle localization
based on multi-level sensor fusion and online parameter estimation. In 6th
Workshop on Positioning Navigation and Communication (WPNC 2009) (pp. 235–242).
https://doi.org/10.1109/WPNC.2009.4907833
bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle
localization based on multi-level sensor fusion and online parameter estimation},
DOI={10.1109/WPNC.2009.4907833},
booktitle={6th Workshop on Positioning Navigation and Communication (WPNC 2009)},
author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009},
pages={235–242} }'
chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Robust Vehicle
Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.”
In 6th Workshop on Positioning Navigation and Communication (WPNC 2009),
235–42, 2009. https://doi.org/10.1109/WPNC.2009.4907833.
ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization
based on multi-level sensor fusion and online parameter estimation,” in 6th
Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp.
235–242.
mla: Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level
Sensor Fusion and Online Parameter Estimation.” 6th Workshop on Positioning
Navigation and Communication (WPNC 2009), 2009, pp. 235–42, doi:10.1109/WPNC.2009.4907833.
short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning
Navigation and Communication (WPNC 2009), 2009, pp. 235–242.'
date_created: 2019-07-12T05:27:01Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/WPNC.2009.4907833
keyword:
- covariance matrices
- expectation-maximisation algorithm
- expectation-maximization algorithm
- global positioning system
- Global Positioning System
- GPS
- inertial measurement unit
- interacting multiple model approach
- Kalman filters
- multilevel sensor fusion
- narrow street canyons
- narrow tunnels
- online parameter estimation
- parameter estimation
- road vehicles
- robust vehicle localization
- sensor fusion
- state noise covariances
- time-variant multilevel Kalman filter
- vehicle tracking algorithm
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf
oa: '1'
page: 235-242
publication: 6th Workshop on Positioning Navigation and Communication (WPNC 2009)
status: public
title: Robust vehicle localization based on multi-level sensor fusion and online parameter
estimation
type: conference
user_id: '44006'
year: '2009'
...
---
_id: '11724'
abstract:
- lang: eng
text: In this paper we present a novel vehicle tracking method which is based on
multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman
filtering of GPS and IMU measurements the estimates of the orientation of the
vehicle are combined in an optimal manner to improve the robustness towards drift
errors. The tracking algorithm incorporates the estimation of time-variant covariance
parameters by using an iterative block Expectation-Maximization algorithm to account
for time-variant driving conditions and measurement quality. The proposed system
is compared to an interacting multiple model approach (IMM) and achieves improved
localization accuracy at lower computational complexity. Furthermore we show how
the joint parameter estimation and localizaiton can be conducted with streaming
input data to be able to track vehicles in a real driving environment.
author:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- first_name: Sven
full_name: Peschke, Sven
last_name: Peschke
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Bevermeier M, Peschke S, Haeb-Umbach R. Joint Parameter Estimation and Tracking
in a Multi-Stage Kalman Filter for Vehicle Positioning. In: IEEE 69th Vehicular
Technology Conference (VTC 2009 Spring). ; 2009:1-5. doi:10.1109/VETECS.2009.5073634'
apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Joint Parameter
Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.
In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) (pp. 1–5).
https://doi.org/10.1109/VETECS.2009.5073634
bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Joint Parameter
Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning},
DOI={10.1109/VETECS.2009.5073634},
booktitle={IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}, author={Bevermeier,
Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009}, pages={1–5} }'
chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Joint Parameter
Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.”
In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 1–5, 2009.
https://doi.org/10.1109/VETECS.2009.5073634.
ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Joint Parameter Estimation
and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning,” in IEEE
69th Vehicular Technology Conference (VTC 2009 Spring), 2009, pp. 1–5.
mla: Bevermeier, Maik, et al. “Joint Parameter Estimation and Tracking in a Multi-Stage
Kalman Filter for Vehicle Positioning.” IEEE 69th Vehicular Technology Conference
(VTC 2009 Spring), 2009, pp. 1–5, doi:10.1109/VETECS.2009.5073634.
short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: IEEE 69th Vehicular Technology
Conference (VTC 2009 Spring), 2009, pp. 1–5.'
date_created: 2019-07-12T05:27:02Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/VETECS.2009.5073634
keyword:
- computational complexity
- expectation-maximisation algorithm
- Global Positioning System
- inertial measurement unit
- inertial navigation
- interacting multiple model
- iterative block expectation-maximization algorithm
- Kalman filters
- multi-stage Kalman filter
- parameter estimation
- road vehicles
- vehicle positioning
- vehicle tracking
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf
oa: '1'
page: 1-5
publication: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)
status: public
title: Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for
Vehicle Positioning
type: conference
user_id: '44006'
year: '2009'
...