---
_id: '34171'
abstract:
- lang: eng
text: State estimation when only a partial model of a considered system is available
remains a major challenge in many engineering fields. This work proposes a joint,
square-root unscented Kalman filter to estimate states and model uncertainties
simultaneously by linear combinations of physics-motivated library functions.
Using a sparsity promoting approach, a selection of those linear combinations
is chosen and thus an interpretable model can be extracted. Results indicate a
small estimation error compared to a traditional square-root unscented Kalman
filter and exhibit the enhancement of physically meaningful models.
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Timmermann J. Estimating States and Model Uncertainties Jointly
by a Sparsity Promoting UKF. In: 12th IFAC Symposium on Nonlinear Control Systems
(NOLCOS 2022). Vol 56. ; 2023:85-90. doi:https://doi.org/10.1016/j.ifacol.2023.02.015'
apa: Götte, R.-S., & Timmermann, J. (2023). Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 56(1), 85–90. https://doi.org/10.1016/j.ifacol.2023.02.015
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={https://doi.org/10.1016/j.ifacol.2023.02.015},
number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS
2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90}
}'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” In 12th IFAC Symposium
on Nonlinear Control Systems (NOLCOS 2022), 56:85–90, 2023. https://doi.org/10.1016/j.ifacol.2023.02.015.
ieee: 'R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF,” in 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90,
doi: https://doi.org/10.1016/j.ifacol.2023.02.015.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” 12th IFAC Symposium on
Nonlinear Control Systems (NOLCOS 2022), vol. 56, no. 1, 2023, pp. 85–90,
doi:https://doi.org/10.1016/j.ifacol.2023.02.015.
short: 'R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 2023, pp. 85–90.'
conference:
end_date: 2023-01-06
location: Canberra, Australien
name: 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022
start_date: 2023-01-04
date_created: 2022-12-01T07:17:00Z
date_updated: 2024-11-13T08:43:05Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2023.02.015
intvolume: ' 56'
issue: '1'
keyword:
- joint estimation
- unscented transform
- Kalman filter
- sparsity
- data-driven
- compressed sensing
language:
- iso: eng
page: 85-90
publication: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)
quality_controlled: '1'
status: public
title: Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '44326'
abstract:
- lang: eng
text: "Low-quality models that miss relevant dynamics lead to major challenges in
modelbased\r\nstate estimation. We address this issue by simultaneously estimating
the system’s states\r\nand its model inaccuracies by a square root unscented Kalman
filter (SRUKF). Concretely,\r\nwe augment the state with the parameter vector
of a linear combination containing suitable\r\nfunctions that approximate the
lacking dynamics. Presuming that only a few dynamical terms\r\nare relevant, the
parameter vector is claimed to be sparse. In Bayesian setting, properties like\r\nsparsity
are expressed by a prior distribution. One common choice for sparsity is a Laplace\r\ndistribution.
However, due to disadvantages of a Laplacian prior in regards to the SRUKF,\r\nthe
regularized horseshoe distribution, a Gaussian that approximately features sparsity,
is\r\napplied instead. Results exhibit small estimation errors with model improvements
detected by\r\nan automated model reduction technique."
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Timmermann J. Approximating a Laplacian Prior for Joint State and
Model Estimation within an UKF. In: IFAC-PapersOnLine. Vol 56. ; 2023:869-874.'
apa: Götte, R.-S., & Timmermann, J. (2023). Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine,
56(2), 869–874.
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Approximating a Laplacian
Prior for Joint State and Model Estimation within an UKF}, volume={56}, number={2},
booktitle={IFAC-PapersOnLine}, author={Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2023}, pages={869–874} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian
Prior for Joint State and Model Estimation within an UKF.” In IFAC-PapersOnLine,
56:869–74, 2023.
ieee: R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint
State and Model Estimation within an UKF,” in IFAC-PapersOnLine, Yokohama,
Japan, 2023, vol. 56, no. 2, pp. 869–874.
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF.” IFAC-PapersOnLine,
vol. 56, no. 2, 2023, pp. 869–74.
short: 'R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.'
conference:
end_date: 2023-07-14
location: Yokohama, Japan
name: 22nd IFAC World Congress
start_date: 2023-07-09
date_created: 2023-05-02T15:16:43Z
date_updated: 2024-11-13T08:42:37Z
department:
- _id: '153'
- _id: '880'
intvolume: ' 56'
issue: '2'
keyword:
- joint estimation
- unscented Kalman filter
- sparsity
- Laplacian prior
- regularized horseshoe
- principal component analysis
language:
- iso: eng
page: 869-874
publication: IFAC-PapersOnLine
quality_controlled: '1'
status: public
title: Approximating a Laplacian Prior for Joint State and Model Estimation within
an UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '25046'
abstract:
- lang: eng
text: While increasing digitalization enables multiple advantages for a
reliable operation of technical systems, a remaining challenge in the context
of condition monitoring is seen in suitable consideration of uncertainties affecting
the monitored system. Therefore, a suitable prognostic approach to predict the
remaining useful lifetime of complex technical systems is required. To handle
different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based
prognostic approach is developed and evaluated by the use case of rubber-metal-elements.
These elements are maintained preventively due to the strong influence of uncertainties
on their behavior. In this paper, two measurement quantities are compared concerning
their ability to establish a prediction of the remaining useful lifetime of the
monitored elements and the influence of present uncertainties. Based on three
performance indices, the results are evaluated. A comparison with predictions
of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle
Filter. Finally, the value of the developed method for enabling condition monitoring
of technical systems related to uncertainties is given exemplary by a comparison
between the preventive and the predictive maintenance strategy for the use case.
article_number: '210'
article_type: original
author:
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
citation:
ama: Bender A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider
Uncertainties in RUL Predictions. Machines. 2021;9(10). doi:10.3390/machines9100210
apa: Bender, A. (2021). A Multi-Model-Particle Filtering-Based Prognostic Approach
to Consider Uncertainties in RUL Predictions. Machines, 9(10), Article
210. https://doi.org/10.3390/machines9100210
bibtex: '@article{Bender_2021, title={A Multi-Model-Particle Filtering-Based Prognostic
Approach to Consider Uncertainties in RUL Predictions}, volume={9}, DOI={10.3390/machines9100210},
number={10210}, journal={Machines}, author={Bender, Amelie}, year={2021} }'
chicago: Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach
to Consider Uncertainties in RUL Predictions.” Machines 9, no. 10 (2021).
https://doi.org/10.3390/machines9100210.
ieee: 'A. Bender, “A Multi-Model-Particle Filtering-Based Prognostic Approach to
Consider Uncertainties in RUL Predictions,” Machines, vol. 9, no. 10, Art.
no. 210, 2021, doi: 10.3390/machines9100210.'
mla: Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach
to Consider Uncertainties in RUL Predictions.” Machines, vol. 9, no. 10,
210, 2021, doi:10.3390/machines9100210.
short: A. Bender, Machines 9 (2021).
date_created: 2021-09-27T07:07:58Z
date_updated: 2022-11-03T11:42:46Z
department:
- _id: '151'
doi: 10.3390/machines9100210
intvolume: ' 9'
issue: '10'
keyword:
- prognostics
- RUL predictions
- particle filter
- uncertainty consideration
- Multi-Model-Particle Filter
- model-based approach
- rubber-metal-elements
- predictive maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.mdpi.com/2075-1702/9/10/210
oa: '1'
publication: Machines
publication_identifier:
issn:
- 2075-1702
publication_status: published
quality_controlled: '1'
status: public
title: A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties
in RUL Predictions
type: journal_article
user_id: '54290'
volume: 9
year: '2021'
...
---
_id: '17652'
author:
- first_name: Gleb
full_name: Polevoy, Gleb
id: '83983'
last_name: Polevoy
- first_name: Stojan
full_name: Trajanovski, Stojan
last_name: Trajanovski
- first_name: Paola
full_name: Grosso, Paola
last_name: Grosso
- first_name: Cees
full_name: de Laat, Cees
last_name: de Laat
citation:
ama: 'Polevoy G, Trajanovski S, Grosso P, de Laat C. Filtering Undesirable Flows
in Networks. In: Combinatorial Optimization and Applications: 11th International
Conference, COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part
I. Lecture Notes in Computer Science. Cham: Springer International Publishing;
2017:3-17. doi:10.1007/978-3-319-71150-8_1'
apa: 'Polevoy, G., Trajanovski, S., Grosso, P., & de Laat, C. (2017). Filtering
Undesirable Flows in Networks. In Combinatorial Optimization and Applications:
11th International Conference, COCOA 2017, Shanghai, China, December 16-18, 2017,
Proceedings, Part I (pp. 3–17). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-71150-8_1'
bibtex: '@inproceedings{Polevoy_Trajanovski_Grosso_de Laat_2017, place={Cham}, series={Lecture
Notes in Computer Science}, title={Filtering Undesirable Flows in Networks}, DOI={10.1007/978-3-319-71150-8_1},
booktitle={Combinatorial Optimization and Applications: 11th International Conference,
COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part I}, publisher={Springer
International Publishing}, author={Polevoy, Gleb and Trajanovski, Stojan and Grosso,
Paola and de Laat, Cees}, year={2017}, pages={3–17}, collection={Lecture Notes
in Computer Science} }'
chicago: 'Polevoy, Gleb, Stojan Trajanovski, Paola Grosso, and Cees de Laat. “Filtering
Undesirable Flows in Networks.” In Combinatorial Optimization and Applications:
11th International Conference, COCOA 2017, Shanghai, China, December 16-18, 2017,
Proceedings, Part I, 3–17. Lecture Notes in Computer Science. Cham: Springer
International Publishing, 2017. https://doi.org/10.1007/978-3-319-71150-8_1.'
ieee: 'G. Polevoy, S. Trajanovski, P. Grosso, and C. de Laat, “Filtering Undesirable
Flows in Networks,” in Combinatorial Optimization and Applications: 11th International
Conference, COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part
I, 2017, pp. 3–17.'
mla: 'Polevoy, Gleb, et al. “Filtering Undesirable Flows in Networks.” Combinatorial
Optimization and Applications: 11th International Conference, COCOA 2017, Shanghai,
China, December 16-18, 2017, Proceedings, Part I, Springer International Publishing,
2017, pp. 3–17, doi:10.1007/978-3-319-71150-8_1.'
short: 'G. Polevoy, S. Trajanovski, P. Grosso, C. de Laat, in: Combinatorial Optimization
and Applications: 11th International Conference, COCOA 2017, Shanghai, China,
December 16-18, 2017, Proceedings, Part I, Springer International Publishing,
Cham, 2017, pp. 3–17.'
date_created: 2020-08-06T15:19:48Z
date_updated: 2022-01-06T06:53:16Z
department:
- _id: '63'
- _id: '541'
doi: 10.1007/978-3-319-71150-8_1
extern: '1'
keyword:
- flow
- filter
- MMSA
- set cover
- approximation
- local ratio algorithm
language:
- iso: eng
page: 3-17
place: Cham
publication: 'Combinatorial Optimization and Applications: 11th International Conference,
COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part I'
publication_identifier:
isbn:
- 978-3-319-71150-8
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Filtering Undesirable Flows in Networks
type: conference
user_id: '83983'
year: '2017'
...
---
_id: '9879'
abstract:
- lang: eng
text: Application of prognostics and health management (PHM) in the field of Proton
Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing
the reliability and availability of these systems. Though a lot of work is currently
being conducted to develop PHM systems for fuel cells, various challenges have
been encountered including the self-healing effect after characterization as well
as accelerated degradation due to dynamic loading, all which make RUL predictions
a difficult task. In this study, a prognostic approach based on adaptive particle
filter algorithm is proposed. The novelty of the proposed method lies in the introduction
of a self-healing factor after each characterization and the adaption of the degradation
model parameters to fit to the changing degradation trend. An ensemble of five
different state models based on weighted mean is then developed. The results show
that the method is effective in estimating the remaining useful life of PEM fuel
cells, with majority of the predictions falling within 5\% error. The method was
employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the
winner of the RUL category of the challenge.
author:
- first_name: 'James Kuria '
full_name: 'Kimotho, James Kuria '
last_name: Kimotho
- first_name: Tobias
full_name: Meyer, Tobias
last_name: Meyer
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter
with model parameter adaptation. In: Prognostics and Health Management (PHM),
2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406'
apa: Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics
using particle filter with model parameter adaptation. In Prognostics and Health
Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
bibtex: '@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics
using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406},
booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on},
author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014},
pages={1–6} }'
chicago: Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell
Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics
and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406.
ieee: J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle
filter with model parameter adaptation,” in Prognostics and Health Management
(PHM), 2014 IEEE Conference on, 2014, pp. 1–6.
mla: Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter
with Model Parameter Adaptation.” Prognostics and Health Management (PHM),
2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406.
short: 'J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management
(PHM), 2014 IEEE Conference On, 2014, pp. 1–6.'
date_created: 2019-05-20T13:11:02Z
date_updated: 2019-05-20T13:12:27Z
department:
- _id: '151'
doi: 10.1109/ICPHM.2014.7036406
keyword:
- ageing
- particle filtering (numerical methods)
- proton exchange membrane fuel cells
- remaining life assessment
- PEM fuel cell prognostics
- PHM
- RUL predictions
- accelerated degradation
- adaptive particle filter algorithm
- dynamic loading
- model parameter adaptation
- prognostics and health management
- proton exchange membrane fuel cells
- remaining useful life estimation
- self-healing effect
- Adaptation models
- Data models
- Degradation
- Estimation
- Fuel cells
- Mathematical model
- Prognostics and health management
language:
- iso: eng
page: 1-6
publication: Prognostics and Health Management (PHM), 2014 IEEE Conference on
status: public
title: PEM fuel cell prognostics using particle filter with model parameter adaptation
type: conference
user_id: '55222'
year: '2014'
...
---
_id: '11726'
abstract:
- lang: eng
text: In this paper we present a robust location estimation algorithm especially
focused on the accuracy in vertical position. A loosely-coupled error state space
Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the
output of a Global Positioning System device, is augmented by height information
from an altitude measurement unit. This unit consists of a barometric altimeter
whose output is fused with topographic map information by a Kalman filter to provide
robust information about the current vertical user position. These data replace
the less reliable vertical position information provided the GPS device. It is
shown that typical barometric errors like thermal divergences and fluctuations
in the pressure due to changing weather conditions can be compensated by the topographic
map information and the barometric error Kalman filter. The resulting height information
is shown not only to be more reliable than height information provided by GPS.
It also turns out that it leads to better attitude and thus better overall localization
estimation accuracy due to the coupling of spatial orientations via the Direct
Cosine Matrix. Results are presented both for artificially generated and field
test data, where the user is moving by car.
author:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- 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, Walter O, Peschke S, Haeb-Umbach R. Barometric height estimation
combined with map-matching in a loosely-coupled Kalman-filter. In: 7th Workshop
on Positioning Navigation and Communication (WPNC 2010). ; 2010:128-134. doi:10.1109/WPNC.2010.5650745'
apa: Bevermeier, M., Walter, O., Peschke, S., & Haeb-Umbach, R. (2010). Barometric
height estimation combined with map-matching in a loosely-coupled Kalman-filter.
In 7th Workshop on Positioning Navigation and Communication (WPNC 2010)
(pp. 128–134). https://doi.org/10.1109/WPNC.2010.5650745
bibtex: '@inproceedings{Bevermeier_Walter_Peschke_Haeb-Umbach_2010, title={Barometric
height estimation combined with map-matching in a loosely-coupled Kalman-filter},
DOI={10.1109/WPNC.2010.5650745},
booktitle={7th Workshop on Positioning Navigation and Communication (WPNC 2010)},
author={Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach,
Reinhold}, year={2010}, pages={128–134} }'
chicago: Bevermeier, Maik, Oliver Walter, Sven Peschke, and Reinhold Haeb-Umbach.
“Barometric Height Estimation Combined with Map-Matching in a Loosely-Coupled
Kalman-Filter.” In 7th Workshop on Positioning Navigation and Communication
(WPNC 2010), 128–34, 2010. https://doi.org/10.1109/WPNC.2010.5650745.
ieee: M. Bevermeier, O. Walter, S. Peschke, and R. Haeb-Umbach, “Barometric height
estimation combined with map-matching in a loosely-coupled Kalman-filter,” in
7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010,
pp. 128–134.
mla: Bevermeier, Maik, et al. “Barometric Height Estimation Combined with Map-Matching
in a Loosely-Coupled Kalman-Filter.” 7th Workshop on Positioning Navigation
and Communication (WPNC 2010), 2010, pp. 128–34, doi:10.1109/WPNC.2010.5650745.
short: 'M. Bevermeier, O. Walter, S. Peschke, R. Haeb-Umbach, in: 7th Workshop on
Positioning Navigation and Communication (WPNC 2010), 2010, pp. 128–134.'
date_created: 2019-07-12T05:27:04Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/WPNC.2010.5650745
keyword:
- altitude measurement unit
- barometers
- barometric altimeter
- barometric error Kalman filter
- barometric height estimation
- direct cosine matrix
- global positioning system
- Global Positioning System
- GPS device
- height information
- height measurement
- inertial measurement unit
- Kalman filters
- loosely-coupled error state space Kalman filter
- loosely-coupled Kalman-filter
- map matching
- robust information
- robust location estimation
- sensor fusion
- topographic map information
- vertical user position
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/BeWaPeHa10.pdf
oa: '1'
page: 128-134
publication: 7th Workshop on Positioning Navigation and Communication (WPNC 2010)
status: public
title: Barometric height estimation combined with map-matching in a loosely-coupled
Kalman-filter
type: conference
user_id: '44006'
year: '2010'
...
---
_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'
...
---
_id: '11939'
abstract:
- lang: eng
text: In this paper a switching linear dynamical model (SLDM) approach for speech
feature enhancement is improved by employing more accurate models for the dynamics
of speech and noise. The model of the clean speech feature trajectory is improved
by augmenting the state vector to capture information derived from the delta features.
Further a hidden noise state variable is introduced to obtain a more elaborated
model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried
out by a bank of extended Kalman filters, whose outputs are combined according
to the a posteriori probability of the individual state models. Experimental results
on the AURORA2 database show improved recognition accuracy.
author:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Windmann S, Haeb-Umbach R. Modeling the dynamics of speech and noise for speech
feature enhancement in ASR. In: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2008). ; 2008:4409-4412. doi:10.1109/ICASSP.2008.4518633'
apa: Windmann, S., & Haeb-Umbach, R. (2008). Modeling the dynamics of speech
and noise for speech feature enhancement in ASR. In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008) (pp. 4409–4412).
https://doi.org/10.1109/ICASSP.2008.4518633
bibtex: '@inproceedings{Windmann_Haeb-Umbach_2008, title={Modeling the dynamics
of speech and noise for speech feature enhancement in ASR}, DOI={10.1109/ICASSP.2008.4518633},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2008)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2008},
pages={4409–4412} }'
chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
and Noise for Speech Feature Enhancement in ASR.” In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008), 4409–12, 2008. https://doi.org/10.1109/ICASSP.2008.4518633.
ieee: S. Windmann and R. Haeb-Umbach, “Modeling the dynamics of speech and noise
for speech feature enhancement in ASR,” in IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
and Noise for Speech Feature Enhancement in ASR.” IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–12,
doi:10.1109/ICASSP.2008.4518633.
short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.'
date_created: 2019-07-12T05:31:11Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2008.4518633
keyword:
- a posteriori probability
- AURORA2 database
- Bayesian inference
- Bayes methods
- channel bank filters
- extended Kalman filter banks
- hidden noise state variable
- Kalman filters
- noise dynamics
- speech enhancement
- speech feature enhancement
- speech feature trajectory
- switching linear dynamical model approach
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2008/WiHa08-1.pdf
oa: '1'
page: 4409-4412
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2008)
status: public
title: Modeling the dynamics of speech and noise for speech feature enhancement in
ASR
type: conference
user_id: '44006'
year: '2008'
...
---
_id: '11785'
abstract:
- lang: eng
text: 'In this paper we present a novel channel impulse response estimation technique
for block-oriented OFDM transmission based on combining estimators: the estimates
provided by a Kalman filter operating in the time domain and a Wiener filter in
the frequency domain are optimally combined by taking into account their estimated
error covariances. The resulting estimator turns out to be identical to the MAP
estimator of correlated jointly Gaussian mean vectors. Different variants of the
proposed scheme are experimentally investigated in an EEEE 802.11a-like system
setup. They compare favourably with known approaches from the literature resulting
in reduced mean square estimation error and bit error rate. Further, robustness
and complexity issues are discussed'
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
citation:
ama: 'Haeb-Umbach R, Bevermeier M. OFDM Channel Estimation Based on Combined Estimation
in Time and Frequency Domain. In: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2007). Vol 3. ; 2007:III-277-III-280.
doi:10.1109/ICASSP.2007.366526'
apa: Haeb-Umbach, R., & Bevermeier, M. (2007). OFDM Channel Estimation Based
on Combined Estimation in Time and Frequency Domain. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2007) (Vol.
3, pp. III-277-III–280). https://doi.org/10.1109/ICASSP.2007.366526
bibtex: '@inproceedings{Haeb-Umbach_Bevermeier_2007, title={OFDM Channel Estimation
Based on Combined Estimation in Time and Frequency Domain}, volume={3}, DOI={10.1109/ICASSP.2007.366526},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2007)}, author={Haeb-Umbach, Reinhold and Bevermeier, Maik}, year={2007},
pages={III-277-III–280} }'
chicago: Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based
on Combined Estimation in Time and Frequency Domain.” In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 3:III-277-III–280,
2007. https://doi.org/10.1109/ICASSP.2007.366526.
ieee: R. Haeb-Umbach and M. Bevermeier, “OFDM Channel Estimation Based on Combined
Estimation in Time and Frequency Domain,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2007), 2007, vol. 3, pp.
III-277-III–280.
mla: Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based
on Combined Estimation in Time and Frequency Domain.” IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2007), vol. 3, 2007, pp.
III-277-III–280, doi:10.1109/ICASSP.2007.366526.
short: 'R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2007), 2007, pp. III-277-III–280.'
date_created: 2019-07-12T05:28:13Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2007.366526
intvolume: ' 3'
keyword:
- bit error rate
- block-oriented OFDM transmission
- channel estimation
- channel impulse response estimation
- combining estimators
- error statistics
- frequency domain estimation
- Gaussian mean vectors
- Gaussian processes
- Kalman filter
- Kalman filters
- MAP estimator
- maximum likelihood estimation
- OFDM channel estimation
- OFDM modulation
- time domain estimation
- time-frequency analysis
- Wiener filter
- Wiener filters
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf
oa: '1'
page: III-277-III-280
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2007)
status: public
title: OFDM Channel Estimation Based on Combined Estimation in Time and Frequency
Domain
type: conference
user_id: '44006'
volume: 3
year: '2007'
...
---
_id: '11927'
abstract:
- lang: eng
text: Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the
presence of spatially colored noise leads to a generalized eigenvalue problem.
While this approach has extensively been employed in narrowband (antenna) array
beamforming, it is typically not used for broadband (microphone) array beamforming
due to the uncontrolled amount of speech distortion introduced by a narrowband
SNR criterion. In this paper, we show how the distortion of the desired signal
can be controlled by a single-channel post-filter, resulting in a performance
comparable to the generalized minimum variance distortionless response beamformer,
where arbitrary transfer functions relate the source and the microphones. Results
are given both for directional and diffuse noise. A novel gradient ascent adaptation
algorithm is presented, and its good convergence properties are experimentally
revealed by comparison with alternatives from the literature. A key feature of
the proposed beamformer is that it operates blindly, i.e., it neither requires
knowledge about the array geometry nor an explicit estimation of the transfer
functions from source to sensors or the direction-of-arrival.
author:
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Warsitz E, Haeb-Umbach R. Blind Acoustic Beamforming Based on Generalized Eigenvalue
Decomposition. IEEE Transactions on Audio, Speech, and Language Processing.
2007;15(5):1529-1539. doi:10.1109/TASL.2007.898454
apa: Warsitz, E., & Haeb-Umbach, R. (2007). Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition. IEEE Transactions on Audio, Speech,
and Language Processing, 15(5), 1529–1539. https://doi.org/10.1109/TASL.2007.898454
bibtex: '@article{Warsitz_Haeb-Umbach_2007, title={Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition}, volume={15}, DOI={10.1109/TASL.2007.898454},
number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2007}, pages={1529–1539}
}'
chicago: 'Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming
Based on Generalized Eigenvalue Decomposition.” IEEE Transactions on Audio,
Speech, and Language Processing 15, no. 5 (2007): 1529–39. https://doi.org/10.1109/TASL.2007.898454.'
ieee: E. Warsitz and R. Haeb-Umbach, “Blind Acoustic Beamforming Based on Generalized
Eigenvalue Decomposition,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 15, no. 5, pp. 1529–1539, 2007.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition.” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 15, no. 5, 2007, pp. 1529–39, doi:10.1109/TASL.2007.898454.
short: E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 15 (2007) 1529–1539.
date_created: 2019-07-12T05:30:57Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2007.898454
intvolume: ' 15'
issue: '5'
keyword:
- acoustic signal processing
- arbitrary transfer function
- array signal processing
- blind acoustic beamforming
- direction-of-arrival
- direction-of-arrival estimation
- eigenvalues and eigenfunctions
- generalized eigenvalue decomposition
- gradient ascent adaptation algorithm
- microphone arrays
- microphones
- narrowband array beamforming
- sensor array
- single-channel post-filter
- spatially colored noise
- transfer functions
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2007/WaHa07.pdf
oa: '1'
page: 1529-1539
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition
type: journal_article
user_id: '44006'
volume: 15
year: '2007'
...
---
_id: '11943'
abstract:
- lang: eng
text: A marginalized particle filter is proposed for performing single channel speech
enhancement with a non-linear dynamic state model. The system consists of a particle
filter for tracking line spectral pair (LSP) parameters and a Kalman filter per
particle for speech enhancement. The state model for the LSPs has been learnt
on clean speech training data. In our approach parameters and speech samples are
processed at different time scales by assuming the parameters to be constant for
small blocks of data. Further enhancement is obtained by an iteration which can
be applied on these small blocks. The experiments show that similar SNR gains
are obtained as with the Kalman-LM-iterative algorithm. However better values
of the noise level and the log-spectral distance are achieved
author:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006). Vol 1. ; 2006:I.
doi:10.1109/ICASSP.2006.1660058'
apa: Windmann, S., & Haeb-Umbach, R. (2006). Iterative Speech Enhancement using
a Non-Linear Dynamic State Model of Speech and its Parameters. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) (Vol.
1, p. I). https://doi.org/10.1109/ICASSP.2006.1660058
bibtex: '@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement
using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1},
DOI={10.1109/ICASSP.2006.1660058},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006},
pages={I} }'
chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement
Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006),
1:I, 2006. https://doi.org/10.1109/ICASSP.2006.1660058.
ieee: S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, vol. 1, p.
I.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using
a Non-Linear Dynamic State Model of Speech and Its Parameters.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol.
1, 2006, p. I, doi:10.1109/ICASSP.2006.1660058.
short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:31:15Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1660058
intvolume: ' 1'
keyword:
- clean speech training data
- iterative methods
- iterative speech enhancement
- Kalman filter
- Kalman filters
- Kalman-LM-iterative algorithm
- line spectral pair parameters
- log-spectral distance
- marginalized particle filter
- noise level
- nonlinear dynamic state speech model
- particle filtering (numerical methods)
- single channel speech enhancement
- SNR gains
- speech enhancement
- speech samples
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)
status: public
title: Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech
and its Parameters
type: conference
user_id: '44006'
volume: 1
year: '2006'
...
---
_id: '11930'
abstract:
- lang: eng
text: For human-machine interfaces in distant-talking environments multichannel
signal processing is often employed to obtain an enhanced signal for subsequent
processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum
beamformer to adjust the coefficients of FIR filters to changing acoustic room
impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient
ascent algorithm are derived from a constrained optimization problem, which iteratively
estimates the eigenvector corresponding to the largest eigenvalue of the cross
power spectral density of the microphone signals. The method does not require
an explicit estimation of the speaker location. The experimental results show
fast adaptation and excellent robustness of the proposed algorithm.
author:
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Warsitz E, Haeb-Umbach R. Acoustic filter-and-sum beamforming by adaptive
principal component analysis. In: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005). Vol 4. ; 2005:iv/797-iv/800 Vol.
4. doi:10.1109/ICASSP.2005.1416129'
apa: Warsitz, E., & Haeb-Umbach, R. (2005). Acoustic filter-and-sum beamforming
by adaptive principal component analysis. In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2005) (Vol. 4, p. iv/797-iv/800
Vol. 4). https://doi.org/10.1109/ICASSP.2005.1416129
bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2005, title={Acoustic filter-and-sum
beamforming by adaptive principal component analysis}, volume={4}, DOI={10.1109/ICASSP.2005.1416129},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2005)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2005},
pages={iv/797-iv/800 Vol. 4} }'
chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming
by Adaptive Principal Component Analysis.” In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2005), 4:iv/797-iv/800
Vol. 4, 2005. https://doi.org/10.1109/ICASSP.2005.1416129.
ieee: E. Warsitz and R. Haeb-Umbach, “Acoustic filter-and-sum beamforming by adaptive
principal component analysis,” in IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005), 2005, vol. 4, p. iv/797-iv/800
Vol. 4.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming
by Adaptive Principal Component Analysis.” IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2005), vol. 4, 2005, p. iv/797-iv/800
Vol. 4, doi:10.1109/ICASSP.2005.1416129.
short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005), 2005, p. iv/797-iv/800 Vol. 4.'
date_created: 2019-07-12T05:31:00Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2005.1416129
intvolume: ' 4'
keyword:
- acoustic filter-and-sum beamforming
- acoustic room impulses
- acoustic signal processing
- adaptive principal component analysis
- adaptive signal processing
- architectural acoustics
- constrained optimization problem
- cross power spectral density
- deterministic algorithm
- deterministic algorithms
- distant-talking environments
- eigenvalues and eigenfunctions
- eigenvector
- enhanced signal
- filter-and-sum beamformer
- FIR filter coefficients
- FIR filter coefficients
- FIR filters
- gradient methods
- human-machine interfaces
- iterative estimation
- iterative methods
- largest eigenvalue
- microphone signals
- multichannel signal processing
- optimisation
- principal component analysis
- spectral analysis
- stochastic gradient ascent algorithm
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2005/WaHa05.pdf
oa: '1'
page: iv/797-iv/800 Vol. 4
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2005)
status: public
title: Acoustic filter-and-sum beamforming by adaptive principal component analysis
type: conference
user_id: '44006'
volume: 4
year: '2005'
...
---
_id: '11931'
abstract:
- lang: eng
text: The paper is concerned with binaural signal processing for a bimodal human-robot
interface with hearing and vision. The two microphone signals are processed to
obtain an enhanced single-channel input signal for the subsequent speech recognizer
and to localize the acoustic source, an important information for establishing
a natural human-robot communication. We utilize a robust adaptive algorithm for
filter-and-sum beamforming (FSB) and extract speaker direction information from
the resulting FIR filter coefficients. Further, particle filtering is applied
which conducts a nonlinear Bayesian tracking of speaker movement. Good location
accuracy can be achieved even in highly reverberant environments. The results
obtained outperform the conventional generalized cross correlation (GCC) method.
author:
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Warsitz E, Haeb-Umbach R. Robust speaker direction estimation with particle
filtering. In: IEEE Workshop on Multimedia Signal Processing (MMSP 2004).
; 2004:367-370. doi:10.1109/MMSP.2004.1436569'
apa: Warsitz, E., & Haeb-Umbach, R. (2004). Robust speaker direction estimation
with particle filtering. In IEEE Workshop on Multimedia Signal Processing (MMSP
2004) (pp. 367–370). https://doi.org/10.1109/MMSP.2004.1436569
bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2004, title={Robust speaker direction
estimation with particle filtering}, DOI={10.1109/MMSP.2004.1436569},
booktitle={IEEE Workshop on Multimedia Signal Processing (MMSP 2004)}, author={Warsitz,
Ernst and Haeb-Umbach, Reinhold}, year={2004}, pages={367–370} }'
chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” In IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 367–70, 2004. https://doi.org/10.1109/MMSP.2004.1436569.
ieee: E. Warsitz and R. Haeb-Umbach, “Robust speaker direction estimation with particle
filtering,” in IEEE Workshop on Multimedia Signal Processing (MMSP 2004),
2004, pp. 367–370.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” IEEE Workshop on Multimedia Signal Processing (MMSP
2004), 2004, pp. 367–70, doi:10.1109/MMSP.2004.1436569.
short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 2004, pp. 367–370.'
date_created: 2019-07-12T05:31:01Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/MMSP.2004.1436569
keyword:
- bimodal human-robot interface
- binaural signal processing
- enhanced single-channel input signal
- filter-and-sum beamforming
- filtering theory
- FIR filter coefficient
- generalized cross correlation method
- microphones
- microphone signal
- nonlinear Bayesian tracking
- particle filtering
- robust adaptive algorithm
- robust speaker direction estimation
- signal processing
- speech enhancement
- speech recognition
- speech recognizer
- user interfaces
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2004/WaHa04.pdf
oa: '1'
page: 367-370
publication: IEEE Workshop on Multimedia Signal Processing (MMSP 2004)
status: public
title: Robust speaker direction estimation with particle filtering
type: conference
user_id: '44006'
year: '2004'
...