---
_id: '11816'
abstract:
- lang: eng
  text: In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters
    of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the
    resulting Expectation Maximization (EM) algorithm delivers virtually biasfree
    and efficient estimates, and we discuss its convergence properties. We also discuss
    optimal classification in the presence of censored data. Censored data are frequently
    encountered in wireless LAN positioning systems based on the fingerprinting method
    employing signal strength measurements, due to the limited sensitivity of the
    portable devices. Experiments both on simulated and real-world data demonstrate
    the effectiveness of the proposed algorithms.
author:
- first_name: Manh Kha
  full_name: Hoang, Manh Kha
  last_name: Hoang
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
    Gaussian data with application to WiFi indoor positioning. In: <i>38th International
    Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>'
  apa: Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification
    of censored Gaussian data with application to WiFi indoor positioning. In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>
    (pp. 3721–3725). <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>
  bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
    classification of censored Gaussian data with application to WiFi indoor positioning},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>},
    booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
    (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
    pages={3721–3725} }'
  chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    3721–25, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>.
  ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
    censored Gaussian data with application to WiFi indoor positioning,” in <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–3725.
  mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–25, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>.
  short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- Gaussian processes
- Global Positioning System
- convergence
- expectation-maximisation algorithm
- fingerprint identification
- indoor radio
- signal classification
- wireless LAN
- EM algorithm
- ML estimation
- WiFi indoor positioning
- censored Gaussian data classification
- clipped data
- convergence properties
- expectation maximization algorithm
- fingerprinting method
- maximum likelihood estimation
- optimal classification
- parameters estimation
- portable devices sensitivity
- signal strength measurements
- wireless LAN positioning systems
- Convergence
- IEEE 802.11 Standards
- Maximum likelihood estimation
- Parameter estimation
- Position measurement
- Training
- Indoor positioning
- censored data
- expectation maximization
- signal strength
- wireless LAN
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
  to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11913'
abstract:
- lang: eng
  text: In this paper we propose to employ directional statistics in a complex vector
    space to approach the problem of blind speech separation in the presence of spatially
    correlated noise. We interpret the values of the short time Fourier transform
    of the microphone signals to be draws from a mixture of complex Watson distributions,
    a probabilistic model which naturally accounts for spatial aliasing. The parameters
    of the density are related to the a priori source probabilities, the power of
    the sources and the transfer function ratios from sources to sensors. Estimation
    formulas are derived for these parameters by employing the Expectation Maximization
    (EM) algorithm. The E-step corresponds to the estimation of the source presence
    probabilities for each time-frequency bin, while the M-step leads to a maximum
    signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about
    the source activity. Experimental results are reported for an implementation in
    a generalized sidelobe canceller (GSC) like spatial beamforming configuration
    for 3 speech sources with significant coherent noise in reverberant environments,
    demonstrating the usefulness of the novel modeling framework.
author:
- first_name: Dang Hai
  full_name: Tran Vu, Dang Hai
  last_name: Tran Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Tran Vu DH, Haeb-Umbach R. Blind speech separation employing directional statistics
    in an Expectation Maximization framework. In: <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>. ; 2010:241-244.
    doi:<a href="https://doi.org/10.1109/ICASSP.2010.5495994">10.1109/ICASSP.2010.5495994</a>'
  apa: Tran Vu, D. H., &#38; Haeb-Umbach, R. (2010). Blind speech separation employing
    directional statistics in an Expectation Maximization framework. In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i> (pp. 241–244).
    <a href="https://doi.org/10.1109/ICASSP.2010.5495994">https://doi.org/10.1109/ICASSP.2010.5495994</a>
  bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2010, title={Blind speech separation
    employing directional statistics in an Expectation Maximization framework}, DOI={<a
    href="https://doi.org/10.1109/ICASSP.2010.5495994">10.1109/ICASSP.2010.5495994</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2010)}, author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2010},
    pages={241–244} }'
  chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing
    Directional Statistics in an Expectation Maximization Framework.” In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>, 241–44,
    2010. <a href="https://doi.org/10.1109/ICASSP.2010.5495994">https://doi.org/10.1109/ICASSP.2010.5495994</a>.
  ieee: D. H. Tran Vu and R. Haeb-Umbach, “Blind speech separation employing directional
    statistics in an Expectation Maximization framework,” in <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>, 2010,
    pp. 241–244.
  mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing
    Directional Statistics in an Expectation Maximization Framework.” <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)</i>, 2010,
    pp. 241–44, doi:<a href="https://doi.org/10.1109/ICASSP.2010.5495994">10.1109/ICASSP.2010.5495994</a>.
  short: 'D.H. Tran Vu, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2010), 2010, pp. 241–244.'
date_created: 2019-07-12T05:30:40Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2010.5495994
keyword:
- array signal processing
- blind source separation
- blind speech separation
- complex vector space
- complex Watson distribution
- directional statistics
- expectation-maximisation algorithm
- expectation maximization algorithm
- Fourier transform
- Fourier transforms
- generalized sidelobe canceller
- interference suppression
- maximum signal-to-noise ratio beamformer
- microphone signal
- probabilistic model
- spatial aliasing
- spatial beamforming configuration
- speech enhancement
- statistical distributions
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2010/DaHa10-2.pdf
oa: '1'
page: 241-244
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2010)
status: public
title: Blind speech separation employing directional statistics in an Expectation
  Maximization framework
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: <i>6th Workshop
    on Positioning Navigation and Communication (WPNC 2009)</i>. ; 2009:235-242. doi:<a
    href="https://doi.org/10.1109/WPNC.2009.4907833">10.1109/WPNC.2009.4907833</a>'
  apa: Bevermeier, M., Peschke, S., &#38; Haeb-Umbach, R. (2009). Robust vehicle localization
    based on multi-level sensor fusion and online parameter estimation. In <i>6th
    Workshop on Positioning Navigation and Communication (WPNC 2009)</i> (pp. 235–242).
    <a href="https://doi.org/10.1109/WPNC.2009.4907833">https://doi.org/10.1109/WPNC.2009.4907833</a>
  bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle
    localization based on multi-level sensor fusion and online parameter estimation},
    DOI={<a href="https://doi.org/10.1109/WPNC.2009.4907833">10.1109/WPNC.2009.4907833</a>},
    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 <i>6th Workshop on Positioning Navigation and Communication (WPNC 2009)</i>,
    235–42, 2009. <a href="https://doi.org/10.1109/WPNC.2009.4907833">https://doi.org/10.1109/WPNC.2009.4907833</a>.
  ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization
    based on multi-level sensor fusion and online parameter estimation,” in <i>6th
    Workshop on Positioning Navigation and Communication (WPNC 2009)</i>, 2009, pp.
    235–242.
  mla: Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level
    Sensor Fusion and Online Parameter Estimation.” <i>6th Workshop on Positioning
    Navigation and Communication (WPNC 2009)</i>, 2009, pp. 235–42, doi:<a href="https://doi.org/10.1109/WPNC.2009.4907833">10.1109/WPNC.2009.4907833</a>.
  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: <i>IEEE 69th Vehicular
    Technology Conference (VTC 2009 Spring)</i>. ; 2009:1-5. doi:<a href="https://doi.org/10.1109/VETECS.2009.5073634">10.1109/VETECS.2009.5073634</a>'
  apa: Bevermeier, M., Peschke, S., &#38; Haeb-Umbach, R. (2009). Joint Parameter
    Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.
    In <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i> (pp. 1–5).
    <a href="https://doi.org/10.1109/VETECS.2009.5073634">https://doi.org/10.1109/VETECS.2009.5073634</a>
  bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Joint Parameter
    Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning},
    DOI={<a href="https://doi.org/10.1109/VETECS.2009.5073634">10.1109/VETECS.2009.5073634</a>},
    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 <i>IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)</i>, 1–5, 2009.
    <a href="https://doi.org/10.1109/VETECS.2009.5073634">https://doi.org/10.1109/VETECS.2009.5073634</a>.
  ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Joint Parameter Estimation
    and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning,” in <i>IEEE
    69th Vehicular Technology Conference (VTC 2009 Spring)</i>, 2009, pp. 1–5.
  mla: Bevermeier, Maik, et al. “Joint Parameter Estimation and Tracking in a Multi-Stage
    Kalman Filter for Vehicle Positioning.” <i>IEEE 69th Vehicular Technology Conference
    (VTC 2009 Spring)</i>, 2009, pp. 1–5, doi:<a href="https://doi.org/10.1109/VETECS.2009.5073634">10.1109/VETECS.2009.5073634</a>.
  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'
...