[{"publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","type":"conference","status":"public","abstract":[{"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.","lang":"eng"}],"department":[{"_id":"54"}],"user_id":"44006","_id":"11816","language":[{"iso":"eng"}],"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"],"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf","description":"Poster","relation":"supplementary_material"}]},"publication_identifier":{"issn":["1520-6149"]},"page":"3721-3725","citation":{"short":"M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.","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} }","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>.","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>","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.","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>.","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>"},"year":"2013","author":[{"first_name":"Manh Kha","full_name":"Hoang, Manh Kha","last_name":"Hoang"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_created":"2019-07-12T05:28:48Z","oa":"1","date_updated":"2022-01-06T06:51:09Z","doi":"10.1109/ICASSP.2013.6638353","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf","open_access":"1"}],"title":"Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning"},{"year":"2010","page":"241-244","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>","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>.","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} }","short":"D.H. Tran Vu, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010, pp. 241–244.","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>"},"date_updated":"2022-01-06T06:51:12Z","oa":"1","author":[{"first_name":"Dang Hai","last_name":"Tran Vu","full_name":"Tran Vu, Dang Hai"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_created":"2019-07-12T05:30:40Z","title":"Blind speech separation employing directional statistics in an Expectation Maximization framework","doi":"10.1109/ICASSP.2010.5495994","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2010/DaHa10-2.pdf","open_access":"1"}],"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)","type":"conference","abstract":[{"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.","lang":"eng"}],"status":"public","_id":"11913","department":[{"_id":"54"}],"user_id":"44006","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"}]},{"year":"2009","page":"235-242","citation":{"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.","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>","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>.","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} }","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp. 235–242."},"title":"Robust vehicle localization based on multi-level sensor fusion and online parameter estimation","doi":"10.1109/WPNC.2009.4907833","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf"}],"oa":"1","date_updated":"2022-01-06T06:51:07Z","date_created":"2019-07-12T05:27:01Z","author":[{"full_name":"Bevermeier, Maik","last_name":"Bevermeier","first_name":"Maik"},{"first_name":"Sven","full_name":"Peschke, Sven","last_name":"Peschke"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"abstract":[{"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.","lang":"eng"}],"status":"public","publication":"6th Workshop on Positioning Navigation and Communication (WPNC 2009)","type":"conference","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"}],"_id":"11723","department":[{"_id":"54"}],"user_id":"44006"},{"citation":{"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} }","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, 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.” <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>.","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>","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.","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>"},"page":"1-5","year":"2009","author":[{"last_name":"Bevermeier","full_name":"Bevermeier, Maik","first_name":"Maik"},{"last_name":"Peschke","full_name":"Peschke, Sven","first_name":"Sven"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_created":"2019-07-12T05:27:02Z","oa":"1","date_updated":"2022-01-06T06:51:07Z","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf","open_access":"1"}],"doi":"10.1109/VETECS.2009.5073634","title":"Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning","type":"conference","publication":"IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)","status":"public","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."}],"user_id":"44006","department":[{"_id":"54"}],"_id":"11724","language":[{"iso":"eng"}],"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"]}]