[{"keyword":["joint estimation","unscented transform","Kalman filter","sparsity","data-driven","compressed sensing"],"language":[{"iso":"eng"}],"_id":"34171","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","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."}],"status":"public","publication":"12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)","type":"conference","title":"Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF","doi":"https://doi.org/10.1016/j.ifacol.2023.02.015","conference":{"name":"12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022","start_date":"2023-01-04","end_date":"2023-01-06","location":"Canberra, Australien"},"date_updated":"2024-11-13T08:43:05Z","volume":56,"author":[{"first_name":"Ricarda-Samantha","id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"date_created":"2022-12-01T07:17:00Z","year":"2023","intvolume":"        56","page":"85-90","citation":{"chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” In <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, 56:85–90, 2023. <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","ieee":"R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF,” in <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","ama":"Götte R-S, Timmermann J. Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. In: <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>. Vol 56. ; 2023:85-90. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>","short":"R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 2023, pp. 85–90.","bibtex":"@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>}, number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90} }","mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, vol. 56, no. 1, 2023, pp. 85–90, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","apa":"Götte, R.-S., &#38; Timmermann, J. (2023). Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, <i>56</i>(1), 85–90. <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>"},"quality_controlled":"1","issue":"1"},{"_id":"44326","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","keyword":["joint estimation","unscented Kalman filter","sparsity","Laplacian prior","regularized horseshoe","principal component analysis"],"language":[{"iso":"eng"}],"publication":"IFAC-PapersOnLine","type":"conference","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."}],"status":"public","date_updated":"2024-11-13T08:42:37Z","volume":56,"author":[{"full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte","first_name":"Ricarda-Samantha"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"}],"date_created":"2023-05-02T15:16:43Z","title":"Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF","conference":{"location":"Yokohama, Japan","end_date":"2023-07-14","start_date":"2023-07-09","name":"22nd IFAC World Congress"},"quality_controlled":"1","issue":"2","year":"2023","intvolume":"        56","page":"869-874","citation":{"chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” In <i>IFAC-PapersOnLine</i>, 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 <i>IFAC-PapersOnLine</i>, Yokohama, Japan, 2023, vol. 56, no. 2, pp. 869–874.","ama":"Götte R-S, Timmermann J. Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. In: <i>IFAC-PapersOnLine</i>. Vol 56. ; 2023:869-874.","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} }","short":"R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.","mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” <i>IFAC-PapersOnLine</i>, vol. 56, no. 2, 2023, pp. 869–74.","apa":"Götte, R.-S., &#38; Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. <i>IFAC-PapersOnLine</i>, <i>56</i>(2), 869–874."}},{"status":"public","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."}],"publication":"7th Workshop on Positioning Navigation and Communication (WPNC 2010)","type":"conference","language":[{"iso":"eng"}],"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"],"department":[{"_id":"54"}],"user_id":"44006","_id":"11726","page":"128-134","citation":{"mla":"Bevermeier, Maik, et al. “Barometric Height Estimation Combined with Map-Matching in a Loosely-Coupled Kalman-Filter.” <i>7th Workshop on Positioning Navigation and Communication (WPNC 2010)</i>, 2010, pp. 128–34, doi:<a href=\"https://doi.org/10.1109/WPNC.2010.5650745\">10.1109/WPNC.2010.5650745</a>.","short":"M. Bevermeier, O. Walter, S. Peschke, R. Haeb-Umbach, in: 7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010, pp. 128–134.","bibtex":"@inproceedings{Bevermeier_Walter_Peschke_Haeb-Umbach_2010, title={Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter}, DOI={<a href=\"https://doi.org/10.1109/WPNC.2010.5650745\">10.1109/WPNC.2010.5650745</a>}, 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} }","apa":"Bevermeier, M., Walter, O., Peschke, S., &#38; Haeb-Umbach, R. (2010). Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter. In <i>7th Workshop on Positioning Navigation and Communication (WPNC 2010)</i> (pp. 128–134). <a href=\"https://doi.org/10.1109/WPNC.2010.5650745\">https://doi.org/10.1109/WPNC.2010.5650745</a>","ama":"Bevermeier M, Walter O, Peschke S, Haeb-Umbach R. Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter. In: <i>7th Workshop on Positioning Navigation and Communication (WPNC 2010)</i>. ; 2010:128-134. doi:<a href=\"https://doi.org/10.1109/WPNC.2010.5650745\">10.1109/WPNC.2010.5650745</a>","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 <i>7th Workshop on Positioning Navigation and Communication (WPNC 2010)</i>, 2010, pp. 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 <i>7th Workshop on Positioning Navigation and Communication (WPNC 2010)</i>, 128–34, 2010. <a href=\"https://doi.org/10.1109/WPNC.2010.5650745\">https://doi.org/10.1109/WPNC.2010.5650745</a>."},"year":"2010","doi":"10.1109/WPNC.2010.5650745","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2010/BeWaPeHa10.pdf","open_access":"1"}],"title":"Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter","author":[{"first_name":"Maik","last_name":"Bevermeier","full_name":"Bevermeier, Maik"},{"last_name":"Walter","full_name":"Walter, Oliver","first_name":"Oliver"},{"last_name":"Peschke","full_name":"Peschke, Sven","first_name":"Sven"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_created":"2019-07-12T05:27:04Z","date_updated":"2022-01-06T06:51:07Z","oa":"1"},{"status":"public","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"}],"publication":"6th Workshop on Positioning Navigation and Communication (WPNC 2009)","type":"conference","language":[{"iso":"eng"}],"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"],"department":[{"_id":"54"}],"user_id":"44006","_id":"11723","page":"235-242","citation":{"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.","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>","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>","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."},"year":"2009","doi":"10.1109/WPNC.2009.4907833","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf"}],"title":"Robust vehicle localization based on multi-level sensor fusion and online parameter estimation","date_created":"2019-07-12T05:27:01Z","author":[{"first_name":"Maik","last_name":"Bevermeier","full_name":"Bevermeier, Maik"},{"first_name":"Sven","full_name":"Peschke, Sven","last_name":"Peschke"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"oa":"1","date_updated":"2022-01-06T06:51:07Z"},{"page":"1-5","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} }","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.","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>","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.","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>.","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>"},"year":"2009","doi":"10.1109/VETECS.2009.5073634","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf"}],"title":"Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning","date_created":"2019-07-12T05:27:02Z","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"}],"date_updated":"2022-01-06T06:51:07Z","oa":"1","status":"public","abstract":[{"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.","lang":"eng"}],"publication":"IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)","type":"conference","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"],"department":[{"_id":"54"}],"user_id":"44006","_id":"11724"},{"title":"Modeling the dynamics of speech and noise for speech feature enhancement in ASR","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2008/WiHa08-1.pdf"}],"doi":"10.1109/ICASSP.2008.4518633","oa":"1","date_updated":"2022-01-06T06:51:12Z","author":[{"first_name":"Stefan","full_name":"Windmann, Stefan","last_name":"Windmann"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_created":"2019-07-12T05:31:11Z","year":"2008","citation":{"apa":"Windmann, S., &#38; Haeb-Umbach, R. (2008). Modeling the dynamics of speech and noise for speech feature enhancement in ASR. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)</i> (pp. 4409–4412). <a href=\"https://doi.org/10.1109/ICASSP.2008.4518633\">https://doi.org/10.1109/ICASSP.2008.4518633</a>","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech and Noise for Speech Feature Enhancement in ASR.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 2008, pp. 4409–12, doi:<a href=\"https://doi.org/10.1109/ICASSP.2008.4518633\">10.1109/ICASSP.2008.4518633</a>.","short":"S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.","bibtex":"@inproceedings{Windmann_Haeb-Umbach_2008, title={Modeling the dynamics of speech and noise for speech feature enhancement in ASR}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2008.4518633\">10.1109/ICASSP.2008.4518633</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2008}, pages={4409–4412} }","ieee":"S. Windmann and R. Haeb-Umbach, “Modeling the dynamics of speech and noise for speech feature enhancement in ASR,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 2008, pp. 4409–4412.","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech and Noise for Speech Feature Enhancement in ASR.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 4409–12, 2008. <a href=\"https://doi.org/10.1109/ICASSP.2008.4518633\">https://doi.org/10.1109/ICASSP.2008.4518633</a>.","ama":"Windmann S, Haeb-Umbach R. Modeling the dynamics of speech and noise for speech feature enhancement in ASR. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>. ; 2008:4409-4412. doi:<a href=\"https://doi.org/10.1109/ICASSP.2008.4518633\">10.1109/ICASSP.2008.4518633</a>"},"page":"4409-4412","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"}],"_id":"11939","user_id":"44006","department":[{"_id":"54"}],"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."}],"status":"public","type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)"},{"author":[{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"},{"last_name":"Bevermeier","full_name":"Bevermeier, Maik","first_name":"Maik"}],"date_created":"2019-07-12T05:28:13Z","volume":3,"oa":"1","date_updated":"2022-01-06T06:51:08Z","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf","open_access":"1"}],"doi":"10.1109/ICASSP.2007.366526","title":"OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain","citation":{"ieee":"R. Haeb-Umbach and M. Bevermeier, “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 2007, vol. 3, pp. III-277-III–280.","chicago":"Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 3:III-277-III–280, 2007. <a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">https://doi.org/10.1109/ICASSP.2007.366526</a>.","ama":"Haeb-Umbach R, Bevermeier M. OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>. Vol 3. ; 2007:III-277-III-280. doi:<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>","bibtex":"@inproceedings{Haeb-Umbach_Bevermeier_2007, title={OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain}, volume={3}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>}, 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} }","mla":"Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, vol. 3, 2007, pp. III-277-III–280, doi:<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>.","short":"R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 2007, pp. III-277-III–280.","apa":"Haeb-Umbach, R., &#38; Bevermeier, M. (2007). OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i> (Vol. 3, pp. III-277-III–280). <a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">https://doi.org/10.1109/ICASSP.2007.366526</a>"},"page":"III-277-III-280","intvolume":"         3","year":"2007","user_id":"44006","department":[{"_id":"54"}],"_id":"11785","language":[{"iso":"eng"}],"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"],"type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)","status":"public","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"}]},{"year":"2006","page":"I","intvolume":"         1","citation":{"ieee":"S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006, vol. 1, p. I.","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 1:I, 2006. <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>.","ama":"Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I. doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>","short":"S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.","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={<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006}, pages={I} }","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol. 1, 2006, p. I, doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>.","apa":"Windmann, S., &#38; Haeb-Umbach, R. (2006). Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol. 1, p. I). <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>"},"date_updated":"2022-01-06T06:51:12Z","oa":"1","volume":1,"date_created":"2019-07-12T05:31:15Z","author":[{"full_name":"Windmann, Stefan","last_name":"Windmann","first_name":"Stefan"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"title":"Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters","doi":"10.1109/ICASSP.2006.1660058","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf"}],"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)","type":"conference","abstract":[{"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","lang":"eng"}],"status":"public","_id":"11943","department":[{"_id":"54"}],"user_id":"44006","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"}]}]