[{"abstract":[{"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.","lang":"eng"}],"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: <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>","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} }","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>.","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>.","short":"M. Bevermeier, O. Walter, S. Peschke, R. Haeb-Umbach, in: 7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010, pp. 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>","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."},"publication":"7th Workshop on Positioning Navigation and Communication (WPNC 2010)","oa":"1","department":[{"_id":"54"}],"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"],"type":"conference","date_created":"2019-07-12T05:27:04Z","date_updated":"2022-01-06T06:51:07Z","author":[{"last_name":"Bevermeier","first_name":"Maik","full_name":"Bevermeier, Maik"},{"full_name":"Walter, Oliver","first_name":"Oliver","last_name":"Walter"},{"full_name":"Peschke, Sven","first_name":"Sven","last_name":"Peschke"},{"full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242"}],"status":"public","year":"2010","title":"Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter","doi":"10.1109/WPNC.2010.5650745","user_id":"44006","language":[{"iso":"eng"}],"_id":"11726","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2010/BeWaPeHa10.pdf","open_access":"1"}],"page":"128-134"},{"doi":"10.1109/WPNC.2009.4907833","user_id":"44006","page":"235-242","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf"}],"language":[{"iso":"eng"}],"_id":"11723","date_updated":"2022-01-06T06:51:07Z","title":"Robust vehicle localization based on multi-level sensor fusion and online parameter estimation","year":"2009","status":"public","author":[{"full_name":"Bevermeier, Maik","last_name":"Bevermeier","first_name":"Maik"},{"full_name":"Peschke, Sven","first_name":"Sven","last_name":"Peschke"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"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"],"type":"conference","oa":"1","department":[{"_id":"54"}],"date_created":"2019-07-12T05:27:01Z","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)","citation":{"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.","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>","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>.","short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, 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.” <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} }","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>"}},{"date_created":"2019-07-12T05:27:02Z","type":"conference","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"],"oa":"1","department":[{"_id":"54"}],"publication":"IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)","citation":{"short":"M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 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>.","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.","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>","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>."},"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"}],"page":"1-5","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf","open_access":"1"}],"_id":"11724","language":[{"iso":"eng"}],"doi":"10.1109/VETECS.2009.5073634","user_id":"44006","title":"Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning","year":"2009","status":"public","author":[{"full_name":"Bevermeier, Maik","first_name":"Maik","last_name":"Bevermeier"},{"full_name":"Peschke, Sven","first_name":"Sven","last_name":"Peschke"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_updated":"2022-01-06T06:51:07Z"}]