@inproceedings{11723, abstract = {{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 = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}}, booktitle = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}}, keywords = {{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}}, pages = {{235--242}}, title = {{{Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}}}, doi = {{10.1109/WPNC.2009.4907833}}, year = {{2009}}, } @inproceedings{11724, abstract = {{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 = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}}, keywords = {{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}}, pages = {{1--5}}, title = {{{Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning}}}, doi = {{10.1109/VETECS.2009.5073634}}, year = {{2009}}, }