TY - CONF AB - 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. AU - Bevermeier, Maik AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11723 KW - covariance matrices KW - expectation-maximisation algorithm KW - expectation-maximization algorithm KW - global positioning system KW - Global Positioning System KW - GPS KW - inertial measurement unit KW - interacting multiple model approach KW - Kalman filters KW - multilevel sensor fusion KW - narrow street canyons KW - narrow tunnels KW - online parameter estimation KW - parameter estimation KW - road vehicles KW - robust vehicle localization KW - sensor fusion KW - state noise covariances KW - time-variant multilevel Kalman filter KW - vehicle tracking algorithm T2 - 6th Workshop on Positioning Navigation and Communication (WPNC 2009) TI - Robust vehicle localization based on multi-level sensor fusion and online parameter estimation ER - TY - CONF AB - 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. AU - Bevermeier, Maik AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11724 KW - computational complexity KW - expectation-maximisation algorithm KW - Global Positioning System KW - inertial measurement unit KW - inertial navigation KW - interacting multiple model KW - iterative block expectation-maximization algorithm KW - Kalman filters KW - multi-stage Kalman filter KW - parameter estimation KW - road vehicles KW - vehicle positioning KW - vehicle tracking T2 - IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) TI - Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning ER -