@inproceedings{11818,
  abstract     = {{In this paper we present a system for indoor navigation based on received signal strength index information of Wireless-LAN access points and relative position estimates. The relative position information is gathered from inertial smartphone sensors using a step detection and an orientation estimate. Our map data is hosted on a server employing a map renderer and a SQL database. The database includes a complete multilevel office building, within which the user can navigate. During navigation, the client retrieves the position estimate from the server, together with the corresponding map tiles to visualize the user's position on the smartphone display.}},
  author       = {{Hoang, Manh Kha and Schmitz, Sarah and Drueke, Christian and Vu, Dang Hai Tran and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{Positioning Navigation and Communication (WPNC), 2013 10th Workshop on}},
  keywords     = {{SQL, navigation, smart phones, wireless LAN, RSSI, SQL database, complete multilevel office building, inertial sensor information, inertial smartphone sensors, map renderer, received signal strength index information, relative position estimates, server based indoor navigation, step detection, wireless-LAN access points, Smartphone, fingerprint, indoor navigation, map tile}},
  pages        = {{1--6}},
  title        = {{{Server based indoor navigation using RSSI and inertial sensor information}}},
  doi          = {{10.1109/WPNC.2013.6533263}},
  year         = {{2013}},
}

@inproceedings{11925,
  abstract     = {{In this paper we present a system for car navigation by fusing sensor data on an Android smartphone. The key idea is to use both the internal sensors of the smartphone (e.g., gyroscope) and sensor data from the car (e.g., speed information) to support navigation via GPS. To this end we employ a CAN-Bus-to-Bluetooth adapter to establish a wireless connection between the smartphone and the CAN-Bus of the car. On the smartphone a strapdown algorithm and an error-state Kalman filter are used to fuse the different sensor data streams. The experimental results show that the system is able to maintain higher positioning accuracy during GPS dropouts, thus improving the availability and reliability, compared to GPS-only solutions.}},
  author       = {{Walter, Oliver and Schmalenstroeer, Joerg and Engler, Andreas and Haeb-Umbach, Reinhold}},
  booktitle    = {{9th Workshop on Positioning Navigation and Communication (WPNC 2012)}},
  keywords     = {{Smartphone, navigation, sensor fusion}},
  title        = {{{Smartphone-Based Sensor Fusion for Improved Vehicular Navigation}}},
  year         = {{2012}},
}

@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}},
}

@inproceedings{11881,
  abstract     = {{A combination of GPS (global positioning system) and INS (inertial navigation system) is known to provide high precision and highly robust vehicle localization. Notably during times when the GPS signal has a poor quality, e.g. due to the lack of a sufficiently large number of visible satellites, the INS, which may consist of a gyroscope and an odometer, will lead to improved positioning accuracy. In this paper we show how velocity information obtained from GSM (global system for mobile communications) signalling, rather than from a tachometer, can be used together with a gyroscope sensor to support localization in the presence of temporarily unavailable GPS data. We propose a sensor fusion system architecture and present simulation results that show the effectiveness of this approach.}},
  author       = {{Peschke, Sven and Bevermeier, Maik and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{cellular radio, distance measurement, global positioning system, Global Positioning System, global system for mobile communications, GPS positioning approach, GSM velocity, gyroscopes, gyroscope sensor, inertial navigation, inertial navigation system, odometer, sensor fusion system architecture, sensors}},
  pages        = {{195--202}},
  title        = {{{A GPS positioning approach exploiting GSM velocity estimates}}},
  doi          = {{10.1109/WPNC.2009.4907827}},
  year         = {{2009}},
}

@inproceedings{39052,
  abstract     = {{Smart homes provide their users with maximum comfort and convenience. In this paper, we present a profile management framework for situation-dependent customization in smart home environments, which meet the user preferences with given device capabilities. We apply profile processing and evolution methods to customize profiles on the fly and to automatically evolve user preferences. Furthermore, we give a comprehensive study on profile management technology.}},
  author       = {{Groppe, Jinghua and Müller, Wolfgang}},
  booktitle    = {{Proceedings of the 1st International Workshop on Secure and Ubiquitous Networks (SUN-2005)}},
  isbn         = {{0-7695-2424-9}},
  keywords     = {{Technology management, Smart homes, Environmental management, Resource description framework, Data models, Navigation, Mobile computing, Embedded computing, Ubiquitous computing, Mobile communication}},
  location     = {{Copenhagen, Denmark }},
  publisher    = {{IEEE}},
  title        = {{{Profile Management technology for Smart Customization in Private Home Applications}}},
  doi          = {{10.1109/DEXA.2005.156}},
  year         = {{2005}},
}