S. Leinen

Improving the performance of tightly-coupled GPS/INS navigation by using time-differenced GPS-carrier-phase measurements and low-cost MEMS IMU

To avoid the difficulties in fixing the carrier phase ambiguities, the time difference carrier phase approach is applied to a GPS/IMU tightly-coupled navigation system to eliminate the ambiguity between two successive GPS epochs, which can provide high velocity estimation accuracy. The carrier phases are carefully corrected before use. A modified method is proposed by using the system matrix in each time update to calculate the integration of the velocity errors in the measurement update equation. A Cubature Kalman Filter (CKF) is applied to the integrated navigation system to improve the attitude estimation accuracy. The navigation result and comparison show the accuracy improvement after applying the carrier phase corrections, modified measurement update method and the CKF.

Estimability in Strapdown Airborne Vector Gravimetry

Estimability was introduced as a measure of how much observations can contribute to the estimation process for a linear system. Several publications analyse the estimabilities for integrated navigation systems, comprising a strapdown inertial measurement unit (IMU) and observations from global navigation satellite systems (GNSS). This concept will hereby be adapted to strapdown vector gravimetry, being a special application of a IMU/GNSS system. Simulations are presented showing to what extent different observation types, as positions, velocities and attitudes, can contribute to the determination of gravity disturbances. Knowledge about these characteristics of the system may be useful for the planning of future airborne gravimetry missions. In particular, the estimability of the deflection of the vertical (DOV) is analysed. During a non-accelerated flight, it is poorly estimable due to its high sensitivity to attitude errors. However, for flight manoeuvres comprising horizontal or vertical accelerations, the DOV estimability can be shown to increase significantly.

Effect of stochastic model fitting on the significance of CORS coordinate time series parameters.

Abstract Based on the reprocessing of a regional continuously operating reference station (CORS) network the effect of stochastic modelling on the significance of coordinate time series parameters is investigated. The aim is to identify a suitable stochastic model for the time correlated time series. It is needed as stochastic model of the time series data and to enable a realistic statement about the significance of estimated parameters like velocity, offsets, annual signal. The approach of power-law noise stochastic modelling is applied and investigated. Estimated stochastic parameters and resulting models best fitting to the time series are shown and discussed. It is found that the stochastic parameters are strongly site specific. Utilizing such a best fitting stochastic model will yield realistic statements about accuracy and significance of parameters (velocity, offsets, annual signal). The application to the assessment of the velocity field of the network concludes the paper.

Benefits of Multi-Constellation/Multi-Frequency GNSS in a Tightly Coupled GNSS/IMU/Odometry Integration Algorithm

Localization algorithms based on global navigation satellite systems (GNSS) play an important role in automotive positioning. Due to the advent of autonomously driving cars, their importance is expected to grow even further in the next years. Simultaneously, the performance requirements for these localization algorithms will increase because they are no longer used exclusively for navigation, but also for control of the vehicle’s movement. These requirements cannot be met with GNSS alone. Instead, algorithms for sensor data fusion are needed. While the combination of GNSS receivers with inertial measurements units (IMUs) is a common approach, it is traditionally executed in a single-frequency/single-constellation architecture, usually with the Global Positioning System’s (GPS) L1 C/A signal. With the advent of new GNSS constellations and civil signals on multiple frequencies, GNSS/IMU integration algorithm performance can be improved by utilizing these new data sources. To achieve this, we upgraded a tightly coupled GNSS/IMU integration algorithm to process measurements from GPS (L1 C/A, L2C, L5) and Galileo (E1, E5a, E5b). After investigating various combination strategies, we chose to preferably work with ionosphere-free combinations of L5-L1 C/A and E5a-E1 pseudo-ranges. L2C-L1 C/A and E5b-E1 combinations as well as single-frequency pseudo-ranges on L1 and E1 serve as backup when no L5/E5a measurements are available. To be able to process these six types of pseudo-range observations simultaneously, the differential code biases (DCBs) of the employed receiver need to be calibrated. Time-differenced carrier-phase measurements on L1 and E1 provide the algorithm with pseudo-range-rate observations. To provide additional aiding, information about the vehicle’s velocity obtained by an odometry model fed with angular velocities from all four wheels as well as the steering wheel angle is incorporated into the algorithm. To evaluate the performance improvement provided by these new data sources, two sets of measurement data are collected and the resulting navigation solutions are compared to a higher-grade reference system, consisting of a geodetic GNSS receiver for real-time kinematic positioning (RTK) and a navigation grade IMU. The multi-frequency/multi-constellation algorithm with odometry aiding achieves a 3-D root mean square (RMS) position error of 3.6 m/2.1 m in these data sets, compared to 5.2 m/2.9 m for the single-frequency GPS algorithm without odometry aiding. Odometry is most beneficial to positioning accuracy when GNSS measurement quality is poor. This is demonstrated in data set 1, resulting in a reduction of the horizontal position error’s 95% quantile from 6.2 m without odometry aiding to 4.2 m with odometry aiding.

Datenfusion für die präzise Lokalisierung

In Kraftfahrzeugen wird eine zunehmende Anzahl an heterogenen und haufig redundanten Sensoren eingesetzt. In diesem Kapitel wird die Methodik zur Fusion von heterogenen Sensordaten zur prazisen Lokalisierung und daruber hinaus zur Fahrdynamikschatzung vorgestellt, sie beruht im Wesentlichen auf [1]. Ziel ist hierbei die Erzeugung eines konsistenten Datensatzes mit erhohter Genauigkeit. Es werden Klassifizierungen und Ontologien fur in Frage kommende Systemarchitekturen und Fusionsfilter gezeigt, spezielle Erweiterungen fur das Filter zur Verwendung mit heterogenen, seriennahen Sensoren werden hergeleitet. Dies fuhrt zum Konzept eines virtuellen Sensors als neue Ebene zwischen Sensoren und Anwendungen: Hierfur werden, insbesondere zur Verwendung in sicherheitskritischen Applikationen, Anforderungen fur eine Datenqualitatsbeschreibung hergeleitet. Diese gliedert sich in ein Integritats- und ein Genauigkeitsmas; eine beispielhafte Umsetzung fur einen gegebenen Satz an Sensoren wird vorgestellt. Auserdem wird der Spezialfall eines Fahrzeugs auf bewegtem Untergrund (z. B. Fahre) betrachtet und es werden Losungsansatze fur damit verknupfte Probleme aufgezeigt. Des Weiteren werden Ergebnisse des umgesetzten Fusionsfilters prasentiert und diskutiert. Ein Ausblick mit Fokus auf Erweiterungsmoglichkeiten und die Einbindung weiterer Sensoren schliest die Betrachtungen ab.

Monitoring Deformation on Karasu Viaduct Using Gps & Precise Leveling Techniques

A project has been carried out to investigate the performance of GPS for determining deformations of large strong structures. As a test structure, a viaduct has been chosen 40 km away from West of Istanbul and built in a marsh area. Four epochs of GPS and levelling measurements have been done. Results obtained from epoch measurements are investigated and expressed in this paper. Also the needs of a special force centring equipment to achieve the accurate results are emphasised.