Low-cost sensors based multi-sensor data fusion techniques for RPAS navigation and guidance

In order for Remotely Piloted Aircraft Systems (RPAS) to coexist seamlessly with manned aircraft in non-segregated airspace, enhanced navigational capabilities are essential to meet the Required Navigational Performance (RNP) levels in all flight phases. A Multi-Sensor Data Fusion (MSDF) framework is adopted to improve the navigation capabilities of an integrated Navigation and Guidance System (NGS) designed for small-sized RPAS. The MSDF architecture includes low-cost and low weight/volume navigation sensors suitable for various classes of RPAS. The selected sensors include Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). A loosely integrated navigation architecture is presented where an Unscented Kalman Filter (UKF) is used to combine the navigation sensor measurements. The presented UKF based VBS-INS-GNSS-ADM (U-VIGA) architecture is an evolution of previous research performed on Extended Kalman Filter (EKF) based VBS-INS-GNSS (E-VIGA) systems. An Aircraft Dynamics Model (ADM) is adopted as a virtual sensor and acts as a knowledge-based module providing additional position and attitude information, which is pre-processed by an additional/local UKF. The E-VIGA and U-VIGA performances are evaluated in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) by exploring a representative cross-section of this RPAS operational flight envelope. The position and attitude accuracy comparison shows that the E-VIGA and U-VIGA systems fulfill the relevant RNP criteria, including precision approach in CAT-II. A novel Human Machine Interface (HMI) architecture is also presented, whose design takes into consideration the coordination tasks of multiple human operators. In addition, the interface scheme incorporates the human operator as an integral part of the control loop providing a higher level of situational awareness

Low-cost MEMS-INS/GPS integration using nonlinear filtering approaches

Some important key issues in GNSS/INS integration mainly arise in the field of creating and developing low-cost, robust and at the same time highly accurate navigation systems, putting a focus of interest onto powerful sensor fusion algorithms. The so-called tightly-coupled integration is one of the most promising approaches to fuse the GNSS (global navigation satellite systems) data with INS (inertial navigation system) measurements. However, when modeling the underlying problem, the system process and observation models turn out to be nonlinear, and the GNSS stochastic measurement errors are often non-Gaussian distributed (e.g., due to multipath effects). Among other estimation approaches, the so-called particle filter (PF) as a nonlinear/non-Gaussian estimation method is especially theoretically attractive to be used in this field. However, its large computational burden usually limits its practical usage. In order to reduce the computational burden without degrading the system estimation accuracy, recently, an unscented particle filter (UPF) has been proposed, which combines the PF with the unscented Kalman filter (UKF). In this thesis, only one UKF is used in the algorithm, and the re-sampling step is not required anymore. Thus, the number of particles can be largely reduced, and the implementation of the PF on a hardware platform turns out to be feasible.Aktuelle Entwicklungen auf dem Gebiet der Fusion von inertialer Navigation und satellitengestützten Positionierungsverfahren zielen klar auf kosteneffiziente, robuste und gleichzeitig hochpräzise Lösungen ab. Leistungsfähige Sensordatenfusionsansätze spielen hier eine Schlüsselrolle, wobei die sogenannte "Tightly Coupled Integration" zur Fusion der satellitengestützten Navigationsdaten mit den Messdaten eines inertialen Systems besonders vielversprechend erscheint. Als erschwerender Umstand ergeben sich hier allerdings nichtlineare Prozess- und Beobachtungsmodelle, die in Verbindung mit nicht länger gaußverteilten Beobachtungsfehlern, beispielsweise aufgrund von Mehrwegeausbreitung, nichtlineare, möglichst optimale Datenfusionsverfahren, wie beispielsweise Partikelfilter-Ansätze erfordern. Theoretisch elegant und leistungsfähig auf der einen Seite, benötigen diese Ansätze in der praktischen Realisierung vielfach eine ungemein hohe Anzahl von einzelnen "Partikeln", so dass der hierdurch verursachte Berechnungsaufwand die praktische Einsatzfähigkeit unter Echtzeitbedingungen vielfach entweder im Hinblick auf die Filterperformance oder auf die Taktzeit limitiert. Ein Ansatz zur Lösung dieser Problematik besteht in der Kombination eines Partikelfilters mit einem Unscented Kalman Filter. Hierbei wird der sonst bei Partikelfiltern übliche, aber zeitaufwändige, Resampling Schritt nicht mehr benötigt. Auch die Anzahl der benötigten Partikel kann stark reduziert werden, so dass eine Realisierung auf einer Signalprozessorplattform möglich wird