Marcel Maier

Zusammenfassung

Constrained by tight cost calculations and relatively small payload capabilities, we propose an integrated navigation system for urban air mobility flight operation using low-cost hardware. Our concept features a WiFi-based Positioning System (WPS), which complements GNSS, a barometer and an Inertial Navigation System (INS), which makes use of multiple Inertial Measurement Units (IMUs). These four sensor systems are fused using a loosely coupled Kalman filter. Compared to basic GNSS/INS-fusion, our approach has an improved robustness and accuracy. The WPS consists of an off-the-shelf WiFi router mounted to an Unmanned Aerial Vehicle (UAV) and a set of stationary WiFi routers. If the positions of the stationary routers are known, Round Trip Times (RTTs) to the UAV mounted router can be used to calculate the position of the UAV (see figure 1). Flight tests have shown that the WPS’s position accuracy is comparable to GNSS code-phase positioning. The WiFi routers are also used as a bidirectional data link, providing the UAV with measured RTTs and telemetry and enabling down-streaming of the navigation solution for monitoring purposes. The INS makes use of a multi-IMU array, in which specific forces and angular rates of an arbitrary number of Single IMUs (SIMUs) are fused consistently in a B-spline Kalman filter. The B-spline fusion model acts as a low-pass filter which allows to separate actual dynamic motion from sensor noise. In general, this concept provides redundancy and has a better precision than a SIMU (see figure 2). The proposed integrated navigation system is implemented in the open-source software framework INSTINCT (Institute of Navigation, Stuttgart, Toolkit for Integrated Navigation Concepts and Training). The potential of this approach is validated by simulations and flight tests and the results will be summarized in our presentation.

Zusammenfassung

Navigating during GNSS outages is becoming more important due to a significant increase of jamming and spoofing attacks and motivated by unmanned operations in urban environments. Inertial Measurement Units (IMUs) are usually the choice to mitigate the GNSS signal losses, but not all applications allow for IMUs that are precise and stable enough to rely solely on inertial navigation over longer periods of time. Payload capacity – either mass or volume – can impose strict limitations on the usage of IMUs and the demand for low-cost solutions can define additional boundary conditions. In this study, we show that fusing multiple IMUs with B-splines can improve the navigation solution considerably. The developed algorithm can fuse angular rates and accelerations from an arbitrary number of Single IMUs (SIMUs) using a Kalman filter, which outputs one combined stream of Virtual IMU (VIMU) measurements. Expressing the angular rates and the accelerations therein as the sum of B-spline base functions makes it possible to resolve temporal changes at a user-defined resolution, while only a few parameters have to be chosen. The B-spline approach does not only allow a dynamic and continuous representation of the angular rates and the accelerations which occur along a given trajectory, but also inherently avoids the problem of high-frequency noise. Several high data rate IMUs can be fused, even if sampling rates or the quality of the measurements are different. The concept of the fusion is also inherently redundant: If one SIMU fails, the fusion keeps on working with the remaining SIMUs. The gain in precision of the navigation solution is directly dependent on the number of SIMUs. The formal error could theoretically be reduced by the square root of the number of SIMUs, if averaging independent measurements. The IMU fusion based on B-splines is implemented in our open source software framework INSTINCT (INS Toolkit for Integrated Navigation Concepts and Training). Its performance will be shown in a Monte Carlo simulation of a pure inertial navigation solution in comparison to a SIMU. A dynamic simulation will show the applicability to GNSS outages, where the VIMU’s combined measurement is used in GNSS/INS-fusion. This setup is validated by drive and flight tests, utilizing an IMU sensor array that was constructed in-house and that consists of five low-cost SIMUs.

Zusammenfassung

Low-cost Inertial Measurement Units (IMUs) are typically subject to biases and noise. Within this study we aim to reduce these effects by combining measurements in a novel way. An integrated random walk is proposed to model acceleration and angular rates from IMU sensors which are attached to the same platform. Thereby, a Kalman Filter (KF) is used to merge the observations from the different sensors in a single state model that considers also relative biases of individual sensors, with respect to one reference sensor. Such a KF has been tested on an array of IMUs that consists of five identical devices. Figure 1 shows the Allan Deviation (ADEV) of the fused Virtual IMU (VIMU) in comparison to each single IMU (here the vertical component of the angular rate, the other components are similar). For small averaging periods τ, the ADEV is reduced considerably. Specifically, the impact of angle random walk and the bias instability are reduced. With rising τ, the VIMU’s ADEV converges towards the reference sensor’s, due to long term drifts which are not covered by the state space of the KF. In general, it can be stated that the VIMU’s ADEV is lower than any of the single devices which indicates that such an approach has the potential to improve the accuracy of low-cost inertial sensors by fusing the output from an array of such sensors. In our presentation we demonstrate the concept of fusing multiple IMUs within the flow-based software framework INSTINCT (INS Toolkit for Integrated Navigation Concepts and Training), with synthetic data and measurements from an array of IMUs. We discuss the potential of this approach and show that the output of the fusion algorithm, which contains acceleration and angular rates, can be fed into subsequent processing like in classical INS/GNSS-fusion.