Development of an Experimental Platform for Testing Autonomous UAV Guidance and Control Algorithms

With the United States\u27 push towards using unmanned aerial vehicles (UAVs) for more military missions, wide area search theory is being researched to determine the viability of multiple vehicle autonomous searches over the battle area. Previous work includes theoretical development of detection and attack probabilities while taking into account known enemy presence within the search environment. Simulations have been able to transform these theories into code to predict the UAV performance against known numbers of true and false targets. The next step to transitioning these autonomous search algorithms to an operational environment is the experimental testing of these theories through the use of surrogate vehicles, to determine if the guidance and control laws developed can guide the vehicles when operating in search areas with true and false targets. In addition to the challenge of experimental implementation, dynamic scaling must also be considered so that these smaller surrogate vehicles will scale to full size UAVs performing searches in real world scenarios. This research demonstrates the ability of a given sensor to use a basic ATR algorithm to identify targets in a search area based on its size and color. With this ability, the system\u27s target thresholds can also be altered to mimic real world UAV sensor performance

Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140665/1/1.I010280.pd

Location-Based Sensor Fusion for UAS Urban Navigation.

For unmanned aircraft systems (UAS) to effectively conduct missions in urban environments, a multi-sensor navigation scheme must be developed that can operate in areas with degraded Global Positioning System (GPS) signals. This thesis proposes a sensor fusion plug and play capability for UAS navigation in urban environments to test combinations of sensors. Measurements are fused using both the Extended Kalman Filter (EKF) and Ensemble Kalman Filter (EnKF), a type of Particle Filter. A Long Term Evolution (LTE) transceiver and computer vision sensor each augment the traditional GPS receiver, inertial sensors, and air data system. Availability and accuracy information for each sensor is extracted from the literature. LTE positioning is motivated by a perpetually expanding network that can provide persistent measurements in the urban environment. A location-based logic model is proposed to predict sensor availability and accuracy for a given type of urban environment based on a map database as well as real-time sensor inputs and filter outputs. The simulation is executed in MATLAB where the vehicle dynamics, environment, sensors, and filters are user-customizable. Results indicate that UAS horizontal position accuracy is most dependent on availability of high sampling rate position measurements along with GPS measurement availability. Since the simulation is able to accept LTE sensor specifications, it will be able to show how the UAS position accuracy can be improved in the future with this persistent measurement, even though the accuracy is not improved using current LTE state-of-the-art. In the unmatched true propagation and filter dynamics model scenario, filter tuning proves to be difficult as GPS availability varies from urban canyon to urban canyon. The main contribution of this thesis is the generation of accuracy data for different sensor suites in both a homogeneous urban environment (solid walls) using matched dynamics models and a heterogeneous urban environment layout using unmatched models that necessitate filter tuning. Future work should explore the use of downward facing VISION sensors and LiDAR, integrate real-time map information into sensor availability and measurement weighting decisions, including the use of LTE for approximate localization, and more finely represent expected measurement accuracies in the GPS and LTE networks.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110361/1/jrufa_1.pd

OTDOA/GPS Fusion for Urban UAS Navigation using Particle Filtering Techniques

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106497/1/AIAA2013-4878.pd