The advance of MEMS-based inertial sensors successfully expands their applications to small unmanned aerial vehicles (UAV), thus resulting in the challenge of reliable and accurate in-flight alignment for airborne MEMS-based inertial navigation system (INS). In order to strengthen the rapid response capability for UAVs, this paper proposes a robust in-flight alignment scheme for airborne MEMS-INS aided by global navigation satellite system (GNSS). Aggravated by noisy MEMS sensors and complicated flight dynamics, a rotation-vector-based attitude determination method is devised to tackle the in-flight coarse alignment problem, and the technique of innovation-based robust Kalman filtering is used to handle the adverse impacts of measurement outliers in GNSS solutions. The results of flight test have indicated that the proposed alignment approach can accomplish accurate and reliable in-flight alignment in cases of measurement outliers, which has a significant performance improvement compared with its traditional counterparts.
This paper describes a synthetic aperture radar system for tactical-level imagery intelligence installed on board an unmanned aerial vehicle. Selected results of its tests are provided. The system contains interchange-able S-band and Ku-band linear frequency-modulated, continuous wave radar sensors that were built within a frame of a research project named WATSAR, conducted by the Military University of Technology and WB Electronics S.A. One of several algorithms of radar image synthesis, implemented in the scope of the project, is described in this paper. The WATSAR system can create online and off-line radar images.
The paper presents a method of calculation of position deviations from a theoretical, nominally rectilinear trajectory for a SAR imaging system installed on board of UAV. The UAV on-board system consists of a radar sensor, an antenna system, a SAR processor and a navigation system. The main task of the navigation part is to determine the vector of differences between the theoretical and the measured trajectories of UAV center of gravity. The paper includes chosen results of experiments obtained during ground and flight tests.
Understanding the factors that influence the quality of unmanned aerial vehicle (UAV)-based products is a scientifically ongoing and relevant topic. Our research focused on the impact of the interior orientation parameters (IOPs) on the positional accuracy of points in a calibration field, identified and measured in an orthophoto and a point cloud. We established a calibration field consisting of 20 materialized points and 10 detailed points measured with high accuracy. Surveying missions with a fixed-wing UAV were carried out in three series. Several image blocks that differed in flight direction (along, across), flight altitude (70 m, 120 m), and IOPs (known or unknown values in the image-block adjustment) were composed. The analysis of the various scenarios indicated that fixed IOPs, computed from a good geometric composition, can especially improve vertical accuracy in comparison with self-calibration; an image block composed from two perpendicular flight directions can yield better results than an image block composed from a single flight direction.
The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.