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.
One of the little described problems in hydrostatic drives is the fast changing runs in the hydraulic line of this drive affecting the nature of the formation and intensity of pressure pulsation and flow rate occurring in the drive. Pressure pulsation and flow rate are the cause of unstable operation of servos, delays in the control system and other harmful phenomena. The article presents a flow model in a hydrostatic drive line based on fluid continuity equations (mass conservation), maintaining the amount of Navier-Stokes motion in the direction of flow (x axis), energy conservation (liquid state). The movement of liquids in a hydrostatic line is described by partial differential equations of the hyperbolic type, so modeling takes into account the wave phenomena occurring in the line. The hydrostatic line was treated as a cross with two inputs and two outputs, characterized by a specific transmittance matrix. The product approximation was used to solve the wave equations. An example of the use of general equations is presented for the analysis of a miniaturized hydrostatic drive line fed from a constant pressure source and terminated by a servo mechanism.
The Land and Property Register (LPR) also called the Cadastre by the legislator should function in accordance with regulations in force, meet expectations of the public and provide universal access to Register data for its users. Beyond any doubt, credibility and usefulness of data in this public register are affected by the manner it is kept, which generally in-cludes active and passive approach. If the LPR is kept in an active manner and constantly up to date, its data is very useful. The qualitative aspect of the land and buildings database’s records establishes the calculation accuracy of the owners’ land parcels evidenced in the Land and Mortgage Registers, which protect the ownership right to the property. In order to ensure that the plot of land is unequivocally and correctly measured, it is necessary to establish breakpoints of the parcels’ bounda-ries in the presence of the interested parties.
Research conducted on the possibility of using the unmanned aerial vehicle (UAV) for measuring purposes indicates immense probability where this technology may be used for the selected details of group I (most accurately located) in modernization of land and buildings registers.
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.
A navigation complex of an unmanned flight vehicle of small class is considered. Increasing the accuracy of navigation definitions is done with the help of a nonlinear Kalman filter in the implementation of the algorithm on board an aircraft in the face of severe limitations on the performance of the special calculator. The accuracy of the assessment depends on the available reliable information on the model of the process under study, which has a high degree of uncertainty. To carry out high-precision correction of the navigation complex, an adaptive non-linear Kalman filter with parametric identification was developed. The model of errors of the inertial navigation system is considered in the navigation complex, which is used in the algorithmic support. The procedure for identifying the parameters of a non-linear model represented by the SDC method in a scalar form is used. The developed adaptive non-linear Kalman filter is compact and easy to implement on board an aircraft.