Inertial navigation is a device, which estimates its position, based on sensing external conditions (such as acceleration or angular velocity). It is widely used in variuos applications. Its presence in a drone vehicle for example, allows flight stabilization, by position estimation and feedback-based regulation algorithm execution. A smartphone makes a use of inertial navigation by detecting movement and flipping screen orientation. It is a ubiquitous part of many devices of everyday use, but before using filters and algorithms allowing to calculate the position, a calibration must first be applied to the device. This paper focuses on a separate calibration of each of the sensors - an accelerometer, gyroscope and magnetometer. The further step requires a cross–sensor calibration, and the third step is implementation of data filtration algotithm.
A research study aimed at developing a novel indoor positioning system is presented. The realized system prototype uses sensor fusion techniques to combine information from two sources: an in-house developed local Ultra-Wideband (UWB) radio-based ranging system and an inertial navigation system (INS). The UWB system measures the distance between two transceivers by recording the round-trip-time (RTT) of UWB radio pulses. Its principle of operation is briefly described, together with the main design features. Furthermore, the main characteristics of the INS and of the Extended Kalman Filter information fusion approach are presented. Finally, selected static and dynamic test scenario experimental results are provided. In particular, the advantages of the proposed information fusion approach are further investigated by means of a high dynamic test scenario.
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.
To reduce the influence of the static unbalance on an infrared missile guidance system, a new static unbalance measure system for the gimbals axes has been developed. Considering the coupling effects caused by a mass eccentricity, the static balance condition and measure sequence for each gimbal axis are obtained. A novel static unbalance test approach is proposed after analyzing the dynamic model of the measured gimbal axis. This approach is to drive the measured gimbal axis to do sinusoidal reciprocating motion in a small angle and collect its drive currents in real time. Then the static unbalance of the measured gimbal axis can be obtained by the current multi-cycle integration. Also a measuring system using the proposed approach has been developed. A balanced simulator is used to verify the proposed approach by the load and repeatability tests. The results show the proposed approach enhances the efficiency of the static unbalance measurement, and the developed measuring system is able to achieve a high precision with a greater stability.
A method for evaluating the dynamic characteristics of force transducers against small and short-duration impact forces is developed. In this method, a small mass collides with a force transducer and the impact force is measured with high accuracy as the inertial force of the mass. A pneumatic linear bearing is used to achieve linear motion with sufficiently small friction acting on the mass, which is the moving part of the bearing. Small and short-duration impact forces with a maximum impact force of approximately 5 N and minimum half-value width of approximately 1 ms are applied to a force transducer and the impulse responses are evaluated.
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.