On the application of estimation and correction algorithms in the system for measuring vibration parameters of aircraft structural elements

Nowadays, high-precision measurement of aircraft vibration parameters during its main operations modes, including in-flight operation mode, is still considered an important scientific and technical field of study and research. These kinds of measurements are usually conducted in order to analyze the airplane vibration properties and characteristics, which serves in diagnosing the state of its structure, predicting the appearance and development of defects and deformations, as well as to prevent or avoid the influence of dangerous phenomena such as flutter, buffeting, etc. In this article, the authors present the primary results of their work to build a system designed to measure such airplane vibration parameters. In comparison with the existing analogous systems, the new proposed system makes use of traditional vibrometric measurement methods in combination with approaches typical for solving orientation and navigation problems. So, the article discusses the principles of constructing a measurement system of vibration parameters of aircraft structural elements using the example of a system for measuring aircraft wing vibrations using MEMS IMU units and data fusion technology. A brief review of the main existing solutions in this research field is carried out, and the relevance and expediency of the proposed version of the system is substantiated. The basic components and structure of the proposed system are presented, including MEMS IMU units, a displacement sensor, and an onboard navigation system. The basic principles of the system operation are described based on the use of data from the displacement sensor, inertial measurements and optimal Kalman estimation. The main algorithms for the system operation are presented, including algorithms for inertial measurements, estimation and correction, as well as the actual algorithm for calculating vibration parameters. In addition, the mathematical errors models of the main measurements units of the system are presented. The article also presents simulation results, which are encouraging, and they demonstrate the performance of the system and its expected relatively high accuracy characteristics, which in turns confirms the expected efficiency of its application and the prospects of the chosen direction of research and development.

An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.

UWB and MEMS IMU Integrated Positioning Algorithm for a Work-Tool Tracking System

In this paper, we address a system that can accurately locate and monitor work tools in a complex assembly process, such as automotive production. Our positioning monitoring system is positioned by a combined sensor of the UWB module and the MEMS IMU (inertial measuring unit) sensor based on the extended Kalman filter. The MEMS IMU sensor provides the positioning calibration information. The proposed method incorporates IMU and UWB positioning to compensate for errors that can only occur in UWB positioning through the extended Kalman filter (EKT). This EKT is improved by the error dynamic equation derived from the sparse state-space matrix. Also, the proposed method computes the transmission time and distance between the tag and anchor of the UWB module by the TWR (two-way range) system. The tag of a mobile node, which is attached to a moving tool, measures the position of the work tool and transmits the position coordinate data to the anchor. Here, the proposed method uses the trilateration localization method by the confidence distance compensation to prevent the distance error by obstacles and changes in the indoor environment. Experimental results verified that the proposed method confirms whether a specific tool is accurately used according to the prescribed regulations and has more positioning accuracy than the conventional methods.

Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications.