An Algorithm for Improving the Accuracy of Systems Measuring Parameters of Moving Objects

Abstract

The paper considers an algorithm for increasing the accuracy of measuring systems operating on moving objects. The algorithm is based on the Kalman filter. It aims to provide a high measurement accuracy for the whole range of change of the measured quantity and the interference effects, as well as to eliminate the influence of a number of interference sources, each of which is of secondary importance but their total impact can cause a considerable distortion of the measuring signal. The algorithm is intended for gyro-free measuring systems. It is based on a model of the moving object dynamics. The mathematical model is developed in such a way that it enables to automatically adjust the algorithm parameters depending on the current state of measurement conditions. This makes possible to develop low-cost measuring systems with a high dynamic accuracy. The presented experimental results prove effectiveness of the proposed algorithm in terms of the dynamic accuracy of measuring systems of that type.

A Gyro-Free System for Measuring the Parameters of Moving Objects

“…Taking into account the characteristics of the quantities constituting the measurement environment where the measuring instruments under consideration operate, it can be concluded that the best form of eliminating the influence of the interference sources is the Kalman filter. The characteristics of this algorithm fit very well into the solution of a number of problems emerging in the process of optimization of the accuracy characteristics of the measuring instruments defining the parameters of the above listed moving objects [8,10,13,14,17]. Therefore, a module for signal processing by means of the Kalman algorithm is included in each measurement channel of the block diagram (Fig.2.).…”

Geometric Synthesis of Involute Planetary Gears With Connected Gear Wheels of Type 2k-H

“…The quality indicators of the planetary gears with asymmetric profile depend on the parameters of the tool, its coefficient of displacement and the parameter q: When selecting the number of teeth of the gear wheels, so that the parameter q has a value of zero the following correlations for the alignment condition of the gear are obtained: (6) After determining the engagement angles for the two parts of the composite profile a method for calculating the vertex circle of the wheel is selected. Irrespective of the adopted method of making the wheels, the following correlation should always be observed: (7) where d 12 a2 is diameter of the vertex circle for wheel 2, but determined by external meshing with wheel 1; -diameter of vertex circle for wheel 2, but determined by internal meshing with wheel 3;…”

Error Mitigation Algorithm Based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle

“…Compared with the Wiener filtering, this algorithm can adjust the parameters of the filter by self-learning without knowing the prior information of input signals and noises and can thus obtain optimal estimation. However, the effect of adaptive filtering is difficult to be controlled [15]. Unlike the traditional Wiener filtering and KF algorithms, algorithms based on the least squares criterion, such as th recursive least squares method [16], and QR decomposition of the minimum method, regard the minimum squared error sum as the optimization target.…”