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

Open access

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.

References

  • [1] Rivkin, S.S. (2002). Definition of Dynamic Errors of Gyro-Instruments on a Moving Base. Moscow: Azimut.

  • [2] Dichev, D., Koev, H., Bakalova, T., Louda, P. (2014). A Model of the Dynamic Error as a Measurement Result of Instruments Defining the Parameters of Moving Objects. Measurement Science Review, 14(4), 183−189.

  • [3] Pan, H., Tian, S., Ye, P. (2010). An Adaptive Synthesis Calibration Method for Time-Interleaved Sampling Systems. Metrol. Meas. Syst., 17(3), 405−414.

  • [4] Grant, P.M., Cowan, C.F., Adams, P.F. (1985). Adaptive filters. Englewood Cliffs. NJ: Prentice-Hall.

  • [5] Borkowski, D., Barczentewicz, S. (2014). Power Grid Impedance Tracking with Uncertainty Estimation Using Two Stage Weighted Least Squares. Metrol. Meas. Syst., 21(1), 99−110.

  • [6] Zhu, R., Sun, D., Zhou, Z., Wang., D. (2007). A linear fusion algorithm for attitude determination using low cost MEMS-based sensors. Measurement, 40(3), 322-328.

  • [7] Yuling, Z. (2015). The Accurate Marketing System Design Based on Data Mining Technology: A New Approach. AMEII 2015, Zhengzhou, China, 1952−1956.

  • [8] Dichev, D., Koev, H., Louda, P. (2014). A Measuring System with an Additional Channel for Eliminating the Dynamic Error. Journal of Theoretical and Applied Mechanics, 44(1), 3−20.

  • [9] Dichev, D., Koev, H., Bakalova, T., Louda, P. (2016). A Measuring Method for Gyro-Free Determination of the Parameters of Moving Objects. Metrol. Meas. Syst., 23(1), 107−118.

  • [10] Rivkin, S.S. (1974). Kalman Method for Optimal Filtration and Its Application in Navigation Systems. St. Petersburg: Sudostroene.

  • [11] Sun, S., Deng, Z. (2004). Multi-sensor optimal information fusion Kalman filter. Automatica, 40(6), 1017−1023.

  • [12] Danilov, А.Т. (2001). A Gyroscopic Measuring System for Parameters of Moving Objects. Problems of Special Machinebuilding Magazine, 4(1), 178−181.

  • [13] Furrera, R., Bengtsson, T. (2007). Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. Journal of Multivariate Analysis, 98(2), 227−255.

  • [14] Bordai, И., Necvetaev, I. (1999). Ship Shaking at Rough Sea. St. Petersburg: Sudostroenie.

  • [15] Lukianov, D. (2001). Inertial Navigation Systems of Sea Vessels. St. Petersburg: Sudostroenie.

  • [16] Ventcel, E. S., Ovcharov, L.A. (2007). Random Processes Theory and Its Engineering Application. Moscow: Visha Shkola.

  • [17] Lee, S.H., Song, J.B., Choi, W.C., Hong, D. (2003). Position control of a Stewart platform using inverse dynamics control with approximate dynamics. Mechatronics, 13(6), 605−619.

  • [18] Tsai, M.S., Shiau, T.N., Tsai, Y.J., Chang, T.H. (2003). Direct kinematic analysis of a 3-PRS parallel mechanism. Mechanism and Machine Theory, 38(1), 71−83.

  • [19] Terrier, M., Dugas, A., Hascoet, J. (2004). Qualification of parallel kinematics machines in high-speed milling on free form surfaces. International Journal of Machine Tools and Manufacture, 44(7), 865−877.

Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 15 15 15
PDF Downloads 3 3 3