doi: 10.17586/2226-1494-2019-19-6-1139-1150


EFFICIENCY RESEARCH OF SIGNAL RECOVERY ALGORITHMS WITH LONG GAPS AND RARE ARRIVAL OF MEASUREMENTS

O. V. Zaitsev


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Zaitsev O.V. Efficiency research of signal recovery algorithms with long gaps and rare arrival of measurements. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 6, pp. 1139–1150 (in Russian). doi: 10.17586/2226-1494-2019-19-6-1139-1150


Abstract
Subject of Research. Efficiency study of cameral signal recovery algorithms is carried out in the presence of single long gaps and rare arrival of measurements. Quantitative comparison of the algorithms is performed by modeling and cameral processing of satellite navigation receiver real solutions. The standard error is an algorithm measure of efficiency. Method. The algorithm is considered based on a quadratic model taking into account constraints on the signal size and its derivative. The algorithm is known from the literature, however, it is used for the first time in satellite navigation problems. Moreover, comparison is made with the other two algorithms: quadratic approximation without regard to constraints and linear interpolation. Main Results. After analyzing the results, the following recommendations have been developed on the use of recovery algorithms in order to achieve the minimum mean square error of recovery. It is established that the quadratic approximation with constraints is the best of the considered algorithms in terms of accuracy; however, when recovering the signal during the long-term measurement absence at the beginning and at the end of the gap, it is better to use linear interpolation. In order to achieve the minimum standard error in the central part of the gap, it is recommended to use the algorithm with constraints and break down a fragment of the processed measurement implementation so that no more than one polynomial interval junction of the restored signal is located on the measurement absence section. For short measurement intervals to the left and to the right of the gap, the best option is to split the implementation fragment into 2 intervals. In case of the signal restoration under conditions of rarely received measurements, it is advisable to choose the interval duration of the polynomial representation less than the period of measurement discreteness. Practical Relevance. The application of the developed algorithms can improve the positioning accuracy for the users of global-positioning satellite systems, however, their application area may be more extensive and include post-processing of field measurements in geodesy and mapping tasks.

Keywords: signal estimation, modeling, cameral processing, field data, satellite navigation receiver

Acknowledgements. This work was financially supported by the Russian Foundation for Basic Research, project No. 18-08-01101А.

References
  1. Little R.J.A., Rubin D.B. Statistical analysis with missing data. John Wiley & Sons, 1987, 278 p.
  2. Efimov A.S. Clusterization problem solution by competitive learning for incomplete statistical data. Vestnik of Lobachevsky University of Nizhni Novgorod, 2010, no. 1, pp. 220–225. (in Russian)
  3. Iagaru A., McDougall I.R.Treatment of thyrotoxicosis. Journal of Nuclear Medicine, 2007, vol. 48, no. 3, pp. 379–389.
  4. Vasilev V.I., Shevchenko A.I. Missed data  recovery in empirical tables. Artificial Intelligence, 2003, no. 3, pp. 317–324. (in Russian)
  5. Kondrashov D., Shprits Y., Ghil M. Gap filling of solar wind data by singular spectrum analysis. Geophysical research letters, 2010, vol. 37, no. 15, pp. L15101. doi: 10.1029/2010GL044138
  6. Voloshko A.V., Bederak Y.S., Lutchyn T.M., Kudritskiy M.Yu. The problem of accounting data recovery on chemical enterprise. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2014, vol. 324, no. 5, pp. 101–107. (in Russian)
  7. Crocoll P., Görcke L., Trommer G.F., Holzapfel F. Unified model technique for inertial navigation aided by vehicle dynamics model. Navigation, Journal of the Institute of Navigation, 2013, vol. 60, no. 3, pp. 179–193. doi: 10.1002/navi.39
  8. Crocoll P., Seibold J., Scholz G., Trommer G.F. Model-aided navigation for a quadrotor helicopter: A novel navigation system and first experimental results. Proc. Institute of Navigation International Technical Meeting 2014 (ITM 2014), 2014, pp. 384–406.
  9. Stepanov O.A. Basics on the theory of assessment with applications to the tasks of processing navigation information. Part 2. Introduction to the theory of filtration. St. Petersburg, State Research Center of the Russian Federation Concern CSRI Elektropribor, JSC, 2012, 417 p. (in Russian)
  10. Dmitriev S.P., Stepanov O.A. Multiple model filtering for navigation problems of information processing. Radiotekhnika, 2004, no. 7, pp. 11–17. (in Russian)
  11. Dmitriev S.P., Koshaev D.A., Stepanov O.A. Multichannel filtration and its application in removing ambiguity when positioning objects by using the GPS. Journal of Computer and Systems Sciences International, 1997, vol. 36, no. 1, pp. 57–62.
  12. Koshaev D.A. Method of dummy measurements for multiple model estimation of processes in a linear stochastic system. Automation and Remote Control, 2016, vol. 77, no. 6, pp. 1009–1030. doi: 10.1134/S0005117916060060
  13. Loparev A.V., Stepanov O.A., Kulakova V.I. Robust filtering using the method of local approximations of power spectral densities. Gyroscopy and Navigation, 2014, vol. 5, no. 1, pp. 40–43. doi: 10.1134/S2075108714010088
  14. Kozionov A.P., Pyayt A.L., Mokhov I.I., Ivanov Yu.P. Research on gap-filling algorithms for dike health monitoring systems. St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunication and Control Systems, 2015, no. 2-3(217-222), pp. 93–104. (in Russian). doi: 10.5862/JCSTCS.217-222.8
  15. Golyandina, N.E. The «Caterpilla»'-SSA: method for analysis of time series. St. Petersburg: St. Petersburg State University, 2003, 87 p. (in Russian)
  16. Refan M.H., Dameshghi A., Kamarzarrin M. Utilizing hybrid recurrent neural network and genetic algorithm for predicting the pseudo-range correction factors to improve the accuracy of RTDGPS. Gyroscopy and Navigation, 2015, vol. 6, no. 3, pp. 197–206. doi: 10.1134/S207510871503013X
  17. Dmitriev S.P., Koshaev D.A. Estimation of continuously differentiable signal with allowance for constraints. Automation and Remote Control, 2011, vol. 72, no. 7, pp. 1458–1473. doi: 10.1134/S0005117911070149
  18. Zaitsev O.V. Improvement in accuracy of assessment of stochastically uncertain processes with the account for restrictions in the form of inequalities. Journal of Instrument Engineering, 2017, vol. 60, no. 3, pp. 211–220. (in Russian). doi: 10.17586/0021-3454-2017-60-3-211-220
  19. Zaitsev O.V. Prediction of GNSS differential corrections taking into account constraints. Proceedings of the TSU. Technical sciences, 2019, no. 6, pp. 245–258. (in Russian)


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