Compensation of randomly delayed and lost measurements in line of sight guidance law by adaptive Kalman filter

Document Type : Research Paper

Authors

Abstract

Measurement data of guidance sensors are commonly lost and delayed in ground to air missile systems. These phenomena affect the missile efficiency. Kalman filter is used to estimate the variables needed in implementation of guidance law. But the performance of Kalman filters is dependent on the knowing exact model of the system. In practical problems, the exact parameters of the systems model, especially the one of delay and loss is not known. In this study, adaptive Kalman filter is employed to compensate the uncertainty in the stochastic model of delay and loss which is employed in a line of sight guidance algorithm of a defensive missile. A set of recursive difference equations are used to obtain the adaptive filter gains. The problem is formulated in presence of delayed and missing measurements, then the adaptive filter structure and correction factor are presented. Simulation results are presented to verify the improved performance of the approach.

Keywords

Main Subjects

References

[1] E. Mirzazadeh, Route Analysis in Line of Sight Guidance Algorithm, MS Thesis, Department of Electrical & Computer Engineering, K. N. Toosi University of Technology, Tehran, 2010. (in Persianفارسی ).
[2] S. H. Sajjadi, S. H. Jalali Naini, Second-Order optimal line-of-sight guidance for stationary targets, Modares Mechanical Engineering, Vol. 15, No. 11, pp. 387-395, 2015 (in Persianفارسی )
[3] J. Holloway, M. Krstic, Predictor Observers for Proportional Navigation Systems Subjected to Seeker Delay, IEEE Transactions On Control Systems Technology, vol.24, no. 6, 2016, pp. 2002-2015.
[4] X. Lihua, H. Zhang, Control and estimation of systems with input/ output delays, Springer Berlin Heidelberg, 2007.
[5] S. Sun, L. Xie, W. Xiao, N. Xia, Optimal Filtering for Systems with Multiple Packet Dropouts, IEEE transactions on circuits and systems—II, vol. 55, no. 7, 2008, pp. 695-699.
[6] S. Sun, Optimal Linear Filters for Discrete- time Systems with Randomly Delayed and Lost Measurements with/without Time Stamps, IEEE Transactions on Automatic Control, vol.5, no. 7, 2013, pp. 1447-1466.
[7] Y. H. Yang, M. Y. Fu, H. S. Zhang, State Estimation Subject to Random Communication Delays, Acta Automatica Sinica, vol.39, no. 3, 2013, pp. 237-243.
[8] S. Sun, Linear minimum variance estimators for systems with bounded random measurement delays and packet dropouts, Signal Processing, vol. 89, 2009, pp. 1457 –1466.
[9] M. Moayedi, Y. C. Soh, Y. K. Foo, Optimal Kalman Filtering with random sensor delays, packet dropouts and missing measurement, American Control Conference, 2009, pp. 3404-3410.
[10] H. Zhang, G. Feng, C. Han, Linear estimation for random delay systems, Systems & Control, Letters, vol. 60, 2011, pp. 450–459.
[11] D. Chen, L. Xu, J. Du, Optimal filtering for systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements, Communications in Nonlinear Science and Numerical Simulation, vol. 32, 2016, pp. 211–224.
[12] B. Safarinejadian, M. Mazaffari, a new state estimation method for unit time-delay systems based on Kalman Filter, 21st Iranian Conference on Electrical Engineering, 2013, pp. 1-5.
[13] S. Sun, G. Wang, Modeling and estimation for networked systems with multiple random transmission delays and packet losses, Systems & Control Letters, vol. 73, 2014, pp. 6–16.
[14] S. Sun, J. Ma, Linear estimation for networked control systems with random transmission delays and packet dropouts, Information Sciences, vol.269, 2014, pp. 349–365.
[15] H. Rezaei, R. Mahboobi, M. H. Sedaghi, Improved Kalman filtering for systems with randomly delayed and lost measurements, Circuits Systems and Signal Processing, vol. 13, no. 7, 2014, pp. 2217-2236.
[16] I. Peñarrocha, R. Sanchis, P. Albertos, Estimation in multisensory networked systems with scarce measurements and time varying delays, Systems and Control Letters, vol. 61, no. 4, 2012, pp. 555–562.
[17] X., Guochang, Sciences of Geodesy – I: Advances and Future Directions, Springer-Verlag Berlin Heidelberg, 2010.
[18] G. Chang, Kalman filter with both adaptivity and robustness, Journal of Process Control, vol. 24, 2014, pp. 81–87.
[19] M. Moayedi, Y. K. Foo, Y. C. Soh, Adaptive Kalman filtering in networked systems with random sensor delays, multiple packet dropouts and missing measurements, IEEE Transactions on Signal Processing, vol. 58, 2010, pp. 1577–1588.
[20] S. Chen, Y. Li, G. Qi, A. Sheng, Adaptive Kalman Estimation in target tracking mixed with random one step delays, stochastic bias measurement and missing measurements, Discrete Dynamic in Nature and Society, 2013, pp. 1-14.
[21] H. Wu, H. Ye, State estimation for networked systems: an extended IMM algorithm, International Journal of Systems Science, vol. 44, 2013, pp. 1274-1289.
[22] S. Torkamani, E. A. Butcher, Optimal estimation of parameters and states in stochastic time-varying systems with time delay, Communications in Nonlinear Science and Numerical Simulation, vol. 1,2013, pp. 188–201.
[23] Y. Yang, Adaptively robust Kalman filters with applications in navigations, in Sciences of Geodesy, Berlin Heidelberg: Springer, 2010, pp. 49-82.
[24] A. Nikfetrat, R. Mahboobi Esfanjani, Adaptive Kalman Filtering for Systems Subject to Randomly Delayed and Lost Measurements, Circuits Systems and Signal Processing, 2017, In press.
[25] K., Karl-Rudolf, Parameter estimation and hypothesis testing in linear model, Newyork: Springer-Verlag Berlin Heidelberg, 1999.

Files

Share

How to cite

Statistics