A Coordinate System Choice when Information Signal Processing by Kalman Filter
Abstract
The article describes information signal smoothing problems in the technical systems with movable platform. In such technical systems different coordinate system are used and there is a signal conditioning from one coordinate system into one another when actuating element controlling. A choice of coordinate system, which is used for information signal processing, must take account of behavior of processed signals. Kalman filter allows providing signal processing required accuracy, if there is taken account of a system of dimensioning.
References
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24. Sun S.L., Deng Z.L. Multi-Sensor Optimal Information Fusion Kalman Filter. Automatica. 2004; 40(6):1017-1023. (In Eng.) doi: https://doi.org/10.1016/j.automatica.2004.01.014
25. Feddaoui A., Boizot N., Busvelle E., Hugel V. High-gain Extended Kalman Filter for Continuous-Discrete Systems with Asynchronous Measurements. International Journal of Control. 2018; 93(8):2001-2014. (In Eng.) doi: https://doi.org/10.1080/00207179.2018.1539525
26. Luo R.C., Chang C.C., Lai C.C. Multisensor Fusion and Integration: Theories, Applications, and its Perspectives. IEEE Sensors Journal. 2011; 11(12):3122-3138. (In Eng.) doi: https://doi.org/10.1109/JSEN.2011.2166383
27. Castanedo F. A Review of Data Fusion Techniques. The Scientific World Journal. 2013; 2013:704504. (In Eng.) doi: https://doi.org/10.1155/2013/704504
28. Smith D., Singh S. Approaches to Multisensor Data Fusion in Target Tracking: A Survey. IEEE Transactions on Knowledge and Data Engineering. 2006; 18(12):1696-1710. (In Eng.) doi: https://doi.org/10.1109/TKDE.2006.183
29. Durrant-Whyte H., Henderson T.C. Multisensor Data Fusion. In: Siciliano B., Khatib O. (eds.) Springer Handbook of Robotics. Springer, Berlin, Heidelberg; 2008. p. 585-610. (In Eng.) doi: https://doi.org/10.1007/978-3-540-30301-5_26
2. Armesto L., Tornero J., Vincze M. Fast Ego-Motion Estimation with Multi-Rate Fusion of Inertial and Vision. The International Journal of Robotics Research. 2007; 26(6):577-589. (In Eng.) doi: https://doi.org/10.1177/0278364907079283
3. Chong C.-Y., Mori S., Barker W.H., Chang K.-C. Architectures and Algorithms Track Association and Fusion. IEEE Aerospace and Electronic System Magazine. 2000; 15(1):5-13. (In Eng.) doi: https://doi.org/10.1109/62.821657
4. Willner D., Chang C.B., Dunn K.P. Kalman Filter Algorithms for a Multi-Sensor System. 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes. IEEE Press, Clearwater, FL, USA; 1976. p. 570-574. (In Eng.) doi: https://doi.org/10.1109/CDC.1976.267794
5. Gan Q., Harris Chris J. Comparison of Two Measurement Fusion Methods for Kalman-Filter-Based Multisensory Data Fusion. IEEE Transactions on Aerospace and Electronic Systems. 2001; 37(1):273-279. (In Eng.) doi: https://doi.org/10.1109/7.913685
6. Ponyatsky V.M. Quality improvement of the information processing derived from several video sensors in control problems. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2016; 12(4):165-172. Available at: https://www.elibrary.ru/item.asp?id=28151074 (accessed 23.08.2021). (In Russ., abstract in Eng.)
7. Ponyatsky V.M., Zenov B.V. The Use of Kalman Filter in Moving Object Control. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2018; 14(3):609-615. (In Russ., abstract in Eng.) doi: https://doi.org/10.25559/SITITO.14.201803.609-615
8. Bar-Shalom Y., Campo L. The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance. IEEE Transactions on Aerospace and Electronic Systems. 1986; 22(11):803-805. (In Eng.) doi: https://doi.org/10.1109/TAES.1986.310815
9. Yan L.P., Liu B.S., Zhou D.H. The Modeling and Estimation of Asynchronous Multirate Multisensory Dynamic Systems. Aerospace Science and Technology. 2006; 10:63-71. (In Eng.) doi: https://doi.org/10.1016/j.ast.2005.09.001
10. Bar-Shalom Y. Update with Out-of-Sequence Measurements in Tracking: Exact Solution. IEEE Transactions on Aerospace and Electronic Systems. 2002; 38(3):769-777. (In Eng.) doi: https://doi.org/10.1109/TAES.2002.1039398
11. Alexander H.L. State Estimation for Distributed Systems with Sensing Delay. Proc. SPIE 1470, Data Structures and Target Classification. SPIE; 1991. p. 103-111. (In Eng.) doi: https://doi.org/10.1117/12.44843
12. Larsen T.D., Andersen N.A., Ravn O., Poulson N.K. Incorporation of Time Delayed Measurements in a Discrete-Time Kalman Filter. Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171). 1998. p. 4:3972-3977. (In Eng.) doi: https://doi.org/10.1109/CDC.1998.761918
13. Sahebsara M., Chena T., Shah S.L. Optimal Fast-Rate Soft-sensor Design for Multi-rate Processes. Proceedings of the 2006 American Control Conference. IEEE Press, Minneapolis, MN, USA; 2006. p. 976-981. (In Eng.) doi: https://doi.org/10.1109/ACC.2006.1655485
14. Hara T., Tomizuka M. Multi-rate Controller for Hard Disk Drive with Redesign of State Estimator. Proceedings of the 1998 American Control Conference (IEEE Cat. No.98CH36207). IEEE Press, Philadelphia, PA, USA; 1998. p. 3033-3037. (In Eng.) doi: https://doi.org/10.1109/ACC.1998.688414
15. Mallick M., Coraluppi S., Carthel C. Advances in Asynchronous and Decentralized Estimation. 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542). IEEE Press, Big Sky, MT, USA; 2001. Vol. 4. p. 1873-1888. (In Eng.) doi: https://doi.org/10.1109/AERO.2001.931505
16. Nettleton E.W., Durrant-Whyte H.F. Delayed and Asequent Data in Decentralized Sensing Networks. Proc. SPIE 4571, Proceedings of SPIE ‒ The International Society for Optical Engineering. SPIE; 2001. p. 1-9. (In Eng.) doi: https://doi.org/10.1117/12.444148
17. Zhang K.S., Li X.R., Zhu Y.M. Optimal Update with Out-of-Sequence Updates for Distributed Filtering. IEEE Transactions on Signal Processing. 2005; 53(6):1992-2004. (In Eng.) doi: https://doi.org/10.1109/TSP.2005.847830
18. Mallick M., Krant S.J., Bar-Shalom Y. Multi-Sensor Multi-Target Tracking Using Out-of-Sequence Measurements. Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997). IEEE Press, Annapolis, MD, USA; 2002. Vol. 1. p. 135-142. (In Eng.) doi: https://doi.org/10.1109/ICIF.2002.1021142
19. Steffes S. Computationally Distributed Real-Time Dual Rate Kalman Filter. Journal of Guidance, Control and Dynamics. 2014; 37(4):1064-1068. (In Eng.) doi: https://doi.org/10.2514/1.G000179
20. Anitha R., Renuka S., AbudhahirA. Multi Sensor Data Fusion Algorithms for Target Tracking Using Multiple Measurements. 2013 IEEE International Conference on Computational Intelligence and Computing Research. IEEE Press, Enathi, India; 2013. p. 1-4. (In Eng.) doi: https://doi.org/10.1109/iccic.2013.6724283
21. Gao J.B., Harris C.J. Some remarks on Kalman Filters for the multisensory fusion. Information Fusion. 2002; 3(3):191-201. (In Eng.) doi: https://doi.org/10.1016/S1566-2535(02)00070-2
22. Guo Y., Zhao Y., Huang B. Development of soft sensor by incorporating the delayed infrequent and irregular measurements. Journal of Process Control. 2014; 24:1733-1739. (In Eng.) doi: https://doi.org/10.1016/j.jprocont.2014.09.006
23. Wang Y., Kosti´ D., Jansen S., Nijmeijer H. Filling the Gap between Low Frequency Measurements with Their Estimates. 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE Press, Hong Kong, China; 2014. p. 175-180. (In Eng.) doi: https://doi.org/10.1109/ICRA.2014.6906606
24. Sun S.L., Deng Z.L. Multi-Sensor Optimal Information Fusion Kalman Filter. Automatica. 2004; 40(6):1017-1023. (In Eng.) doi: https://doi.org/10.1016/j.automatica.2004.01.014
25. Feddaoui A., Boizot N., Busvelle E., Hugel V. High-gain Extended Kalman Filter for Continuous-Discrete Systems with Asynchronous Measurements. International Journal of Control. 2018; 93(8):2001-2014. (In Eng.) doi: https://doi.org/10.1080/00207179.2018.1539525
26. Luo R.C., Chang C.C., Lai C.C. Multisensor Fusion and Integration: Theories, Applications, and its Perspectives. IEEE Sensors Journal. 2011; 11(12):3122-3138. (In Eng.) doi: https://doi.org/10.1109/JSEN.2011.2166383
27. Castanedo F. A Review of Data Fusion Techniques. The Scientific World Journal. 2013; 2013:704504. (In Eng.) doi: https://doi.org/10.1155/2013/704504
28. Smith D., Singh S. Approaches to Multisensor Data Fusion in Target Tracking: A Survey. IEEE Transactions on Knowledge and Data Engineering. 2006; 18(12):1696-1710. (In Eng.) doi: https://doi.org/10.1109/TKDE.2006.183
29. Durrant-Whyte H., Henderson T.C. Multisensor Data Fusion. In: Siciliano B., Khatib O. (eds.) Springer Handbook of Robotics. Springer, Berlin, Heidelberg; 2008. p. 585-610. (In Eng.) doi: https://doi.org/10.1007/978-3-540-30301-5_26
Published
2021-12-20
How to Cite
PONYATSKY, Valeriy Mariafovich.
A Coordinate System Choice when Information Signal Processing by Kalman Filter.
Modern Information Technologies and IT-Education, [S.l.], v. 17, n. 4, p. 831-837, dec. 2021.
ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/799>. Date accessed: 31 oct. 2025.
doi: https://doi.org/10.25559/SITITO.17.202104.831-837.
Section
Cognitive information technologies in control systems
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