Sensor Fusion in Case of Aliquant Sample Repetition Frequiencies of Measurements and Output Estimates

Abstract

The problem of measurement fusion from two sensors with no coincident pairs of measurement at any time is considered. Besides, the required moments of returning the result also lie between indication times. Several association algorithms based on Kalman filtering both with use of the centralized filter, and on the basis of two parallel local filters as a part of decentralized filtration approach are proposed. Filter parameter adjustment to operation at non-equidistant times is described. The options for combining the assessments of the process state inside the filtration loop and outside it are considered. Comparison of methods is carried out in terms of the relative mean square error value of result estimate. The case of uniformly precise measurements and a case when the noise level into indications of sensors considerably differs are separately analyzed. The operability of methods is investigated at various values of bandwidth determined by a ratio of process noise intensity and the measurement noise one.

Author Biographies

Valeriy Mariafovich Ponyatsky, KBP Instrument Design Bureau

Head of Department, Ph.D. (Engineering)

Boris Vladislavovich Zenov, KBP Instrument Design Bureau

Lead Research Engineer

References

[1] 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; AES-22(6):803-805. (In Eng.) DOI: https://doi.org/10.1109/TAES.1986.310815
[2] Willner D., Chang C.B., Dunn K.P. Kalman filter algorithms for a multi-sensor system. In: 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes. Clearwater, FL, USA; 1976. p. 570-574. (In Eng.) DOI: https://doi.org/10.1109/CDC.1976.267794
[3] Roecker J.A., McGillem C.D. Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion. IEEE Transactions on Aerospace and Electronic Systems. 1988; 24(4):447-449. (In Eng.) DOI: https://doi.org/10.1109/7.7186
[4] Gan Q., Harris C.J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems. 2001; 37(1):273-279. (In Eng.) DOI: https://doi.org/10.1109/7.913685
[5] 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
[6] Chong C.-Y., Mori S., Barker W.H., Chang K.-C. Architectures and algorithms for track association and fusion. IEEE Aerospace and Electronic Systems Magazine. 2000; 15(1):5-13. (In Eng.) DOI: https://doi.org/10.1109/62.821657
[7] Dhuli R., Kandagadla M., Lall B. Multirate Kalman Filter for Sensor Data Fusion. In: Proceedings of the Fifteenth National Conference on Communications (NCC 2009). January 16-18, 2009, Guwahati, India; 2009. p. 229-233. (In Eng.)
[8] 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(1):63-71. (In Eng.) DOI: https://doi.org/10.1016/j.ast.2005.09.001
[9] 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
[10] Alexander H.L. State estimation for distributed systems with sensing delay. Proc. SPIE. Data Structures and Target Classification. 1991; 1470: 103-111. (In Eng.) DOI: https://doi.org/10.1117/12.44843
[11] Larsen T.D., Andersen N.A., Ravn O., Poulsen N.K. Incorporation of time delayed measurements in a discrete-time Kalman filter. In: Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171). Tampa, FL, USA. 1998; 4:3972-3977. (In Eng.) DOI: https://doi.org/10.1109/CDC.1998.761918
[12] Sahebsara M., Chen T., Shah S.L. Optimal fast-rate soft-sensor design for multi-rate processes. In: 2006 American Control Conference. Minneapolis, MN, USA; 2006. p. 976-981. (In Eng.) DOI: https://doi.org/10.1109/ACC.2006.1655485
[13] Hara T., Tomizuka M. Multi-rate controller for hard disk drive with redesign of state estimator. In: Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207). Philadelphia, PA, USA. 1998; 5:3033-3037. (In Eng.) DOI: https://doi.org/10.1109/ACC.1998.688414
[14] Mallick M., Coraluppi S., Carthel C. Advances in asynchronous and decentralized estimation. In: 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542). Big Sky, MT, USA. 2001; 4:1873-1888. (In Eng.) DOI: https://doi.org/10.1109/AERO.2001.931505
[15] Nettleton E.W., Durrant-Whyte H.F. Delayed and asequent data in decentralized sensing networks. In: Proc. SPIE - Sensor Fusion and Decentralized Control in Robotic Systems. 2001; 4571:1-9. (In Eng.) DOI: https://doi.org/10.1117/12.444148
[16] Zhang K., Li X.R., Zhu Y. Optimal update with out-of-sequence measurements. IEEE Transactions on Signal Processing. 2005; 53(6):1992-2004. (In Eng.) DOI: https://doi.org/10.1109/TSP.2005.847830
[17] Mallick M., Krant J., Bar-Shalom Y. Multi-sensor multi-target tracking using out-of-sequence measurements. In: Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997). Annapolis, MD, USA. 2002; 1:135-142. (In Eng.) DOI: https://doi.org/10.1109/ICIF.2002.1021142
[18] Steffes S. Computationally Distributed Real-Time Dual Rate Kalman Filter. Journal of Guidance, Control, and Dynamics. 2014; 37(4):1064-1086. (In Eng.) DOI: https://doi.org/10.2514/1.G000179
[19] Anitha R., Renuka S., Abudhahir A. Multi sensor data fusion algorithms for target tracking using multiple measurements. In: 2013 IEEE International Conference on Computational Intelligence and Computing Research. Enathi, India; 2013. p. 1-4. (In Eng.) DOI: https://doi.org/10.1109/ICCIC.2013.6724283
[20] 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
[21] Guo Y., Zhao Y., Huang B. Development of soft sensor by incorporating the delayed infrequent and irregular measurements. Journal of Process Control. 2014; 24(11):1733-1739. (In Eng.) DOI: https://doi.org/10.1016/j.jprocont.2014.09.006
[22] Wang Y., Kostić D., Jansen S.T.H., Nijmeijer H. Filling the gap between low frequency measurements with their estimates. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China; 2014. p. 175-180. (In Eng.) DOI: https://doi.org/10.1109/ICRA.2014.6906606
[23] 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
[24] Feddaoui A., Boizot N., Busvelle E., Hugel V. High-gain extended Kalman filter for continuous-discrete systems with asynchronous measurements. International Journal of Control. 2020; 93(8):2001-2014. (In Eng.) DOI: https://doi.org/10.1080/00207179.2018.1539525
[25] 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
[26] 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
[27] 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
[28] Durrant-Whyte H., Henderson T.C. Multisensor Data Fusion. In: Siciliano B., Khatib O. (ed.) 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
[29] Bar-Shalom Y., Fortmann T. Tracking and Data Association. Academic Press, New York; 1988. (In Eng.)
[30] Manyika J., Durrant-Whyte H.F. Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Ellis Horwood, New York; 1994. (In Eng.)
[31] Blackman S.S., Popoli R.F. Design and Analysis of Modern Tracking Systems. Artech House, Boston; 1999. (In Eng.)
Published
2020-11-30
How to Cite
PONYATSKY, Valeriy Mariafovich; ZENOV, Boris Vladislavovich. Sensor Fusion in Case of Aliquant Sample Repetition Frequiencies of Measurements and Output Estimates. Modern Information Technologies and IT-Education, [S.l.], v. 16, n. 3, p. 575-581, nov. 2020. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/680>. Date accessed: 12 sep. 2025. doi: https://doi.org/10.25559/SITITO.16.202003.575-581.
Section
Cognitive information technologies in control systems