THE USE OF KALMAN FILTER IN MOVING OBJECT CONTROL
Abstract
The problems arising in object control based on signals from several sensors in the course of difference in noise characteristics in their readings and missing of priori information on noise intensity of investigated process and applied sensors are considered. The basic approaches of their decision are presented, variants of filter structures providing an estimation of the body angular orientation using fusion of readings from gyroscopes and accelerometers taking into account the specifics of their data are proposed. Modeling of their work is performed at various noise intensities in sensor indications and a various choice of factors of filtering links to get the required bandwidth. On the basis of the comparative analysis of the received results recommendations for choice quantities of channels in structure for a filtration of coming measurements and their bandwidths are developed.
References
[2] Shpektorov A.G., Fam V.T. Analysis of micromechanical measuring systems application for marine ocean vehicles stabilization. Izvestiya SPbGETU “LETI”. 2017; 5:16-20. Available at: https://izv.eltech.ru/assets/files/izv-etu-5-2017-16-20.pdf (accessed 12.07.2018). (In Russian)
[3] Basics of building strapless inertial navigation systems [Osnovy postroyeniya besplatformennykh inertsial’nykh navigatsionnykh system]. V.Ya. Raspopov (Ed.). SPb.: GNTS RF OAO “Kontsern “TSNII “Elektropribor”, 2009. 280 p. (In Russian)
[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. DOI: 10.1109/7.913685
[5] Assa A., Janabi-Sharifi F. A Kalman Filter-Based Framework for Enhanced Sensor Fusion. IEEE Sensors Journal. 2015; l(6):3281–3292. DOI: 10.1109/JSEN.2014.2388153
[6] Wu Y., Cai M., Li J. Multi-sensor adaptive data fusion with colored measurement noise. Proceedings of the 29th Chinese Control And Decision Conference (CCDC), pp. 6102–6107, 2017. DOI: 10.1109/CCDC.2017.7978267
[7] Sun S.L., Deng Z.L. Multi-sensor optimal information fusion Kalman filter. Automatica. 2004; 40(6):1017–1023. DOI: 10.1016/j.automatica.2004.01.014
[8] Shivashankarappa N., Adiga S., Avinash R.A., Janardhan H.R. Kalman filter based multiple sensor data fusion in systems with time delayed state. 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 375–382, 2016. DOI: 10.1109/SPIN.2016.7566723
[9] Ka-Veng Yuen, Ka-In Hoi, Kai-Meng Mok. Selection of noise parameters for Kalman filter. Earthquake Engineering and Engineering Vibration. 2007; 6:49–56. DOI: 10.1007/s11803-007-0659-9
[10] Guan H., Li L., Jia X. Multi-sensor fusion vehicle positioning based on Kalman Filter. 2013 IEEE Third International Conference on Information Science and Technology (ICIST). Yangzhou, pp. 296–299, 2013. DOI: 10.1109/ICIST.2013.6747554
[11] Caron F., Duflos E., Pomorski D., Vanheeghe P. GPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspects. Information Fusion. 2006; 7(2):221-230. DOI: 10.1016/j.inffus.2004.07.002
[12] Chavez-Garcia R.O., Aycard O. Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking. IEEE Transactions on Intelligent Transportation Systems. 2016; 17(2):525–534. DOI: 10.1109/TITS.2015.2479925
[13] Yang C., Zheng J., Ren X., Yang W., Shi H., Shi L. Multi-Sensor Kalman Filtering With Intermittent Measurements. IEEE Transactions on Automatic Control. 2018; 63(3):797–804. DOI: 10.1109/TAC.2017.2734643
[14] Saha R.K., Chang K.C. An efficient algorithm for multisensor track fusion. IEEE Transactions on Aerospace and Electronic Systems. 1998; 34(1):200–210. DOI: 10.1109/7.640278
[15] Ponyatskiy V.M. Study of ways to implement an adaptive control system with Kalman filter. Stokhasticheskaya optimizatsiya v informatike. 2008; 4(1-1):196-210. Available at: https://elibrary.ru/item.asp?id=12994135 (accessed: 12.07.2018). (In Russian)
[16] Anitha R., Renuka S., Abudhahir A. Multi sensor data fusion algorithms for target tracking using multiple measurements. IEEE International Conference on Computational Intelligence and Computing Research, 1–4, 2013. DOI: 10.1109/ICCIC.2013.6724283
[17] Pornsarayouth S., Wongsaisuwan M. Sensor fusion of delay and non-delay signal using Kalman Filter with moving covariance. IEEE International Conference on Robotics and Biomimetics. 2009:2045–2049. DOI: 10.1109/ROBIO.2009.4913316
[18] Sage A.P., Melsa J.L. Estimation Theory with Applications to Communications and Control. McGraw-Hill Inc., US, 1971. 752 p.
[19] Durrant-Whyte H., Henderson T.C. Multisensor Data Fusion. B. Siciliano, O. Khatib (Eds.) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham, pp. 867-896, 2016. DOI: 10.1007/978-3-319-32552-1_35
[20] Kasper R., Schmidt S. Sensor-data-fusion for an autonomous vehicle using a Kalman-filter. 6th International Symposium on Intelligent Systems and Informatics. Subotica, pp. 1-5, 2008. DOI: 10.1109/SISY.2008.4664905
[21] 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. DOI: 10.1109/7.7186
[22] Fung M.L., Chen M.Z.Q., Chen Y.H. Sensor fusion: A review of methods and applications. 29th Chinese Control And Decision Conference (CCDC). Chongqing, pp. 3853–3860, 2017. DOI: 10.1109/CCDC.2017.7979175
[23] Zgurovsky M.Z., Podladchikov V.N. Analytical methods of Kalman filtering for systems with a priori uncertainty [Analiticheskiye metody kalmanovskoy fil’tratsii dlya sistem s apriornoy neopredelennost’yu]. Kiev: Naukova Dumka, 1995. 298.
[24] Bar-Shalom Y., Li X. R. Multitarget-Multisensor Tracking: Principles and Techniques. CT, Storrs: YBS Publishing, 1995. 615 p.

This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication policy of the journal is based on traditional ethical principles of the Russian scientific periodicals and is built in terms of ethical norms of editors and publishers work stated in Code of Conduct and Best Practice Guidelines for Journal Editors and Code of Conduct for Journal Publishers, developed by the Committee on Publication Ethics (COPE). In the course of publishing editorial board of the journal is led by international rules for copyright protection, statutory regulations of the Russian Federation as well as international standards of publishing.
Authors publishing articles in this journal agree to the following: They retain copyright and grant the journal right of first publication of the work, which is automatically licensed under the Creative Commons Attribution License (CC BY license). Users can use, reuse and build upon the material published in this journal provided that such uses are fully attributed.