Informatics and Applications
2023, Volume 17, Issue 3, pp 8-17
NONLINEAR DYNAMIC SYSTEM STATE OPTIMAL FILTERING BY OBSERVATIONS WITH RANDOM DELAYS
Abstract
A mathematical model of a nonlinear dynamic observation system with a discrete time which allows taking into account the dependence of the time of receiving observations on the state of the observed object is proposed. The model implements the assumption that the time between the moment when the measurement of the state is formed and the moment when the measured state is received by the observer depends randomly on the position of the moving object. Such an assumption source is the process of observation by stationary means of an autonomous underwater apparatus in which the time of obtaining up-to-date data depends on the unknown distance between the object and the observer. Unlike deterministic delays formed by the known state of the observation environment, to account for the dependence of time delays on the unknown state of the object of observation, it is required to use random functions to describe them. The main result of the study of the proposed model is the solution ofthe optimal filtering problem. For this purpose, recurrent Bayesian relations describing the evolution of the a posteriori probability density are obtained. The difficulties ofusing a semifinished filter for practical purposes are discussed. The proposed model is illustrated by a practical example of the task of tracking a moving underwater object based on the results of measurements performed by typical acoustic sensors. It is assumed that the object moves under the water in a plane with a known average speed, constantly performs chaotic maneuvers, and is observed by two independent complexes of acoustic sensors measuring the distances to the object and the guiding cosines. The complexity of determining the position of such an object is illustrated by a simple filter using the geometric properties ofthe measured quantities and the least squares method.
[+] References (23)
- Bar-Shalom, Y., X. R. Li, and T. Kirubarajan. 2004. Estimation with applications to tracking and navigation: Theory, algorithms and software. New York, NY: JohnWiley&Sons. 548 p.
- Ehlers, F, ed. 2020. Autonomous underwater vehicles: Design and practice (radar, sonar & navigation). London, U.K.: SciTech Publishing. 592 p.
- Advances in marine vehicles, automation and robotics. J. Marine Science Engineering. Special Issue. Available at: www.mdpi.com/journal/jmse/special_issues/ advances_in_marine_vehicles_automation_and_robotics (accessed June 27, 2023).
- Groen, J., S. P. Beerens, R. Been, Y. Doisy, and E. Noutary. 2005. Adaptive port-starboard beamforming of triplet sonar arrays. IEEE J. Oceanic Eng. 30:348-359. doi: 10.1109/JOE.2005.850880.
- Luo, J., Y. Han, and L. Fan. 2018. Underwater acoustic target tracking: A review. Sensors 18(1):112. doi: 10.3390/ s18010112.
- Ghafoor, H., and Y Noh. 2019. An overview of next- generation underwater target detection and tracking: An integrated underwater architecture. IEEE Access 7:98841- 98853. doi: 10.1109/ACCESS.2019.2929932.
- Wolek, A., B.R. Dzikowicz, J. McMahon, and
B. H. Houston. 2019. At-Sea evaluation of an under-water vehicle behavior for passive target tracking. IEEEJ. Oceanic Eng. 44:514-523. doi: 10.1109/J0E.2018.2817268.
- Su, X., I. Ullah, X. Liu, and D. Choi. 2020. A review of underwater localization techniques, algorithms, and challenges. J. Sensors 2020:6403161. doi: 10.1155/ 2020/6403161.
- Borisov, A., A. Bosov, B. Miller, and G. Miller. 2020. Passive underwater target tracking: Conditionally minimax nonlinear filtering with bearing-Doppler observations. Sensors 20(8):2257. doi: 10.3390/s20082257.
- Kumar, M., and S. Mondal. 2021. Recent developments on target tracking problems: A review. Ocean Eng. 236:109558. 20 p. doi: 10.1016/j.oceaneng.2021.109558.
- Miller, A., B. Miller, and G. Miller. 2021. Navigation of underwater drones and integration of acoustic sensing with onboard inertial navigation system. Drones 5(3):83. doi: 10.3390/drones5030083.
- Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. - T. ASME 82(1):35-45. doi: 10.1115/1.3662552.
- Bernstein, I., and B. Friedland. 1966. Estimation of the state of a nonlinear process in the presence of nongaussian noise and disturbances. J. Frankl. Inst. 281(6):455-480. doi: 10.1016/0016-0032(66)90434-0.
- Julier, S.J., and J. K. Uhlmann. 1997. New extension of the Kalman filter to nonlinear systems. Proc. SPIE 3068:182-193. doi: 10.1117/12.280797.
- Julier, S. J., and J. K. Uhlmann. 2004. Unscented filtering and nonlinear estimation. P. IEEE 92(3):401-422. doi: 10.1109/JPR0C.2003.823141.
- Arasaratnam, I., and S. Haykin. 2009. Cubature Kalman filters. IEEE T. Automat. Contr. 54(6):1254-1269. doi: 10.1109/TAC.2009.2019800.
- Wang, T, L. Zhang, and S. Liu. 2022. Improved robust high-degree cubature Kalman filter based on novel cubature formula and maximum correntropy criterion with application to surface target tracking. J. Marine Science Engineering 10(8):1070. doi: 10.3390/jmse10081070.
- Christ, R. D., and R. L. Wernli. 2013. The ROVmanual: A user guide for remotely operated vehicles. 2nd ed. Oxford, U.K.: Butterworth-Heinemann. 712 p.
- Li, L., Y. Li, Y. Zhang, G. Xu, J. Zeng, and X. Feng. 2022. Formation control of multiple autonomous underwater vehicles under communication delay, packet discreteness and dropout. J. Marine Science Engineering 10(7):920. doi: 10.3390/jmse10070920.
- Zhao, L., J. Wang, T. Yu, K. Chen, and A. Su. 2019. Incorporating delayed measurements in an improved high- degree cubature Kalman filter for the nonlinear state estimation of chemical processes. ISA T. 86:122-133. doi: 10.1016/j.isatra.2018.11.004.
- Bertsekas, D. P., and S. E. Shreve. 1978. Stochastic optimal control: The discrete-time case. New York, NY: Academic Press. 330 p.
- Pugachev, V. S., and I. N. Sinitsyn. 1990. Stokhasticheskie differentsial'nye sistemy. Analiz i fil'tratsiya [Stochastic differential systems. Analysis and filtering]. Moscow: Nauka. 632 p.
- Sinitsyn, I.N., and E. R. Korepanov. 2015. Normal'nye uslovno-optimalnye fil'try Pugacheva dlya differentsial'nykh stokhasticheskikh sistem, lineynykh otnositel'no sostoyaniya [Normal Pugachev filters for state linear stochastic systems]. Informatika i ee Primeneniya - Inform Appl. 9(2):30-38. doi: 10.14357/19922264150204.
[+] About this article
Title
NONLINEAR DYNAMIC SYSTEM STATE OPTIMAL FILTERING BY OBSERVATIONS WITH RANDOM DELAYS
Journal
Informatics and Applications
2023, Volume 17, Issue 3, pp 8-17
Cover Date
2023-10-10
DOI
10.14357/19922264230302
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
stochastic dynamic observation system; state filtering; optimal Bayesian filter; mean square evaluation criterion; autonomous underwater vehicle; acoustic sensor; target tracking
Authors
A. V. Bosov
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
|