Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise

CUI Yongpeng; SUN Xiaojun

doi:10.23919/cje.2022.00.364

Volume 33 Issue 1

Jan. 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(1): 282-292

Yongpeng CUI and Xiaojun SUN, “Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 282–292, 2024 doi: 10.23919/cje.2022.00.364

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Yongpeng CUI and Xiaojun SUN, “Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 282–292, 2024 doi: 10.23919/cje.2022.00.364

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Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise

doi: 10.23919/cje.2022.00.364

CUI Yongpeng^1
,,
SUN Xiaojun^{1, 2
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1.
School of Electronic Engineering, Heilongjiang University, Harbin 150080, China
2.
Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China

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Author Bio:
Yongpeng CUI was born in 1997. He studied at Heilongjiang University School of Electrical Engineering, China, in 2021. Currently he is a graduate student in control science and engineering. His research interest focuses on state estimation and information fusion of under-observed systems. (Email: w624531@163.com)

Xiaojun SUN was born in 1980. She received the Ph.D. degree from Heilongjiang University. She is an Associate Professor in Heilongjiang University. Her research interests include multi-sensor information fusion, state estimation, and system identification. (Email: sxj@hlju.edu.cn)
Corresponding author: Email: sxj@hlju.edu.cn
Received Date: 2022-10-31
Accepted Date: 2023-02-16

Available Online: 2022-03-22

Publish Date: 2024-01-05

Abstract

Abstract

The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise. A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all. An extended incremental Kalman filter (EIKF) was then proposed for the nonlinear under-observed systems. Furthermore, an adaptive filtering method was given for optimization. The fusion adaptive incremental Kalman filter weighted by scalar was finally proposed. The comparison analysis was made to verify the optimization of the state estimation using adaptive filtering method in the filtering process.
- Information fusion,
- Multiplicative noise,
- Under-observed systems,
- Adaptive filtering,
- Incremental filtering

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References(33)

References

[1]	X. H. Sun, C. L. Wen, T. Wen, “Maximum correntropy high-order extended Kalman filter,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 190–198, 2022. doi: 10.1049/cje.2020.00.334
[2]	T. Wang, S. L. Huang, M. Y. Gao, et al., “Adaptive extended Kalman filter based dynamic equivalent method of PMSG wind farm cluster,” IEEE Transactions on Industry Applications, vol. 57, no. 3, pp. 2908–2917, 2021. doi: 10.1109/TIA.2021.3055749
[3]	X. H. Sun, C. L. Wen, T. Wen, “High-order extended Kalman filter design for a class of complex dynamic systems with polynomial nonlinearities,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 508–515, 2021. doi: 10.1049/cje.2021.04.004
[4]	J. X. Li, J. Hu, D. Y. Chen, et al., “Distributed extended Kalman filtering for state-saturated nonlinear systems subject to randomly occurring cyberattacks with uncertain probabilities,” Advances in Difference Equations, vol. 2020, no. 1, article no. 437, 2020. doi: 10.1186/s13662-020-02896-3
[5]	X. J. Sun, H. Zhou, H. B. Shen, et al., “Weighted fusion robust incremental Kalman filter,” Journal of Electronics & Information Technology, vol. 43, no. 12, pp. 3680–3686, 2021. (in Chinese) doi: 10.11999/JEIT200122
[6]	J. G. Chen, M. Zhang, W. Wang, et al., “Gaussian sum incremental Kalman filter under poor observation condition,” Application Research of Computers, vol. 32, no. 5, pp. 1365–1368, 2015. (in Chinese) doi: 10.3969/j.issn.1001-3695.2015.05.021
[7]	B. Cai, H. L. Gao, X. G. Song, et al., “Research of UWB indoor location based on improved incremental Kalman filter algorithm,” Machinery Design & Manufacture, no. 2, pp. 22–25, 2020. (in Chinese) doi: 10.3969/j.issn.1001-3997.2020.02.006
[8]	H. M. Fu, T. S. Lou, and Y. Z. Wu, “Extended incremental Kalman filter method under poor observation condition,” Journal of Aerospace Power, vol. 27, no. 4, pp. 777–781, 2012. (in Chinese) doi: 10.13224/j.cnki.jasp.2012.04.004
[9]	L. L. Ma, T. T. Zhao, and J. G. Chen, “Incremental cubature Kalman filtering under poor observation condition,” Computer Engineering, vol. 40, no. 10, pp. 228–231,238, 2014. (in Chinese) doi: 10.3969/j.issn.1000-3428.2014.10.043
[10]	B. Qi and S. L. Sun, “Distributed fusion filtering for multi-sensor networked uncertain systems with unknown communication disturbances and compensations of packet dropouts,” Acta Automatica Sinica, vol. 44, no. 6, pp. 1107–1114, 2018. (in Chinese) doi: 10.16383/j.aas.2017.c160652
[11]	N. Li, J. Ma, and S. L. Sun, “Optimal linear estimation for stochastic uncertain systems with multiple packet dropouts and delays,” Acta Automatica Sinica, vol. 41, no. 3, pp. 611–619, 2015. (in Chinese) doi: 10.16383/j.aas.2015.c140484
[12]	B. S. Chow and W. P. Birkemeier, “A new structure of recursive estimator,” IEEE Transactions on Automatic Control, vol. 34, no. 5, pp. 586–572, 1989. doi: 10.1109/9.24222
[13]	X. K. Yu, G. M. Jin, and J. X. Li, “Target tracking algorithm for system with Gaussian/non-Gaussian multiplicative noise,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 90–100, 2020. doi: 10.1109/TVT.2019.2952368
[14]	B. S. Chow and W. P. Birkemeier, “A new recursive filter for systems with multiplicative noise,” IEEE Transactions on Information Theory, vol. 36, no. 6, pp. 1430–1435, 1990. doi: 10.1109/18.59939
[15]	L. Zhang and X. D. Zhang, “An optimal filtering algorithm for systems with multiplicative/additive noises,” IEEE Signal Processing Letters, vol. 14, no. 7, pp. 469–472, 2007. doi: 10.1109/LSP.2006.891331
[16]	J. Ma, X. M. Yang, and S. L. Sun, “Distributed fusion estimation for multi-sensor systems with time-correlated multiplicative noises,” Acta Automatica Sinica, vol. 47, pp. 1–13, 2021. (in Chinese) doi: 10.16383/j.aas.c210147
[17]	D. P. Bertsekas, Dynamic Programming and Optimal Control: Volume 2, 4th ed., Athena Scientific, Cambridge, U.S.A., pp. 1-50, 2012.
[18]	Y. Yang, F. Xin, et al., “Event-Triggered output feedback control for a class of nonlinear systems via disturbance observer and adaptive dynamic programming,” IEEE Trans. Fuzzy Syst., In Press, pp. 1–13, 2023.
[19]	X. L. Wang, D. R. Ding, H. L. Dong, et al., “Neural-network-based control for discrete-time nonlinear systems with input saturation under stochastic communication protocol,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 766–778, 2021. doi: 10.1109/JAS.2021.1003922
[20]	H. M. Fu, Y. Z. Wu, and T. S. Lou, “Adaptive extended incremental kalman filter method,” Journal of Aerospace Power, vol. 27, no. 8, pp. 1734–1737, 2012. (in Chinese) doi: 10.13224/j.cnki.jasp.2012.08.002
[21]	H. M. Fu, Y. Z. Wu, and T. S. Lou, “Adaptive unscented incremental filter method,” Journal of Aerospace Power, vol. 28, no. 2, pp. 259–263, 2013. (in Chinese) doi: 10.13224/j.cnki.jasp.2013.02.008
[22]	X. J. Sun, H. Zhou, and G. M. Yan, “Adaptive incremental kalman filter based on innovation,” Journal of Electronics & Information Technology, vol. 42, no. 9, pp. 2223–2230, 2020. (in Chinese) doi: 10.11999/JEIT190493
[23]	Y. J. Xu, “A improved adaptive incremental filtering algorithm of transfer alignment,” Command Control & Simulation, vol. 40, no. 4, pp. 33–37, 2018. (in Chinese) doi: 10.3969/j.issn.1673-3819.2018.04.008
[24]	X. L. Wang, Y. Sun, and D. R. Ding, “Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques,” International Journal of Network Dynamics and Intelligence, pp. 85–98, in press, 2022. doi: 10.53941/ijndi0101008
[25]	J. L. Cong, Y. Y. Li, G. Q. Qi, et al., “A fast covariance intersection fusion algorithm and its application,” Acta Automatica Sinica, vol. 46, no. 7, pp. 1433–1444, 2020. (in Chinese) doi: 10.16383/j.aas.c170410
[26]	X. M. Wang, W. Q. Liu, and Z. L. Deng, “Modified robust covariance intersection fusion steady-state kalman predictor for uncertain systems,” Acta Automatica Sinica, vol. 42, no. 8, pp. 1198–1206, 2016. (in Chinese) doi: 10.16383/j.aas.2016.c150410
[27]	D. P. Wang, H. Zhang, and B. S. Ge, “Adaptive unscented kalman filter for target tacking with time-varying noise covariance based on multi-sensor information fusion,” Sensors, vol. 21, no. 17, article no. 5808, 2021. doi: 10.3390/s21175808
[28]	S. L. Sun, “Distributed optimal linear fusion estimators,” Information Fusion, vol. 63, pp. 56–73, 2020. doi: 10.1016/j.inffus.2020.05.006
[29]	S. L. Sun, “Distributed optimal linear fusion predictors and filters for systems with random parameter matrices and correlated noises,” IEEE Transactions on Signal Processing, vol. 68, pp. 1064–1074, 2020. doi: 10.1109/TSP.2020.2967180
[30]	H. Du, W. Wang, C. Xu, R. Xiao, and C. Sun, “Real-time onboard 3D state estimation of an unmanned aerial vehicle in multi-environments using multi-sensor data Fusion,” Sensors, vol. 20, no. 3, article no. 919, 2020. doi: 10.3390/s20030919
[31]	Z. L. Deng, W. Q. Liu, X. M. Wang, C. S. Yang, Robust Fusion Estimation Theory with Applications. Harbin Institute of Technology Press, Harbin, pp. 1-50, 2019. (in Chinese)
[32]	C. Y. Tao, “Track estimation and target detection based on multi-sensor information fusion,” Master Thesis, Xinjiang University, Urumqi, pp. 20-41, 2021. (in Chinese)
[33]	J. Li, “Research of track fusion algorithm of distributed multisensor system,” Master Thesis, Taiyuan University of Technology, Taiyuan, pp. 6-11, 2011. (in Chinese)