Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

Jiaxing Li; Zidong Wang; Jun Hu; Hongli Dong; Hongjian Liu

IEEE/CAA Journal of Automatica Sinica

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J. Li, Z. Wang, J. Hu, H. Dong, and H. Liu, “Cubature Kalman fusion filtering under amplify-and-forward relays with randomly varying channel parameters,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–13, Jun. 2024.

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J. Li, Z. Wang, J. Hu, H. Dong, and H. Liu, “Cubature Kalman fusion filtering under amplify-and-forward relays with randomly varying channel parameters,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–13, Jun. 2024.

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

Funds: This work was supported in part by the National Natural Science Foundation of China (12171124, 61933007), the Natural Science Foundation of Heilongjiang Province of China (ZD2022F003), the National High-End Foreign Experts Recruitment Plan of China (G2023012004L), the Royal Society of UK, and the Alexander von Humboldt Foundation of Germany

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Author Bio:
Jiaxing Li received the B.Sc. degree in mathematics and applied mathematics from Harbin University of Commerce in 2017 and the M.Sc. degree in mathematics from Harbin University of Science and Technology in 2020, respectively. She is currently a Ph.D. candidate in mathematics at Harbin University of Science and Technology. From March 2023 to March 2024, she was a Visiting Ph.D. Student with the Department of Computer Science, Brunel University London, UK. Her current research interests include optimal distributed filtering for time-varying state-saturated systems, optimal distributed filtering for sensor networks, and cubature Kalman filtering. She is an active reviewer for many international journals

Zidong Wang (Fellow, IEEE) received the B.Sc. degree in mathematics in 1986 from Suzhou University, the M.Sc. degree in applied mathematics and the Ph.D. degree in electrical engineering both from Nanjing University of Science and Technology, in 1990 and 1994, respectively. He is currently a professor of dynamical systems and computing in the Department of Computer Science, Brunel University London, UK. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany, and the UK. His research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published a number of papers in international journals. Prof. Wang is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, and the William Mong Visiting Research Fellowship of Hong Kong. He serves (or has served) as the Editor-in-Chief for International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for Systems Science & Control Engineering, and an Associate Editor for 12 international journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, and IEEE Transactions on Systems, Man, and Cybernetics–part C. He is a Member of the Academia Europaea, a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of the IEEE, a Fellow of the Royal Statistical Society, and a member of program committee for many international conferences

Jun Hu (Member, IEEE) received the B.Sc. degree in information and computing science and the M.Sc. degree in applied mathematics from Harbin University of Science and Technology, in 2006 and 2009, respectively, and the Ph.D. degree in control science and engineering from Harbin Institute of Technology in 2013. From September 2010 to September 2012, he was a Visiting Ph.D. Student in the Department of Information Systems and Computing, Brunel University London, Uxbridge, UK. From May 2014 to April 2016, he was an Alexander von Humboldt research fellow at the University of Kaiserslautern, Germany. From January 2018 to January 2021, he was a Research Fellow at University of South Wales, Pontypridd, UK. He is with the Department of Applied Mathematics, Harbin University of Science and Technology, and is also with the School of Automation, Harbin University of Science and Technology. His research interests include nonlinear control, filtering and fault estimation, time-varying systems and complex networks. Dr. Hu has published more than 90 papers in refereed international journals. He serves as a reviewer for Mathematical Reviews, as an Editor for Neurocomputing, Journal of Intelligent & Fuzzy Systems, Neural Processing Letters, Systems Science & Control Engineering, and as a Guest Editor for International Journal of General Systems and Information Fusion

Hongli Dong (Senior Member, IEEE) received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology in 2012. From 2009 to 2010, she was a Research Assistant with the Department of Applied Mathematics, City University of Hong Kong, China. From 2010 to 2011, she was a Research Assistant with the Department of Mechanical Engineering, The University of Hong Kong, China. From 2011 to 2012, she was a Visiting Scholar with the Department of Information Systems and Computing, Brunel University London, UK. From 2012 to 2014, she was an Alexander von Humboldt Research Fellow with the University of Duisburg−Essen, Germany. She is currently a Professor with the Artificial Intelligence Energy Research Institute, Northeast Petroleum University. She is also the Director of the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control. Her current research interests include robust control and networked control systems. She is a very active reviewer for many international journals

Hongjian Liu received the B.Sc. degree in applied mathematics in 2003 from Anhui University, and the M.Sc. degree in detection technology and automation equipment in 2009 from Anhui Polytechnic University, and the Ph.D. degree in control science and engineering in 2018 from Donghua University. In 2016, he was a Research Assistant with the Department of Mathematics, Texas A&M University at Qatar, Qatar, for two months. From March 2017 to March 2018, he was a Visiting Scholar in the Department of Information Systems and Computing, Brunel University London, UK. He is currently a Professor in the School of Mathematics and Physics, Anhui Polytechnic University. His current research interests include filtering theory, memristive neural networks, and network communication systems. He is a very active reviewer for many international journals
Corresponding author: Jun Hu, e-mail: jhu@hrbust.edu.cn
Received Date: 2024-02-06
Revised Date: 2024-04-12
Accepted Date: 2024-05-30

Available Online: 2024-11-07

Abstract

Abstract

In this paper, the problem of cubature Kalman fusion filtering (CKFF) is addressed for multi-sensor systems under amplify-and-forward (AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance’s upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
- Amplify-and-forward (AaF) relays,
- covariance intersection fusion,
- cubature Kalman filtering,
- multi-sensor systems,
- uniform boundedness

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References

[1]	R. Caballero-Águila, A. Hermoso-Carazo, and J. Linares-Pérez, “Networked fusion estimation with multiple uncertainties and time-correlated channel noise,” Information Fusion, vol. 54, pp. 161–171, 2020. doi: 10.1016/j.inffus.2019.07.008
[2]	H. Geng, Z. Wang, Y. Chen, F. E. Alsaadi, and Y. Cheng, “Multi-sensor filtering fusion with parametric uncertainties and measurement censoring: Monotonicity and boundedness,” IEEE Trans. Signal Processing, vol. 69, pp. 5875–5890, 2021. doi: 10.1109/TSP.2021.3118538
[3]	W. Li and F. Yang, “Information fusion over network dynamics with unknown correlations: An overview,” Int. J. Network Dynamics and Intelligence, vol. 2, no. 2, p. 100003, 2023.
[4]	T. Tian, S. Sun, and N. Li, “Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises,” Information Fusion, vol. 27, pp. 126–137, 2016. doi: 10.1016/j.inffus.2015.06.001
[5]	X. Wang, A. K. Gostar, T. Rathnayake, B. Xu, A. Bab-Hadiashar, and R. Hoseinnezhad, “Centralized multiple-view sensor fusion using labeled multi-Bernoulli filters,” Signal Processing, vol. 150, pp. 75–84, 2018. doi: 10.1016/j.sigpro.2018.04.010
[6]	Y. Jin, X. Ma, X. Meng, and Y. Chen, “Distributed fusion filtering for cyber-physical systems under round-robin protocol: A mixed H₂/H_∞ framework,” Int. J. Systems Science, vol. 54, no. 8, pp. 1661–1675, 2023. doi: 10.1080/00207721.2023.2208130
[7]	M. Xie, D. Ding, H. Dong, Q.-L. Han, and G. Wei, “Reliable fusion estimation over sensor networks with outliers and energy constraints,” Int. J. Robust and Nonlinear Control, vol. 29, no. 17, pp. 5913–5929, 2019. doi: 10.1002/rnc.4706
[8]	R. Caballero-Águila and J. Linares-Pérez, “Distributed fusion filtering for uncertain systems with coupled noises, random delays and packet loss prediction compensation,” Int. J. Systems Science, vol. 54, no. 2, pp. 371–390, 2023. doi: 10.1080/00207721.2022.2122905
[9]	B. Chen, G. Hu, D. W. C. Ho, W.-A. Zhang, and L. Yu, “Distributed robust fusion estimation with application to state monitoring systems,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 47, no. 11, pp. 2994–3005, 2017. doi: 10.1109/TSMC.2016.2558103
[10]	Z. Xing and Y. Xia, “Distributed federated Kalman filter fusion over multi-sensor unreliable networked systems,” IEEE Trans. Circuits and Systems I: Regular Papers, vol. 63, no. 10, pp. 1714–1725, 2016. doi: 10.1109/TCSI.2016.2587728
[11]	S. Chen and D. W. C. Ho, “Information-based distributed extended Kalman filter with dynamic quantization via communication channels,” Neurocomputing, vol. 469, pp. 251–260, 2022. doi: 10.1016/j.neucom.2021.10.066
[12]	H. Gao, Y. Li, L. Yu, and H. Yu, “Collaborative-prediction-based recursive filtering for nonlinear systems with sensor saturation under duty cycle scheduling,” Systems Science & Control Engineering, vol. 11, no. 1, p. 2247007, 2023.
[13]	W. Li, Y. Jia, and J. Du, “Variance-constrained state estimation for nonlinearly coupled complex networks,” IEEE Trans. Cybern., vol. 48, no. 2, pp. 818–824, 2018. doi: 10.1109/TCYB.2017.2653242
[14]	S. Feng, X. Li, S. Zhang, Z. Jian, H. Duan, and Z. Wang, “A review: State estimation based on hybrid models of Kalman filter and neural network,” Systems Science & Control Engineering, vol. 11, no. 1, p. 2173682, 2023.
[15]	C. Li, Z. Wang, W. Song, S. Zhao, J. Wang, and J. Shan, “Resilient unscented Kalman filtering fusion with dynamic event-triggered scheme: Applications to multiple unmanned aerial vehicles,” IEEE Trans. Control Systems Technology, vol. 31, no. 1, pp. 370–381, 2023. doi: 10.1109/TCST.2022.3180942
[16]	K. Xiong, H. Zhang, and C. Chan, “Performance evaluation of UKF-based nonlinear filtering,” Automatica, vol. 42, no. 2, pp. 261–270, 2006. doi: 10.1016/j.automatica.2005.10.004
[17]	S. Li, Y. Hu, L. Zheng, Z. Li, X. Chen, T. Fernando, H. H. C. Iu, Q. Wang, and X. Liu, “Stochastic event-triggered cubature Kalman filter for power system dynamic state estimation,” IEEE Trans. Circuits and Systems II: Express Briefs, vol. 66, no. 9, pp. 1552–1556, 2019.
[18]	Y.-W. Lv and G.-H. Yang, “An adaptive cubature Kalman filter for nonlinear systems against randomly occurring injection attacks,” Applied Mathematics and Computation, vol. 418, p. 126834, 2022. doi: 10.1016/j.amc.2021.126834
[19]	Y.-W. Lv and G.-H. Yang, “Centralized and distributed adaptive cubature information filters for multi-sensor systems with unknown probability of measurement loss,” Information Sciences, vol. 630, pp. 173–189, 2023. doi: 10.1016/j.ins.2023.02.035
[20]	Y. Ju, D. Liu, D. Ding, and G. Wei, “Distributed cubature Kalman filtering for nonlinear systems with stochastic communication protocol,” Asian J. Control, vol. 24, no. 6, pp. 3566–3579, 2022. doi: 10.1002/asjc.2755
[21]	M. Kooshkbaghi and H. J. Marquez, “Event-triggered discrete-time cubature Kalman filter for nonlinear dynamical systems with packet dropout,” IEEE Trans. Autom. Control, vol. 65, no. 5, pp. 2278–2285, 2020. doi: 10.1109/TAC.2019.2945286
[22]	D. Ciuonzo, A. Aubry, and V. Carotenuto, “Rician MIMO channel-aware and jamming-aware decision fusion,” IEEE Trans. Signal Processing, vol. 65, no. 15, pp. 3866–3880, 2017. doi: 10.1109/TSP.2017.2686375
[23]	X. Ge, Q.-L. Han, Q. Wu, and X.-M. Zhang, “Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1234–1251, 2023. doi: 10.1109/JAS.2022.105845
[24]	X. Ge, Q.-L. Han, X. Zhang, L. Ding, and F. Yang, “Distributed event-triggered estimation over sensor networks: A survey,” IEEE Trans. Cybern., vol. 50, no. 3, pp. 1306–1320, 2020. doi: 10.1109/TCYB.2019.2917179
[25]	F. Han, J. Liu, J. Li, J. Song, M. Wang, and Y. Zhang, “Consensus control for multi-rate multi-agent systems with fading measurements: The dynamic event-triggered case,” Systems Science & Control Engineering, vol. 11, no. 1, p. 2158959, 2023.
[26]	Y. Wang, H.-J. Liu, and H.-L. Tan, “An overview of filtering for sampled-data systems under communication constraints,” Int. J. Network Dynamics and Intelligence, vol. 2, no. 3, p. 100011, 2023.
[27]	Y. Xu, R. Lu, H. Peng, K. Peng, and A. Xue, “Asynchronous dissipative state estimation for stochastic complex networks with quantized jumping coupling and uncertain measurements,” IEEE Trans. Neural Networks and Learning Systems, vol. 28, no. 2, pp. 268–277, 2015.
[28]	W. Chen, D. Ding, H. Dong, and G. Wei, “Distributed resilient filtering for power systems subject to denial-of-service attacks,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 49, no. 8, pp. 1688–1697, 2019. doi: 10.1109/TSMC.2019.2905253
[29]	D. Ding, Q.-L. Han, X. Ge, and J. Wang, “Secure state estimation and control of cyber-physical systems: A survey,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 51, no. 1, pp. 176–190, 2021. doi: 10.1109/TSMC.2020.3041121
[30]	Y. Shen and S. Sun, “Distributed recursive filtering for multi-rate uniform sampling systems with packet losses in sensor networks,” Int. J. Systems Science, vol. 54, no. 8, pp. 1729–1745, 2023. doi: 10.1080/00207721.2023.2209887
[31]	Y.-A. Wang, B. Shen, L. Zou, and Q.-L. Han, “A survey on recent advances in distributed filtering over sensor networks subject to communication constraints,” Int. J. Network Dynamics and Intelligence, vol. 2, no. 2, p. 100007, 2023.
[32]	E. Antonio-Rodriguez, S. Werner, R. López-Valcarce, and R. Wichman, “MMSE filter design for full-duplex filter-and-forward MIMO relays under limited dynamic range,” Signal Processing, vol. 156, pp. 208–219, 2019. doi: 10.1016/j.sigpro.2018.11.001
[33]	S. Kiani, S. ShahbazPanahi, M. Dong, and G. Boudreau, “Power-optimal distributed beamforming for multi-carrier asynchronous bidirectional relay networks,” IEEE Trans. Signal and Infor. Processing Over Networks, vol. 7, pp. 114–129, 2021. doi: 10.1109/TSIPN.2020.3044962
[34]	M. A. Sadr and B. Champagne, “Joint channel estimation and robust beamforming design for AF relaying using IMM Kalman filters,” IEEE Trans. Vehicular Technology, vol. 72, no. 2, pp. 1775–1790, 2023. doi: 10.1109/TVT.2022.3207410
[35]	T. Zhang, G. Liu, H. Zhang, W. Kang, G. K. Karagiannidis, and A. Nallanathan, “Energy-efficient resource allocation and trajectory design for UAV relaying systems,” IEEE Trans. Communi., vol. 68, no. 10, pp. 6483–6498, 2020. doi: 10.1109/TCOMM.2020.3009153
[36]	S. Huang, H. Chen, J. Cai, and F. Zhao, “Energy efficiency and spectral-efficiency tradeoff in amplify-and-forward relay networks,” IEEE Trans. Vehicular Technology, vol. 62, no. 9, pp. 4366–4378, 2013. doi: 10.1109/TVT.2013.2269695
[37]	H. Tan, B. Shen, Q. Li, and W. Qian, “Recursive filtering for nonlinear systems with self-interferences over full-duplex relay networks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 2037–2040, 2022. doi: 10.1109/JAS.2022.105965
[38]	O. Shmuel, A. Cohen, and O. Gurewitz, “Compute-and-forward in large relaying systems: Limitations and asymptotically optimal scheduling,” IEEE Trans. Information Theory, vol. 67, no. 9, pp. 6243–6265, 2021. doi: 10.1109/TIT.2021.3093454
[39]	S. M. J. A. Tabatabaee, M. Rajabzadeh, and M. Towliat, “A novel low-complexity GFDM relay communication system: Relay selection and filter-and-forward,” IEEE Trans. Signal Processing, vol. 69, pp. 5147–5158, 2021. doi: 10.1109/TSP.2021.3108679
[40]	A. Jee, K. Agrawal, and S. Prakriya, “A coordinated direct AF/DF relay-aided NOMA framework for low outage,” IEEE Trans. Communi., vol. 70, no. 3, pp. 1559–1579, 2022. doi: 10.1109/TCOMM.2021.3126632
[41]	R. Senanayake, S. Atapattu, J. S. Evans, and P. J. Smith, “Decentralized relay selection in multi-user multihop decode-and-forward relay networks,” IEEE Trans. Wireless Communications, vol. 17, no. 5, pp. 3313–3326, 2018. doi: 10.1109/TWC.2018.2809748
[42]	F.-S. Tseng, C.-T. Lin, J.-Y. Chen, and K.-Y. Chen, “Finite-order source and relay filtering design for wideband full-duplex amplify-and-forward relaying networks,” IEEE Trans. Communi., vol. 68, no. 12, pp. 7395–7409, 2020. doi: 10.1109/TCOMM.2020.3023929
[43]	Y. Ding, L. Gong, M. Zhang, C. Li, and Z. Qin, “A multi-path adaptive fusion network for multimodal brain tumor segmentation,” Neurocomputing, vol. 412, pp. 19–30, 2020. doi: 10.1016/j.neucom.2020.06.078
[44]	A. S. Leong and D. E. Quevedo, “Kalman filtering with relays over wireless fading channels,” IEEE Trans. Autom. Control, vol. 61, no. 6, pp. 1643–1648, 2016. doi: 10.1109/TAC.2015.2478129
[45]	Y. Liu, Z. Wang, C. Liu, M. Coombes, and W.-H. Chen, “Auxiliary particle filtering over sensor networks under protocols of amplify-andforward and decode-and-forward relays,” IEEE Trans. Signal and Inform. Processing over Networks, vol. 8, pp. 883–893, 2022. doi: 10.1109/TSIPN.2022.3212318
[46]	Y. Chen, X. Meng, Z. Wang, and H. Dong, “Event-triggered recursive state estimation for stochastic complex dynamical networks under hybrid attacks,” IEEE Trans. Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1465–1477, 2023. doi: 10.1109/TNNLS.2021.3105409
[47]	K. Xiong, C. Wei, and L. Liu, “Robust extended Kalman filtering for nonlinear systems with stochastic uncertainties,” IEEE Trans. Systems, Man, and Cybern.——Part A: Systems and Humans, vol. 40, no. 2, pp. 399–405, 2010. doi: 10.1109/TSMCA.2009.2034836
[48]	K. Reif, F. Sonnemann, and R. Unbehauen, “An EKF-based nonlinear observer with a prescribed degree of stability,” Automatica, vol. 34, no. 9, pp. 1119–1123, 1998. doi: 10.1016/S0005-1098(98)00053-3

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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