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Fusion estimation for stochastic uncertain systems with time-correlated rician fading channels. (English) Zbl 1485.93574

Summary: In this article, a fusion estimation scheme is proposed for stochastic uncertain systems with time-correlated fading channels (TFCs). A batch of random variables obeying Gaussian distributions is employed to describe the parameter uncertainties. The sensor communicates with the local filter through a TFC where the evolution of the channel coefficient is characterized by a certain dynamic process with one-step correlated noises. For further analyzing the effects of TFCs, a class of additional variables is first introduced by augmenting the dynamics of channel coefficients and the concerned system. Then, a new group of modified local filters is developed and the unbiasedness of local filters is examined by means of inductive method. Furthermore, the filter gains which minimize the local filtering error covariances are designed for the modified local filters in the simultaneous presence of stochastic uncertainties and TFCs. Subsequently, the cross-covariances among local estimates are computed iteratively and, based on the obtained cross-covariances as well as the unbiased local estimates and their corresponding filtering error covariances, a fusion estimate is obtained by using weighted least square fusion method. Finally, the effectiveness of the proposed fusion estimation scheme is verified by two examples.

MSC:

93E10 Estimation and detection in stochastic control theory
93C41 Control/observation systems with incomplete information
Full Text: DOI

References:

[1] Han, C.; Zhang, H.; Fu, M., Optimal filtering for networked systems with Markovian communication delays, Automatica, 49, 10, 3097-3104 (2013) · Zbl 1358.93174
[2] Kalman, R. E., A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 1, 35-45 (1960)
[3] Quevedo, D. E.; Ahlen, A.; Leong, A. S.; Dey, S., On Kalman filtering over fading wireless channels with controlled transmission powers, Automatica, 48, 7, 1306-1316 (2012) · Zbl 1246.93115
[4] Kwon, W. H.; Pim, P. S.; Han, S., A receding horizon unbiased FIR filter for discrete-time state space models, Automatica, 38, 3, 545-551 (2002) · Zbl 0997.93063
[5] Vazquez-Olguin, M.; Shmaliy, Y. S.; Ibarra-Manzano, O. G., Distributed unbiased FIR filtering with average consensus on measurements for WSNs, IEEE Trans. Ind. Inf., 13, 3, 1440-1447 (2017)
[6] Zhao, R.; Li, X.; Li, Z., Weighted least squares design of 2-D FIR filters using a matrix-based generalized conjugate gradient method, J. Frankl. Inst., 353, 8, 1759-1780 (2016) · Zbl 1347.93260
[7] Zhao, S.; Shmaliy, Y. S.; Huang, B.; Liu, F., Minimum variance unbiased FIR filter for discrete time-variant systems, Automatica (2015) · Zbl 1371.93203
[8] Frezzatto, L.; Oliveira, C. L.F.; Peres, P. L.D., \(H_\infty\) non-minimal filter design in finite frequency ranges for discrete-time Takagi-Sugeno fuzzy systems with time-varying delays, J. Frankl. Inst., 357, 1, 622-634 (2000) · Zbl 1429.93381
[9] Li, X.; Lam, J.; Gao, H.; Xiong, J., \(H_\infty\) and \(H_2\) filtering for linear systems with uncertain Markov transitions, Automatica, 67, 252-266 (2016) · Zbl 1335.93126
[10] Li, P.; Lam, J.; Shu, Z., \(H_\infty\) positive filtering for positive linear discrete-time systems: an augmentation approach, IEEE Trans. Autom. Control, 55, 10, 2337-2342 (2010) · Zbl 1368.93719
[11] Suplin, V.; Fridman, E.; Shaked, U., Sampled-data \(H_\infty\) control and filtering: nonuniform uncertain sampling, Automatica, 43, 6, 1072-1083 (2007) · Zbl 1282.93171
[12] Ge, X.; Han, Q.-L.; Wang, Z., A threshold-parameter-dependent approach to designing distributed event-triggered \(H_\infty\) consensus filters over sensor networks, IEEE Trans. Cybern., 49, 4, 1148-1159 (2019)
[13] Li, Q.; Wang, Z.; Li, N.; Sheng, W., A dynamic event-triggered approach to recursive filtering for complex networks with switching topologies subject to random sensor failures, IEEE Trans. Neural Netw. Learn. Syst., 31, 10, 4381-4388 (2020)
[14] Qu, B.; Li, N.; Liu, Y.; Alsaadi, F. E., Estimation for power quality disturbances with multiplicative noises and correlated noises: a recursive estimation approach, Int. J. Syst. Sci., 51, 7, 1200-1217 (2020) · Zbl 1483.93630
[15] Tan, H.; Shen, B.; Peng, K.; Liu, H., Robust recursive filtering for uncertain stochastic systems with amplify-and-forward relays, Int. J. Syst. Sci., 51, 7, 1188-1199 (2020) · Zbl 1483.93664
[16] Hu, J.; Wang, Z.; Alsaadi, F. E.; Hayat, T., Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities, Inf. Fusion, 38, 774-783 (2017)
[17] art. no. 108908 · Zbl 1436.93084
[18] Li, X.; Wei, G.; Wang, L., Distributed set-membership filtering for discrete-time systems subject to denial-of-service attacks and fading measurements: a zonotopic approach, Inf. Sci., 547, 49-67 (2021) · Zbl 1478.93691
[19] Ge, X.; Han, Q.-L.; Wang, Z., A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks, IEEE Trans. Cybern., 49, 1, 171-183 (2019)
[20] Ding, D.; Wang, Z.; Han, Q. L., A set-membership approach to event-triggered filtering for general nonlinear systems over sensor networks, IEEE Trans. Autom. Control, 65, 4, 1792-1799 (2020) · Zbl 1533.93453
[21] Witsenhausen, H. S., Sets of possible states of linear systems given perturbed observations, IEEE Trans. Autom. Control, 13, 5, 556-558 (1968)
[22] Yang, F.; Li, Y., Set-membership filtering for systems with sensor saturation, Automatica, 45, 8, 1896-1902 (2009) · Zbl 1185.93049
[23] Alessandri, A.; Baglietto, M.; Battistelli, G., Receding-horizon estimation for switching discrete-time linear systems, IEEE Trans. Autom. Control, 50, 11, 1736-1748 (2005) · Zbl 1365.93479
[24] Alessandri, A.; Baglietto, M.; Battistelli, G., Receding-horizon estimation for discrete-time linear systems, IEEE Trans. Autom. Control, 48, 3, 473-478 (2003) · Zbl 1364.93758
[25] Zou, L.; Wang, Z.; Hu, J.; Han, Q., Moving horizon estimation meets multi-sensor information fusion: development, opportunities and challenges, Inf. Fusion, 60, 1-10 (2020)
[26] Andersson, L. E.; Imsland, L.; Brekke, E. F.; Scibilia, F., On Kalman filtering with linear state equality constraints, Automatica, 101, 467-470 (2019) · Zbl 1415.93257
[27] Basin, M.; Perez, J.; Skliar, M., Optimal filtering for polynomial system states with polynomial multiplicative noise, Int. J. Robust Nonlinear Control, 16, 6, 303-314 (2006) · Zbl 1105.93056
[28] Chen, B.; Hu, G.; Ho, D. W.C.; Yu, L., Distributed Kalman filtering for time-varying discrete sequential systems, Automatica, 99, 228-236 (2019) · Zbl 1406.93341
[29] Dey, S.; Leong, A. S.; Evans, J. S., Kalman filtering with faded measurements, Automatica, 45, 10, 2223-2233 (2009) · Zbl 1179.93159
[30] Dragan, V., Optimal filtering for discrete-time linear systems with multiplicative white noise perturbations and periodic coefficients, IEEE Trans. Autom. Control, 58, 4, 1029-1034 (2013) · Zbl 1369.93617
[31] Sinopoli, B.; Schenato, L.; Franceschetti, M.; Poolla, K.; Jordan, M. I.; Sastry, S. S., Kalman filtering with intermittent observations, IEEE Trans. Autom. Control, 49, 9, 1453-1464 (2004) · Zbl 1365.93512
[32] Wu, J.; Shi, G.; Anderson, B. D.O.; Johansson, K. H., Kalman filtering over fading channels: zero-one laws and almost sure stabilities, IEEE Trans. Inf. Theory, 64, 10, 6731-6742 (2018) · Zbl 1401.94054
[33] Zhang, H.; Lu, X.; Zhang, W.; Wang, W., Kalman filtering for linear time-delayed continuous-time systems with stochastic multiplicative noises, Int. J. Control Autom. Syst., 5, 4, 355-363 (2007)
[34] Liang, J.; Wang, F.; Wang, Z.; Liu, X., Robust Kalman filtering for two-dimensional systems with multiplicative noises and measurement degradations: the finite-horizon case, Automatica, 96, 166-177 (2018) · Zbl 1406.93347
[35] Tan, H.; Shen, B.; Liu, Y.; Alsaedi, A.; Ahmad, B., Event-triggered multi-rate fusion estimation for uncertain system with stochastic nonlinearities and colored measurement noises, Inf. Fusion, 36, 313-320 (2017)
[36] Deng, Z.; Gao, Y.; Mao, L.; Li, Y.; Hao, G., New approach to information fusion steady-state Kalman filtering, Automatica, 41, 10, 1695-1707 (2005) · Zbl 1087.93056
[37] Sun, S.; Deng, Z., Multi-sensor optimal information fusion Kalman filter, Automatica, 40, 6, 1017-1023 (2004) · Zbl 1075.93037
[38] Hou, N.; Dong, H.; Wang, Z.; Ren, W.; Alsaadi, F. E., \(H_\infty\) state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through fading channels, Neural Netw., 89, 61-73 (2017) · Zbl 1441.93300
[39] Geng, H.; Wang, Z.; Liang, Y.; Chen, Y.; Alsaadi, F. E., Tobit Kalman filter with fading measurements, Signal Process., 140, 60-68 (2017)
[40] Shi, Z.; Ding, H.; Ma, S.; Tam, K. W., Analysis of HARQ-IR over time-correlated Rayleigh fading channels, IEEE Trans. Wirel. Commun., 14, 12, 7096-7109 (2015)
[41] Liu, W.; Shi, P., Optimal linear filtering for networked control systems with time-correlated fading channels, Automatica, 101, 345-353 (2019) · Zbl 1415.93263
[42] Beaulieu, N. C.; Hemachandra, K. F., Novel simple representations for gaussian class multivariate distributions with generalized correlation, IEEE Trans. Inf. Theory, 57, 12, 8072-8083 (2011) · Zbl 1365.60005
[43] Lin, H.; Sun, S., Optimal sequential fusion estimation with stochastic parameter perturbations, fading measurements, and correlated coises, IEEE Trans. Signal Process., 66, 13, 3571-3583 (2018) · Zbl 1415.94165
[44] Sun, S.; Peng, F.; Lin, H., Distributed asynchronous fusion estimator for stochastic uncertain systems with multiple sensors of different fading measurement rates, IEEE Trans. Signal Process., 66, 3, 641-653 (2018) · Zbl 1414.94593
[45] Liu, Q.; Wang, Z.; He, X.; Zhou, D., Event-based distributed filtering with stochastic measurement fading, IEEE Trans. Ind. Inf., 11, 6, 1643-1652 (2015)
[46] Caballero-Aguila, R.; Hermoso-Carazo, A.; Linares-Perez, J., Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal, Int. J. Gen. Syst., 48, 7, 713-737 (2019)
[47] Zhu, C.; Xia, Y.; Yan, L.; Fu, M., Centralised fusion over unreliable networks, Int. J. Control, 85, 4, 409-418 (2012) · Zbl 1256.93109
[48] H. Tan, B. Shen, H. Shu, Robust recursive filtering for stochastic systems with time-correlated fading channels, IEEE Trans. Syst., Man Cybern., to be published (2021). 10.1109/TSMC.2021.3062848
[49] Feng, J.; Wang, Z.; Zeng, M., Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises, Inf. Fusion, 14, 1, 78-86 (2013)
[50] Cai, Y.; Zhang, H.; Gao, Z.; Sun, S., The distributed output consensus control of linear heterogeneous multi-agent systems based on event-triggered transmission mechanism under directed topology, J. Frankl. Inst., 357, 6, 3267-3298 (2020) · Zbl 1437.93117
[51] Chen, Y.; Shi, L.; Shao, J.; Zheng, W., Sampled-data scaled group consensus for second-order multi-agent systems with switching topologies and random link failures, J. Frankl. Inst., 357, 5, 2868-2881 (2020) · Zbl 1451.93349
[52] Poulsen, D.; Defoort, M.; Djemai, M., Mean square consensus of double-integrator multi-agent systems under intermittent control: a stochastic time scale approach, J. Frankl. Inst., 356, 16, 9076-9094 (2019) · Zbl 1423.93356
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