Self-tuning weighted measurement fusion Wiener filter for autoregressive moving average signals with coloured noise and its convergence analysis
Self-tuning weighted measurement fusion Wiener filter for autoregressive moving average signals with coloured noise and its convergence analysis
- Author(s): J. Liu and Z. Deng
- DOI: 10.1049/iet-cta.2011.0408
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- Author(s): J. Liu 1, 2 and Z. Deng 1
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View affiliations
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Affiliations:
1: Department of Automation, Heilongjiang University, Harbin, People's Republic of China
2: Department of Computer and Information Engineering, Harbin Deqiang College of Commerce, Harbin, People's Republic of China
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Affiliations:
1: Department of Automation, Heilongjiang University, Harbin, People's Republic of China
- Source:
Volume 6, Issue 12,
16 August 2012,
p.
1899 – 1908
DOI: 10.1049/iet-cta.2011.0408 , Print ISSN 1751-8644, Online ISSN 1751-8652
For the multisensor single-channel autoregressive moving average (ARMA) signal with common coloured measurement noise, applying the modern time-series analysis method, based on the ARMA innovation model, the optimal weighted measurement fusion Wiener filter is presented. When the model parameters of coloured measurement noise and partial noise variances are unknown, by applying the recursive instrumental variable, the correlation method and the Gevers–Wouters iterative algorithm with dead band, their local estimates are obtained, then the fused estimates are obtained by taking the average of all corresponding local estimates. Substituting these fused estimates into the optimal weighted measurement fusion Wiener filter, a self-tuning weighted measurement fusion Wiener filter is obtained. By applying the dynamic error system analysis method, it is rigorously proved that the self-tuning weighted measurement fusion Wiener filter converges to the corresponding optimal weighted measurement fusion Wiener filter in a realisation, so that it has asymptotically global optimality. A simulation example shows its effectiveness.
Inspec keywords: self-adjusting systems; autoregressive moving average processes; iterative methods; sensor fusion; convergence; time series; Wiener filters
Other keywords:
Subjects: Other topics in statistics; Signal processing theory; Interpolation and function approximation (numerical analysis); Other topics in statistics; Sensor fusion; Interpolation and function approximation (numerical analysis); Filtering methods in signal processing
References
-
-
1)
- Y. Gao , C.J. Ran , X.J. Sun , Z.L. Deng . Optimal and self-tuning weighted measurement fusion Kalman filters and their asymptotic global optimality. Int. J. Adapt. Control Signal Process. , 982 - 1004
-
2)
- Z.L. Deng , Y. Gao , C.B. Li , G. Hao . Self-tuning decoupled information fusion Wiener state component filters and their Convergence. Automatica , 685 - 695
-
3)
- Z.L. Deng , C.B. Li . Self-tuning information fusion Kalman predictor weighted by diagonal matrices and its convergence analysis. Acta Autom. Sin. , 2 , 156 - 163
-
4)
- Liu, J.F., Deng, Z.L.: `Self-tuning information fusion Wiener filter for the AR signals and its convergence', Eighth IEEE Int. Conf. on Control and Automation, June 2010, Xiamen, China, p. 698–703.
-
5)
- X.J. Sun , Z.L. Deng . Information fusion steady-state white noise deconvolution estimators with time-delayed measurement and colored measurement noises. J. Electron. , 2 , 161 - 169
-
6)
- J.F. Liu , Z.L. Deng . An information fusion identification method for multisensor autoregressive moving average signals with white measurement noise and sensor bias. Sens. Lett. , 1443 - 1447
-
7)
- Ran, C.J., Gu, L., Deng, Z.L.: `Self-tuning centralized fusion Kalman filter for multisensor systems with companion form and its convergence', Eighth IEEE Int. Conf. on Control and Automation, June 2010, Xiamen, China, p. 645–650.
-
8)
- A. Mahmoudi , M. Karimi , H. Amindavar . Parameter estimation of autoregressive signals in presence of colored AR (1) noise as a quadratic eigenvalue problem. Signal Process. , 1151 - 1156
-
9)
- Liu, J.F., Deng, Z.L.: `Self-tuning information fusion Wiener filter for ARMA signals and its convergence', 29thChinese Control Conf., 2010, Beijing, China, p. 2739–2744.
-
10)
- W. Li , Y. Jia . Consensus-based distributed information filter for a class of jump Markov systems. IET Control Theory Appl. , 10 , 1214 - 1222
-
11)
- M. Gevers , W.R.E. Wouters . An innovations approach to the discrete-time stochastic realization problem. Q. J. Autom. , 90 - 109
-
12)
- Ran, C.J., Deng, Z.L.: `Fast self-tuning weighted measurement fusion Kalman filter for the ARMA signal', Proc. 2011 IEEE Int. Conf. on Mechatronics and Automation, August 2011, Beijing, China, p. 1131–1136.
-
13)
- J.D. Gibson , B. Koo , S.D. Gray . Filtering of colored noise for speech enhancement and coding. IEEE Trans. Signal Process. , 8 , 1732 - 1741
-
14)
- Ran, C.J., Deng, Z.L.: `Self-tuning measurement fusion Kalman filter for multisensor systems with companion form and common disturbance noise', 29thChinese Control Conf., 2010, Beijing, China, p. 1172–1177.
-
15)
- Y. Gao , W.J. Jia , X.J. Sun , Z.L. Deng . Self-tuning multisensor weighted measurement Kalman filter. IEEE Trans. Aerosp. Electron. Syst. , 1 , 179 - 191
-
16)
- C.J. Ran , Z.L. Deng . Self-tuning weighted measurement fusion Kalman filtering algorithm. Comput. Stat. Data Anal. , 2112 - 2128
-
17)
- S.L. Sun , C. Zhang . Optimal information fusion distributed smoother for discrete multichannel ARMA signals. IEE Proc.– Vis. Image Signal Process. , 583 - 589
-
18)
- M.E. Liggins , D.L. Hall , J. Llinas , Taylor&Francis Group . (2009) Handbook of multisensor data fusion, Theory and practice.
-
19)
- Q. Gan , C. Harris . Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Trans. Aerosp. Electron. Syst. , 1 , 273 - 280
-
20)
- X.R. Li , Y.M. Zhu , J. Wang , C.Z. Han . Optimal linear estimation fusion – Part I: unified fusion rules. IEEE Trans. Inf. Theory , 9 , 2192 - 2208
-
21)
- A. Mahmoudi , M. Karimi . Parameter estimation of autoregressive signals from observations corrupted with colored noise. Signal Process. , 157 - 164
-
22)
- Z.L. Deng . (2007) Multisensor information fusion filtering theory with applications.
-
23)
- Y. Bar-Shalom , X.R. Li . (1995) Multitarget-multisensor tracking: principles and techniques.
-
24)
- L. Ljung . (1987) System identification, theory for the user.
-
25)
- S. Pillosu , A. Pisano , E. Usai . Decentralised state estimation for linear systems with unknown inputs: a consensus-based approach. IET Control Theory Appl. , 3 , 498 - 506
-
26)
- S. Roy , R.A. Iltis . Decentralized linear estimation in correlated measurement noise. IEEE Trans. Aerosp. Electron. Syst. , 6 , 939 - 941
-
27)
- Tao, G.L., Wang, W., Deng, Z.L.: `The self-tuning distributed information fusion Wiener filter for the ARMA signals', Proc. Eighth World Congress on Intelligent Control and Automation, 2010, Jinan, China, p. 6897–6902.
-
28)
- W. Wang , H. Zhang , L. Xie . Kalman filtering for continuous-time systems with time-varying delay. IET Control Theory Appl. , 590 - 600
-
29)
- E. Song , Y. Zhu , J. Zhou , Z. You . Optimal Kalman filtering fusion with cross-correlated sensor noises. Automatica , 8 , 1450 - 1456
-
30)
- X.J. Sun , Z.L. Deng . Optimal and self-tuning weighted measurement fusionWiener filter for the multisensor multichannel ARMA signals. Signal Process. , 5 , 738 - 752
-
31)
- Z.L. Deng , Y. Gao . Multichannel ARMA signal information fusion Wiener filter. J. Electron. Inf. Technol. , 9 , 1416 - 1419
-
32)
- S.L. Sun . Optimal multi-sensor Kalman smothering fusion for discrete multichannel ARMA signal. J. Control Theory Appl. , 168 - 172
-
33)
- C.J. Ran , Y.S. Hui , L. Gu , Z.L. Deng . Correlated measurement fusion steady-state Kalman filtering algorithms and their optimality. Acta Autom. Sin. , 3 , 233 - 239
-
34)
- J.F. Liu , Z.L. Deng . Self-tuning weighted measurement fusion Kalman filter for ARMA signals with colored noise. Appl. Math. Inf. Sci. , 1 - 7
-
35)
- C.J. Ran , Z.L. Deng . Self-tuning measurement fusion Kalman predictors and their convergence analysis. Int. J. Syst. Sci. , 10 , 1697 - 1708
-
36)
- Gao, Y., Xu, H.Q., Deng, Z.L.: `Multi-stage Information fusion identification method for multisensor ARMA signals with white measurement noises', Eighth IEEE Int. Conf. on Control and Automation, June 2010, Xiamen, China, p. 1115–1119.
-
37)
- C.J. Ran , Z.L. Deng . Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal. Signal Process. , 2028 - 2041
-
38)
- C.J. Ran , G.L. Tao , J.F. Liu , Z.L. Deng . Self-tuning decoupled fusion Kalman predictor and its convergence analysis. IEEE Sens. J. , 12 , 2024 - 2032
-
39)
- W. Rudin . (1976) Principles of mathematical analysis.
-
40)
- W.-R. Wu , D.-C. Chang . Maneuvering target tracking with colored noise. IEEE Trans. Aerosp. Electron. Syst. , 4 , 1311 - 1320
-
1)