Consensus-based distributed information filter for a class of jump Markov systems
Consensus-based distributed information filter for a class of jump Markov systems
- Author(s): W. Li and Y. Jia
- DOI: 10.1049/iet-cta.2010.0240
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- Author(s): W. Li 1 and Y. Jia 1, 2
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View affiliations
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Affiliations:
1: Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing, People's Republic of China
2: Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Ministry of Education, SMSS, Beihang University (BUAA), Beijing, People's Republic of China
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Affiliations:
1: Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing, People's Republic of China
- Source:
Volume 5, Issue 10,
7 July 2011,
p.
1214 – 1222
DOI: 10.1049/iet-cta.2010.0240 , Print ISSN 1751-8644, Online ISSN 1751-8652
This study investigates the problem of distributed fusion for a class of jump Markov systems in a not fully-connected sensor network. A distributed information filter is proposed from the point of view of the consensus theory. To this end, the best-fitting Gaussian (BFG) approximation approach is applied to overcome the difficulty of lacking a global model for multiple model estimation fusion, and a recursive formula is presented for calculating the mean and covariance of this Gaussian distribution. Based on the approximated linear Gaussian system, local information filter is derived for each sensor and the filtering estimates are fused with its neighbouring sensor nodes using the dynamic average-consensus strategy. Performance comparison of the proposed filter with the optimal centralised fusion filter is demonstrated through a multi-static manoeuvring target-tracking simulation study.
Inspec keywords: stochastic systems; Gaussian distribution; linear systems; target tracking; sensor fusion
Other keywords:
Subjects: Signal processing theory; Time-varying control systems; Other topics in statistics
References
-
-
1)
- W. Ren , R.W. Beard , E.M. Atkins . Information consensus in multivehicle cooperative control. IEEE Control Syst. Mag. , 2 , 71 - 82
-
2)
- Y. Yi , L. Guo . Constrained PI tracking control for the output PDFs based on T-S fuzzy model. Int. J. Innov. Comput. Inf. Control , 2 , 349 - 358
-
3)
- P. Zhan , D. Casbeer , A. Swindlehurst . Adaptive mobile sensor positioning for multi-static target tracking. IEEE Trans. Aerosp. Electron. Syst. , 1 , 120 - 132
-
4)
- Kingston, D., Beard, R.: `Discrete-time average-consensus under switching network topologies', Proc. American Control Conf., June 2006, Minneapolis, MN, USA, p. 3551–3556.
-
5)
- 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
-
6)
- Spanos, D., Olfati-Saber, R., Murray, R.: `Approximate distributed Kalman filtering in sensor networks with quantifiable performance', Fourth Int. Symp. Information Processing in Sensor Networks, April 2005, p. 133–139.
-
7)
- Casbeer, D., Beard, R.: `Distributed information filtering using consensus filters', Proc. American Control Conf., June 2009, St. Louis, MO, USA, p. 1882–1887, Hyatt Regency Riverfront.
-
8)
- E. Martin , C. Chong , I. Kadar , M. Alford , V. Vannicola , S. Thomopoulos . Distributed fusion architectures and algorithms for target tracking. Proc. IEEE , 1 , 95 - 107
-
9)
- Olfati-Saber, R.: `Distributed Kalman filtering for sensor networks', Proc. 46th IEEE Conf. Decision and Control, December 2007, New Orleans, LA, USA, p. 5492–5498.
-
10)
- Lee, D.: `Unscented information filtering for distributed estimation and multiple sensor fusion', AIAA Guidance, Navigation and Control Conf. Exhibit, August 2008, Honolulu, HI, p. 1–15.
-
11)
- F. Tao , Q. Zhao . Synthesis of active fault-tolerant control based on Markovian jump system models. IET Control Theory Appl. , 4 , 1160 - 1168
-
12)
- L. Hong . Distributed interacting multipattern data association for multiplatform target tracking. Signal Process. , 7 , 1007 - 1021
-
13)
- T. Vercauteren , X. Wang . Decentralized sigma-point information filters for target tracking in collaborative sensor networks. IEEE Trans. Signal Process. , 8 , 1997 - 2009
-
14)
- V. Dragan , T. Morozan . Discrete-time Riccati type equations and the tracking problem. ICIC Express Letters , 2 , 109 - 116
-
15)
- S. Bi , M. Deng , A. Inoue . Operator based robust stability and tracking performance of MIMO nonlinear systems. Int. J. Innov. Comput. Inf. Control , 10 , 3351 - 3358
-
16)
- M. Zhang , B. Chen , Y. Zhu , J. Hu . Laplacian Kernel based SIG algorithm for FIR filtering in the presence of alpha-stable noise. ICIC Express Letters , 1 , 173 - 176
-
17)
- D. Lee . Nonlinear estimation and multiple sensor fusion using unscented information filtering. IEEE Signal Process. Lett. , 861 - 864
-
18)
- W. Li , Y. Jia . Distributed interacting multiple model H∞ filtering fusion for multiplatform maneuvering target tracking in clutter. Signal Process. , 5 , 1655 - 1668
-
19)
- M. Hernandez , B. Ristic , A. Farina , T. Sathyan , T. Kirubarajan . Performance measure for Markovian switching systems using best-fitting Gaussian distributions. IEEE Trans. Aerosp. Electron. Syst. , 2 , 724 - 747
-
20)
- T. Aysal , B. Oreshkin , M. Coates . Accelerated distributed average consensus via localized node state prediction. IEEE Trans. Signal Process. , 4 , 1563 - 1576
-
21)
- Olfati-Saber, R., Shamma, J.: `Consensus filters for sensor networks and distributed sensor fusion', Proc. 44th IEEE Conf. Decision and Control and the European Control Conf., December 2005, Seville, Spain, p. 6698–6703.
-
22)
- R. Carli , A. Chiuso , L. Schenato , S. Zampieri . Distributed Kalman filtering based on consensus strategies. IEEE J. Sel. Areas Commun. , 4 , 622 - 633
-
23)
- A. Mutambara . (1998) Decentralized estimation and control for multisensor systems.
-
24)
- S. Sardellitti , M. Giona , S. Barbarossa . Fast distributed average consensus algorithms based on advection-diffusion processes. IEEE Trans. Signal Process. , 2 , 826 - 842
-
25)
- E. Kokiopoulou , P. Frossard . Polynomial filtering for fast convergence in distributed consensus. IEEE Trans. Signal Process. , 1 , 342 - 354
-
26)
- M.N. Petsios , E.G. Alivizatos , N.K. Uzunoglu . Manoeuvring target tracking using multiple bistatic range and range-rate measurements. Signal Process. , 4 , 665 - 686
-
27)
- H. Hashemipour , S. Roy , A. Laub . Decentralized structures for parallel Kalman filtering. IEEE Trans. Autom. Control , 1 , 88 - 93
-
28)
- Z. Ding , L. Hong . A distributed IMM fusion algorithm for multi-platform tracking. Signal Process. , 2 , 167 - 176
-
29)
- X. Li , V. Jilkov . Survey of maneuvering target tracking. Part V: multiple-model methods. IEEE Trans. Aerosp. Electron. Syst. , 4 , 1255 - 1321
-
30)
- H. Blom , Y. Bar-Shalom . The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Autom. Control , 8 , 780 - 783
-
31)
- R. Olfati-Saber , J.A. Fax , R.M. Murray . Consensus and cooperation in networked multi-agent systems. Proc. IEEE. , 1 , 215 - 233
-
32)
- Durrant-Whyte, H., Rao, B., Hu, H.: `Toward a fully decentralized architecture for multi-sensor data fusion', Proc. IEEE Int. Conf. Robot. Autom., 1990, p. 1331–1336.
-
33)
- J. Moore , V. Krishnamurthy . De-interleaving pulse trains using discrete-time stochastic dynamic-linear models. IEEE Trans. Signal Process. , 11 , 3092 - 3103
-
34)
- W. Yu , G. Chen , Z. Wang , W. Yang . Distributed consensus filtering in sensor networks. IEEE Trans. Syst. Man Cybern. B, Cybern. , 6 , 1568 - 1577
-
35)
- Olfati-Saber, R.: `Distributed Kalman filter with embedded consensus filters', Proc. 44th IEEE Conf. Decision and Control and the European Control Conf., December 2005, Seville, Spain, p. 8179–8184.
-
36)
- Casbeer, D., Beard, R.: `Multi-static radar target tracking using information consensus filters', AIAA Guidance, Navigation, and Control Conf., August 2009, Chicago, IL, p. 1–9.
-
37)
- Grime, S., Durrant-Whyte, H., Ho, P.: `Communication in decentralized data-fusion systems', Proc. Am. Control Conf., 1992, p. 3299–3303.
-
1)