Abstract
In this chapter, a state fusion estimator design method will be introduced for multisensor systems with measurement delays, which is usually inevitable in sensor networks. Due to delays in the measurements, it is difficult to construct an innovation sequence that is still white Gaussian as usually does in the standard Kalman filter. Therefore, many research works have been devoted to the design of optimal linear estimators for time-delay systems by using the innovation analysis approach and linear matrix inequality approach [1–7]. For the multisensor case, the information fusion problem has been investigated in [8, 9] for linear stochastic systems with time-delayed measurements, where the observation delays were assumed to be constant.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang H, Xie L, Zhang D, Soh YC (2004) A reorganized innovation approach to linear estimation. IEEE Trans Autom Control 49(10):1810–1814
Chen B, Yu L, Zhang WA (2011) Robust Kalman filtering for uncertain state delay systems with random observation delays and missing measurements. IET Control Theory Appl 5(17):1945–1954
Dong H, Wang Z, Gao H (2010) Robust \(H_{\infty }\) filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts. IEEE Trans Signal Process 58(4):1957–1966
Zhang H, Feng G, Han C (2011) Linear estimation for random delay systems. Syst Control Lett 60(7):450–459
Zhang H, Feng G, Duan G, Lu X (2006) \(H_{\infty }\) filtering for multiple-time-delay measurements. IEEE Trans Signal Process 54(5):1681–1688
Ma L, Da F, Zhang KJ (2011) Exponential \(H_{\infty }\) filter design for discrete time-delay stochastic systems with markovian jump parameters and missing measurements. IEEE Trans Circuits Syst-I Regul Pap 58(5):994–1007
Yang F, Wang Z, Feng G, Liu X (2009) Robust filtering with randomly varying sensor delay: the finite-horizon case. IEEE Trans Circuits Syst-I Regul Pap 56(3):664–672
Sun XJ, Deng ZL (2009) Information fusion wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements. IET Signal Process 3(5):403–415
Lv N, Sun SL (2009) Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements. In: Proceedings of IEEE conference on decision and control, Shanghai, Dec 2009, pp 7599–7602
Xia Y, Shang J, Chen J, Liu GP (2009) Networked data fusion with packet losses and variable delays. IEEE Trans Syst Man Cybern B Cybern 39(5):1107–1120
Hounkpevi FO, Yaz EE (2007) Robust minimum variance linear state estimators for multiple sensors with different failure rates. Automatica 43(7):1274–1280
Ahmad A, Gani M, Yang F (2008) Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks. Signal Process 88(8):1919–1928
Wang Z, Zhen Z, Zhang H, Chen Z (2009) Robust information fusion filtering method for discrete-time linear uncertain system. In: IEEE international conference on control and automation, Christchurch, Dec 2009, pp 1734–1738
Feng J, Wang Z, Zeng M (2013) Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises. Inf Fusion 14(1):78–86
Gao H, Meng X, Chen T (2008) Stabilization of networked control systems with a new delay characterization. IEEE Trans Autom Control 53(9):2142–2148
Sun SL, Deng ZL (2004) Multi-sensor optimal information fusion Kalman filter. Automatica 40(6):1017–1023
Kailath T, Syayed AH, Hassibi B (2000) Linear estimation. Prentice Hall, Upper Saddle River
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Science Press, Beijing and Springer Science+Business Media Singapore
About this chapter
Cite this chapter
Zhang, WA., Chen, B., Song, H., Yu, L. (2016). Fusion Estimation for WSNs with Delayed Measurements. In: Distributed Fusion Estimation for Sensor Networks with Communication Constraints. Springer, Singapore. https://doi.org/10.1007/978-981-10-0795-8_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-0795-8_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0793-4
Online ISBN: 978-981-10-0795-8
eBook Packages: EngineeringEngineering (R0)