Abstract
In order to ensure the successful completion of the tasks of a system, the accuracy of data is the foundation and indemnification. To complete the data verification on the decentralized computing platform, a decentralized Kalman filter with state constraints is presented in this paper. The decentralized sensing architecture takes the form of a network of transputer-based sensor nodes, each with its own processing system. So it does not require any central processor or common clock. Based on that, this new algorithm can allow fully decentralization of the multisensory Kalman filter equations with state equality constraints among a number of sensing nodes to verify the data. The algorithm is developed from a centralized method named projection method to minimized the communication among nodes and can take place without any prior synchronization between nodes. Theoretical derivation is provided to the decentralized algorithm. Finally, the case study of the secondary chilled water pump system illustrates the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qi, S.: Studies on architecture of decentralized system in intelligent building. Tsinghua University (in Chinese) (2015)
Lin, et al.: Unbalanced distribution network fault analysis with hybrid compensation. IET Gener. Transm. Distrib. 5(1), 92–100(2010)
Basar, T.: A new approach to linear filtering and prediction problems. Wiley-IEEE Press, pp. 35–45 (2009)
Zhang, J., You, K., Xie, L.: Bayesian Filtering with unknown sensor measurement losses. IEEE Trans. Control. Netw. Syst. 49(22), 315–320 (2016)
Geng, K.K., Chu lin, N.A.: Applications of multi-height sensors data fusion and fault-tolerant Kalman filter in integrated navigation system of UAV. Elsevier Science Publishers B. V (2017)
Jayaram, S.: A new fast converging Kalman filter for sensor fault detection and isolation. Sens. Rev. 16(3), 219–224 (2013)
Mahmoud, M.S., Khalid, H.M.: Fault estimation and monitoring with multi-sensor data fusion: an unscented Kalman filter approach. Dyn. Syst. (2011)
Wei, X., Verhaegen, M., Engelen, T.V.: Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques. Int. J. Adapt. Control. Signal Process. 24(8), 687–707 (2010)
Simon, D., Chia, T.L.: Kalman filtering with state equality constraints. IEEE Trans. Aerosp. Electr. Syst. 128–136 (2002)
Wang, S., et al.: Decentralized optimization for a novel control structure of HVAC system. Mathematical problems in engineering (2016-5-9) 12, 1−13 (2016)
Mendel, J.: Optimal filtering. IEEE Trans. Autom. Control. 25(3), 615–616 (1980)
Jian, L.: Constrained information-based state estimation and fusion. Dissertation, (in Chinese), Shanghai Jiaotong University, China (2014)
Acknowledgements
This work is supported by National Key Research and Development Project of China No. 2017YFC0704100 (entitled New generation Intelligent building platform techniques)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, G., Wang, S. (2019). A Decentralized Parallel Kalman Filter in Multi-sensor System for Data Verification. In: Fang, Q., Zhu, Q., Qiao, F. (eds) Advancements in Smart City and Intelligent Building. ICSCIB 2018. Advances in Intelligent Systems and Computing, vol 890 . Springer, Singapore. https://doi.org/10.1007/978-981-13-6733-5_54
Download citation
DOI: https://doi.org/10.1007/978-981-13-6733-5_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6732-8
Online ISBN: 978-981-13-6733-5
eBook Packages: EngineeringEngineering (R0)