References
Wang Y H, Lin P, Hong Y G. Distributed regression estimation with incomplete data in multi-agent networks. Sci China Inf Sci, 2018, 61: 092202
Shen K, Jing Z, Dong P. A consensus nonlinear filter with measurement uncertainty in distributed sensor networks. IEEE Signal Process Lett, 2017, 24: 1631–1635
Battistelli G, Chisci L. Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability. Automatica, 2014, 50: 707–718
Liu Q, Wang Z, He X, et al. Event-based recursive distributed filtering over wireless sensor networks. IEEE Trans Automat Contr, 2015, 60: 2470–2475
Liu Q, Wang Z, He X, et al. A resilient approach to distributed filter design for time-varying systems under stochastic nonlinearities and sensor degradation. IEEE Trans Signal Process, 2017, 65: 1300–1309
Agamennoni G, Nieto J I, Nebot E M. Approximate inference in state-space models with heavy-tailed noise. IEEE Trans Signal Process, 2012, 60: 5024–5037
Piché R, Särkkä S, Hartikainen J. Recursive outlierrobust filtering and smoothing for nonlinear systems using the multivariate Student-t distribution. In: Prcoeedings of IEEE International Workshop on Machine Learning for Signal Processing, Santander, 2012. 1–6
Nurminen H, Ardeshiri T, Piche R, et al. Robust inference for state-space models with skewed measurement noise. IEEE Signal Process Lett, 2015, 22: 1898–1902
Acknowledgements
This work was jointly supported by National Natural Science Foundation of China (Grant Nos. 61673262, 61175028), Major Program of National Natural Science Foundation of China (Grant Nos. 61690210, 61690212), and Shanghai Key Project of Basic Research (Grant No. 16JC1401100).
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Dong, P., Jing, Z., Shen, K. et al. A distributed consensus filter for sensor networks with heavy-tailed measurement noise. Sci. China Inf. Sci. 61, 119201 (2018). https://doi.org/10.1007/s11432-017-9350-y
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DOI: https://doi.org/10.1007/s11432-017-9350-y