Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels
The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the est...
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| Published in: | Digital signal processing Vol. 85; pp. 77 - 85 |
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| Format: | Journal Article |
| Language: | English |
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01.02.2019
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| ISSN: | 1051-2004, 1095-4333 |
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| Abstract | The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model. |
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| AbstractList | The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model. |
| Author | Caballero-Águila, R. Linares-Pérez, J. Hermoso-Carazo, A. |
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| Cites_doi | 10.1016/j.sigpro.2016.07.004 10.1080/00207721.2010.502601 10.1016/j.automatica.2013.08.021 10.1080/03081079.2017.1341501 10.3390/math5030045 10.1016/j.jfranklin.2017.01.027 10.1016/j.ins.2016.08.020 10.1155/2017/1570719 10.1016/j.automatica.2007.01.010 10.1109/JSEN.2012.2227995 10.1016/j.automatica.2013.09.013 10.1016/j.inffus.2017.03.003 10.1109/TSP.2016.2576420 10.1016/j.dsp.2016.10.003 10.1016/j.ins.2017.02.048 10.1109/TCNS.2015.2459351 10.1080/03081079.2014.973728 10.1016/j.sigpro.2016.02.014 10.1016/j.sigpro.2018.01.015 10.1016/j.ast.2016.11.025 10.1016/j.cam.2011.06.021 |
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| Keywords | Random parameter matrices Uncertain observations Random delays Centralized fusion estimation Packet dropouts |
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| References | Caballero-Águila, Hermoso-Carazo, Linares-Pérez (br0080) 2017; 5 Sun, Tian, Lin (br0110) 2016; 64 Guo (br0120) 2017; 354 Li, Jia, Du (br0070) 2017; 60 Caballero-Águila, Hermoso-Carazo, Linares-Pérez (br0140) 2015; 44 Wang, Sun (br0050) 2017; 63 Yan, Li Rong, Xia, Fu (br0030) 2013; 49 Caballero-Águila, Hermoso-Carazo, Linares-Pérez (br0150) 2016; 127 Ma, Sun (br0200) 2017; 130 Caballero-Águila, Hermoso-Carazo, Linares-Pérez (br0170) 2017; 46 Caballero-Águila, Hermoso-Carazo, Linares-Pérez (br0210) 2017; 2017 Hu, Wang, Alsaadi, Hayat (br0060) 2017; 38 Liu, Wang, He, Zhou (br0040) 2016; 3 Han, Dong, Wang, Li, Alsaadi (br0190) 2018; 147 Sun, Tian, Lin (br0180) 2017; 397–398 Ma, Sun (br0100) 2013; 13 Hu, Wang, Gao (br0130) 2013; 49 Yang, Liang, Pan, Qin, Yang (br0160) 2016; 370–371 García-Ligero, Hermoso-Carazo, Linares-Pérez (br0090) 2011; 236 Song, Zhua, Zhou, You (br0010) 2007; 43 Feng, Zeng (br0020) 2012; 43 Caballero-Águila (10.1016/j.dsp.2018.11.010_br0210) 2017; 2017 Liu (10.1016/j.dsp.2018.11.010_br0040) 2016; 3 Guo (10.1016/j.dsp.2018.11.010_br0120) 2017; 354 Song (10.1016/j.dsp.2018.11.010_br0010) 2007; 43 Caballero-Águila (10.1016/j.dsp.2018.11.010_br0140) 2015; 44 Hu (10.1016/j.dsp.2018.11.010_br0130) 2013; 49 Ma (10.1016/j.dsp.2018.11.010_br0100) 2013; 13 Sun (10.1016/j.dsp.2018.11.010_br0110) 2016; 64 Sun (10.1016/j.dsp.2018.11.010_br0180) 2017; 397–398 Yang (10.1016/j.dsp.2018.11.010_br0160) 2016; 370–371 Caballero-Águila (10.1016/j.dsp.2018.11.010_br0170) 2017; 46 Feng (10.1016/j.dsp.2018.11.010_br0020) 2012; 43 Yan (10.1016/j.dsp.2018.11.010_br0030) 2013; 49 Wang (10.1016/j.dsp.2018.11.010_br0050) 2017; 63 Caballero-Águila (10.1016/j.dsp.2018.11.010_br0150) 2016; 127 Li (10.1016/j.dsp.2018.11.010_br0070) 2017; 60 Caballero-Águila (10.1016/j.dsp.2018.11.010_br0080) 2017; 5 García-Ligero (10.1016/j.dsp.2018.11.010_br0090) 2011; 236 Han (10.1016/j.dsp.2018.11.010_br0190) 2018; 147 Ma (10.1016/j.dsp.2018.11.010_br0200) 2017; 130 Hu (10.1016/j.dsp.2018.11.010_br0060) 2017; 38 |
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| SubjectTerms | Centralized fusion estimation Packet dropouts Random delays Random parameter matrices Uncertain observations |
| Title | Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels |
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