Square-root algorithms for maximum correntropy estimation of linear discrete-time systems in presence of non-Gaussian noise
Recent developments in the realm of state estimation of stochastic dynamic systems in the presence of non-Gaussian noise have induced a new methodology called the maximum correntropy filtering. The filters designed under the maximum correntropy criterion (MCC) utilize a similarity measure (or corren...
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| Vydáno v: | Systems & control letters Ročník 108; s. 8 - 15 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.10.2017
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| ISSN: | 0167-6911, 1872-7956 |
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| Abstract | Recent developments in the realm of state estimation of stochastic dynamic systems in the presence of non-Gaussian noise have induced a new methodology called the maximum correntropy filtering. The filters designed under the maximum correntropy criterion (MCC) utilize a similarity measure (or correntropy) between two random variables as a cost function. They are shown to improve the estimators’ robustness against outliers or impulsive noises. In this paper we explore the numerical stability of linear filtering technique proposed recently under the MCC approach. The resulted estimator is called the maximum correntropy criterion Kalman filter (MCC-KF). The purpose of this study is two-fold. First, the previously derived MCC-KF equations are revised and the related Kalman-like equality conditions are proved. Based on this theoretical finding, we improve the MCC-KF technique in the sense that the new method possesses a better estimation quality with the reduced computational cost compared with the previously proposed MCC-KF variant. Second, we devise some square-root implementations for the newly-designed improved estimator. The square-root algorithms are well known to be inherently more stable than the conventional Kalman-like implementations, which process the full error covariance matrix in each iteration step of the filter. Additionally, following the latest achievements in the KF community, all square-root algorithms are formulated here in the so-called array form. It implies the use of orthogonal transformations for recursive update of the required filtering quantities and, thereby, no loss of accuracy is incurred. Apart from the numerical stability benefits, the array form also makes the modern Kalman-like filters better suited to parallel implementation and to very large scale integration (VLSI) implementation. All the MCC-KF variants developed in this paper are demonstrated to outperform the previously proposed MCC-KF version in two numerical examples. |
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| AbstractList | Recent developments in the realm of state estimation of stochastic dynamic systems in the presence of non-Gaussian noise have induced a new methodology called the maximum correntropy filtering. The filters designed under the maximum correntropy criterion (MCC) utilize a similarity measure (or correntropy) between two random variables as a cost function. They are shown to improve the estimators’ robustness against outliers or impulsive noises. In this paper we explore the numerical stability of linear filtering technique proposed recently under the MCC approach. The resulted estimator is called the maximum correntropy criterion Kalman filter (MCC-KF). The purpose of this study is two-fold. First, the previously derived MCC-KF equations are revised and the related Kalman-like equality conditions are proved. Based on this theoretical finding, we improve the MCC-KF technique in the sense that the new method possesses a better estimation quality with the reduced computational cost compared with the previously proposed MCC-KF variant. Second, we devise some square-root implementations for the newly-designed improved estimator. The square-root algorithms are well known to be inherently more stable than the conventional Kalman-like implementations, which process the full error covariance matrix in each iteration step of the filter. Additionally, following the latest achievements in the KF community, all square-root algorithms are formulated here in the so-called array form. It implies the use of orthogonal transformations for recursive update of the required filtering quantities and, thereby, no loss of accuracy is incurred. Apart from the numerical stability benefits, the array form also makes the modern Kalman-like filters better suited to parallel implementation and to very large scale integration (VLSI) implementation. All the MCC-KF variants developed in this paper are demonstrated to outperform the previously proposed MCC-KF version in two numerical examples. |
| Author | Kulikova, M.V. |
| Author_xml | – sequence: 1 givenname: M.V. orcidid: 0000-0001-8470-9831 surname: Kulikova fullname: Kulikova, M.V. email: maria.kulikova@ist.utl.pt organization: CEMAT (Center for Computational and Stochastic Mathematics), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal |
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| Cites_doi | 10.1109/TASL.2008.2002039 10.1016/j.sigpro.2016.06.025 10.1109/TAC.1986.1104128 10.1109/IJCNN.2012.6252730 10.1109/TPAMI.2010.220 10.1109/9.280773 10.1109/LSP.2015.2428713 10.1137/15M1039833 10.1109/LSP.2014.2319308 10.1109/TSP.2007.896065 10.1016/j.automatica.2016.10.004 10.1016/0165-1684(94)90077-9 10.1016/j.jfranklin.2015.03.039 10.1109/TAC.2013.2272136 10.1016/0005-1098(89)90013-7 10.1109/CISS.2016.7460553 10.1109/TAC.1971.1099816 10.1109/TSP.2015.2493985 10.1016/j.sigpro.2008.07.005 10.1093/oso/9780198535645.003.0010 10.1016/j.ejcon.2014.11.003 10.1109/9.384225 |
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| Keywords | Maximum correntropy criterion Robust estimation Square-root filtering Kalman filter |
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| References | Liu, Pokharel, Príncipe (b1) 2007; 55 G.T. Cinar, J.C. Príncipe, Hidden state estimation using the Correntropy Filter with fixed point update and adaptive kernel size, in: The 2012 International Joint Conference on Neural Networks, IJCNN, 2012, pp. 1–6. Kailath, Sayed, Hassibi (b19) 2000 Chen, Liu, Zhao, Príncipe (b10) 2017; 76 Higham (b22) 2002 Chen, Wang, Zhao, Zheng, Príncipe (b8) 2015; 22 R. Izanloo, S.A. Fakoorian, H.S. Yazdi, D. Simon, Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise, in: 2016 Annual Conference on Information Science and Systems, CISS, 2016,pp. 500–505. Kulikov, Kulikova (b30) 2016; 38 Xu, Príncipe (b5) 2008; 16 Sayed, Kailath (b17) 1994; AC-39 He, Zheng, Hu (b6) 2011; 33 N.J. Higham, Analysis of the Cholesky decomposition of a semi-definite matrix, Tech. Rep. MIMS EPrint: 2008.56, University of Manchester 1990. Liu, Qu, Zhao, Chen (b11) 2017; 130 Simon (b21) 2006 Kulikov, Kulikova (b27) 2014; 59 Park, Kailath (b18) 1995; 40 Kulikov, Kulikova (b29) 2016; 64 Kaminski, Bryson, Schmidt (b15) 1971; AC-16 Bierman (b16) 1977 Park, Kailath (b26) 1994; 40 . Gunduz, Príncipe (b2) 2009; 89 Príncipe (b3) 2010 Chen, Xing, Liang, Zheng, Príncipe (b7) 2014; 21 R. Izanloo, S.A. Fakoorian, H.S. Yazdi, D. Simon, MatLab codes 2016. Grewal, Andrews (b23) 2015 Kulikov, Kulikova (b28) 2015; 21 Ma, Qu, Gui, Xu, Zhao, Chen (b9) 2015; 352 Verhaegen (b14) 1989; 25 Golub, Van Loan (b24) 1983 Verhaegen, Van Dooren (b13) 1986; AC-31 10.1016/j.sysconle.2017.07.016_b4 Golub (10.1016/j.sysconle.2017.07.016_b24) 1983 10.1016/j.sysconle.2017.07.016_b12 Príncipe (10.1016/j.sysconle.2017.07.016_b3) 2010 Verhaegen (10.1016/j.sysconle.2017.07.016_b13) 1986; AC-31 Park (10.1016/j.sysconle.2017.07.016_b18) 1995; 40 He (10.1016/j.sysconle.2017.07.016_b6) 2011; 33 Kulikov (10.1016/j.sysconle.2017.07.016_b28) 2015; 21 Kulikov (10.1016/j.sysconle.2017.07.016_b30) 2016; 38 Xu (10.1016/j.sysconle.2017.07.016_b5) 2008; 16 Ma (10.1016/j.sysconle.2017.07.016_b9) 2015; 352 Park (10.1016/j.sysconle.2017.07.016_b26) 1994; 40 Gunduz (10.1016/j.sysconle.2017.07.016_b2) 2009; 89 Kaminski (10.1016/j.sysconle.2017.07.016_b15) 1971; AC-16 Sayed (10.1016/j.sysconle.2017.07.016_b17) 1994; AC-39 10.1016/j.sysconle.2017.07.016_b20 Chen (10.1016/j.sysconle.2017.07.016_b8) 2015; 22 Kulikov (10.1016/j.sysconle.2017.07.016_b27) 2014; 59 Kulikov (10.1016/j.sysconle.2017.07.016_b29) 2016; 64 10.1016/j.sysconle.2017.07.016_b25 Simon (10.1016/j.sysconle.2017.07.016_b21) 2006 Liu (10.1016/j.sysconle.2017.07.016_b11) 2017; 130 Verhaegen (10.1016/j.sysconle.2017.07.016_b14) 1989; 25 Bierman (10.1016/j.sysconle.2017.07.016_b16) 1977 Chen (10.1016/j.sysconle.2017.07.016_b7) 2014; 21 Chen (10.1016/j.sysconle.2017.07.016_b10) 2017; 76 Kailath (10.1016/j.sysconle.2017.07.016_b19) 2000 Grewal (10.1016/j.sysconle.2017.07.016_b23) 2015 Higham (10.1016/j.sysconle.2017.07.016_b22) 2002 Liu (10.1016/j.sysconle.2017.07.016_b1) 2007; 55 |
| References_xml | – volume: AC-39 start-page: 619 year: 1994 end-page: 622 ident: b17 article-title: Extended publication-title: IEEE Trans. Autom. Control – year: 2002 ident: b22 publication-title: Accuracy and Stability of Numerical Algorithms – volume: 59 start-page: 273 year: 2014 end-page: 279 ident: b27 article-title: Accurate numerical implementation of the continuous-discrete extended Kalman filter publication-title: IEEE Trans. Automat. Control – volume: 89 start-page: 14 year: 2009 end-page: 23 ident: b2 article-title: Correntropy as a novel measure for nonlinearity tests publication-title: Signal Process. – volume: 130 start-page: 152 year: 2017 end-page: 158 ident: b11 article-title: State space maximum correntropy filter publication-title: Signal Process. – year: 2015 ident: b23 publication-title: Kalman Filtering: Theory and Practice using MATLAB – reference: R. Izanloo, S.A. Fakoorian, H.S. Yazdi, D. Simon, MatLab codes 2016. – volume: 64 start-page: 948 year: 2016 end-page: 958 ident: b29 article-title: The accurate continuous-discrete extended Kalman filter for radar tracking publication-title: IEEE Trans. Signal Process. – reference: R. Izanloo, S.A. Fakoorian, H.S. Yazdi, D. Simon, Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise, in: 2016 Annual Conference on Information Science and Systems, CISS, 2016,pp. 500–505. – volume: 25 start-page: 437 year: 1989 end-page: 444 ident: b14 article-title: Round-off error propagation in four generally-applicable, recursive, least-squares estimation schemes publication-title: Automatica – year: 1983 ident: b24 publication-title: Matrix Computations – volume: AC-16 start-page: 727 year: 1971 end-page: 735 ident: b15 article-title: Discrete square-root filtering: a survey of current techniques publication-title: IEEE Trans. Automat. Control – year: 2006 ident: b21 publication-title: Optimal State Estimation: Kalman, H-Infinity, and Nonlinear Approaches – volume: 21 start-page: 880 year: 2014 end-page: 884 ident: b7 article-title: Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion publication-title: IEEE Signal Process. Lett. – reference: N.J. Higham, Analysis of the Cholesky decomposition of a semi-definite matrix, Tech. Rep. MIMS EPrint: 2008.56, University of Manchester 1990. – volume: 21 start-page: 14 year: 2015 end-page: 26 ident: b28 article-title: High-order accurate continuous-discrete extended Kalman filter for chemical engineering publication-title: Eur. J. Control – volume: 38 start-page: A3565 year: 2016 end-page: A3588 ident: b30 article-title: Estimating the state in stiff continuous-time stochastic systems within extended Kalman filtering publication-title: SIAM J. Sci. Comput. – reference: . – volume: 16 start-page: 1420 year: 2008 end-page: 1432 ident: b5 article-title: A pitch detector based on a generalized correlation function publication-title: IEEE Trans. Audio Speech Lang. Process. – volume: 40 start-page: 311 year: 1994 end-page: 318 ident: b26 article-title: An extended inverse QR adaptive filtering algorithm publication-title: Signal Process. – year: 1977 ident: b16 publication-title: Factorization Methods for Discrete Sequential Estimation – reference: G.T. Cinar, J.C. Príncipe, Hidden state estimation using the Correntropy Filter with fixed point update and adaptive kernel size, in: The 2012 International Joint Conference on Neural Networks, IJCNN, 2012, pp. 1–6. – volume: 55 start-page: 5286 year: 2007 end-page: 5298 ident: b1 article-title: Correntropy: properties and applications in non-Gaussian signal processing publication-title: IEEE Trans. Signal Process. – volume: 33 start-page: 1561 year: 2011 end-page: 1576 ident: b6 article-title: Maximum correntropy criterion for robust face recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 352 start-page: 2708 year: 2015 end-page: 2727 ident: b9 article-title: Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments publication-title: J. Franklin Inst. B – volume: 22 start-page: 1723 year: 2015 end-page: 1727 ident: b8 article-title: Convergence of a fixed-point algorithm under maximum correntropy criterion publication-title: IEEE Signal Process. Lett. – volume: 76 start-page: 70 year: 2017 end-page: 77 ident: b10 article-title: Maximum correntropy Kalman filter publication-title: Automatica – volume: AC-31 start-page: 907 year: 1986 end-page: 917 ident: b13 article-title: Numerical aspects of different Kalman filter implementations publication-title: IEEE Trans. Autom. Control – volume: 40 start-page: 895 year: 1995 end-page: 899 ident: b18 article-title: New square-root algorithms for publication-title: IEEE Trans. Automat. Control – year: 2010 ident: b3 publication-title: Information Theoretic Learning: Renyi’s Entropy and Kernel Perspectives – year: 2000 ident: b19 publication-title: Linear Estimation – year: 2002 ident: 10.1016/j.sysconle.2017.07.016_b22 – volume: 16 start-page: 1420 issue: 8 year: 2008 ident: 10.1016/j.sysconle.2017.07.016_b5 article-title: A pitch detector based on a generalized correlation function publication-title: IEEE Trans. Audio Speech Lang. Process. doi: 10.1109/TASL.2008.2002039 – volume: 130 start-page: 152 year: 2017 ident: 10.1016/j.sysconle.2017.07.016_b11 article-title: State space maximum correntropy filter publication-title: Signal Process. doi: 10.1016/j.sigpro.2016.06.025 – volume: AC-31 start-page: 907 issue: 10 year: 1986 ident: 10.1016/j.sysconle.2017.07.016_b13 article-title: Numerical aspects of different Kalman filter implementations publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.1986.1104128 – ident: 10.1016/j.sysconle.2017.07.016_b4 doi: 10.1109/IJCNN.2012.6252730 – volume: 33 start-page: 1561 issue: 8 year: 2011 ident: 10.1016/j.sysconle.2017.07.016_b6 article-title: Maximum correntropy criterion for robust face recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.220 – volume: AC-39 start-page: 619 issue: 3 year: 1994 ident: 10.1016/j.sysconle.2017.07.016_b17 article-title: Extended Chandrasekhar recursion publication-title: IEEE Trans. Autom. Control doi: 10.1109/9.280773 – year: 1983 ident: 10.1016/j.sysconle.2017.07.016_b24 – year: 2000 ident: 10.1016/j.sysconle.2017.07.016_b19 – volume: 22 start-page: 1723 issue: 10 year: 2015 ident: 10.1016/j.sysconle.2017.07.016_b8 article-title: Convergence of a fixed-point algorithm under maximum correntropy criterion publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2015.2428713 – volume: 38 start-page: A3565 issue: 6 year: 2016 ident: 10.1016/j.sysconle.2017.07.016_b30 article-title: Estimating the state in stiff continuous-time stochastic systems within extended Kalman filtering publication-title: SIAM J. Sci. Comput. doi: 10.1137/15M1039833 – year: 2010 ident: 10.1016/j.sysconle.2017.07.016_b3 – volume: 21 start-page: 880 issue: 7 year: 2014 ident: 10.1016/j.sysconle.2017.07.016_b7 article-title: Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2014.2319308 – year: 1977 ident: 10.1016/j.sysconle.2017.07.016_b16 – volume: 55 start-page: 5286 issue: 11 year: 2007 ident: 10.1016/j.sysconle.2017.07.016_b1 article-title: Correntropy: properties and applications in non-Gaussian signal processing publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2007.896065 – volume: 76 start-page: 70 year: 2017 ident: 10.1016/j.sysconle.2017.07.016_b10 article-title: Maximum correntropy Kalman filter publication-title: Automatica doi: 10.1016/j.automatica.2016.10.004 – volume: 40 start-page: 311 issue: 2–3 year: 1994 ident: 10.1016/j.sysconle.2017.07.016_b26 article-title: An extended inverse QR adaptive filtering algorithm publication-title: Signal Process. doi: 10.1016/0165-1684(94)90077-9 – volume: 352 start-page: 2708 issue: 7 year: 2015 ident: 10.1016/j.sysconle.2017.07.016_b9 article-title: Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments publication-title: J. Franklin Inst. B doi: 10.1016/j.jfranklin.2015.03.039 – volume: 59 start-page: 273 issue: 1 year: 2014 ident: 10.1016/j.sysconle.2017.07.016_b27 article-title: Accurate numerical implementation of the continuous-discrete extended Kalman filter publication-title: IEEE Trans. Automat. Control doi: 10.1109/TAC.2013.2272136 – volume: 25 start-page: 437 issue: 3 year: 1989 ident: 10.1016/j.sysconle.2017.07.016_b14 article-title: Round-off error propagation in four generally-applicable, recursive, least-squares estimation schemes publication-title: Automatica doi: 10.1016/0005-1098(89)90013-7 – ident: 10.1016/j.sysconle.2017.07.016_b12 doi: 10.1109/CISS.2016.7460553 – volume: AC-16 start-page: 727 issue: 6 year: 1971 ident: 10.1016/j.sysconle.2017.07.016_b15 article-title: Discrete square-root filtering: a survey of current techniques publication-title: IEEE Trans. Automat. Control doi: 10.1109/TAC.1971.1099816 – year: 2006 ident: 10.1016/j.sysconle.2017.07.016_b21 – volume: 64 start-page: 948 issue: 4 year: 2016 ident: 10.1016/j.sysconle.2017.07.016_b29 article-title: The accurate continuous-discrete extended Kalman filter for radar tracking publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2015.2493985 – volume: 89 start-page: 14 issue: 1 year: 2009 ident: 10.1016/j.sysconle.2017.07.016_b2 article-title: Correntropy as a novel measure for nonlinearity tests publication-title: Signal Process. doi: 10.1016/j.sigpro.2008.07.005 – ident: 10.1016/j.sysconle.2017.07.016_b25 doi: 10.1093/oso/9780198535645.003.0010 – volume: 21 start-page: 14 year: 2015 ident: 10.1016/j.sysconle.2017.07.016_b28 article-title: High-order accurate continuous-discrete extended Kalman filter for chemical engineering publication-title: Eur. J. Control doi: 10.1016/j.ejcon.2014.11.003 – volume: 40 start-page: 895 issue: 5 year: 1995 ident: 10.1016/j.sysconle.2017.07.016_b18 article-title: New square-root algorithms for Kalman filtering publication-title: IEEE Trans. Automat. Control doi: 10.1109/9.384225 – ident: 10.1016/j.sysconle.2017.07.016_b20 – year: 2015 ident: 10.1016/j.sysconle.2017.07.016_b23 |
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| SubjectTerms | Kalman filter Maximum correntropy criterion Robust estimation Square-root filtering |
| Title | Square-root algorithms for maximum correntropy estimation of linear discrete-time systems in presence of non-Gaussian noise |
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