1 Abedor J, Nagpal K, Poola K (1996) A linear matrix inequality approach to peak‐to‐peak gain minimization. Int J Robust Nonl Contr 6:899–927
2 Ahmed NA, Gokhale DV (1989) Entropy expressions and their estimators for multivariate distributions. IEEE Trans Inf Theory 35(3):688–692
3 Ahn CK, Han SH (2008) New FIR smoother for linear discrete‐time state‐space models. IEICE Trans Commun E91.B(3):896–899
4 Ahn CK, Kim PS (2008) Fixed‐lag maximum likelihood FIR smoother for state‐space models. IEICE Electron Express 5(1):11–16
5 Ahn CK, Kim PS (2013) New energy‐to‐peak FIR filter design for systems with disturbances: a convex optimization approach. Int J Inn Comp Inform Contr 9(5):1988–1993
6 Ahn CK (2014) A new solution to induced FIR filtering problem based on two matrix inequalities. Int J Contr 87(2):404–409
7 Ahn CK, Shmaliy YS, Shi P, Zhao S (2017) Receding‐horizon FIR filter with embedded deadbeat property. IEEE Trans Circ Syst–II Express Briefs 64(2):211–215
8 Alspach D, Sorenson H (1972) Nonlinear Bayesian estimation using Gaussian sum approximation. IEEE Trans Autom Contr 17(4):439–448
9 Anderson BDO, Moore JB (1979) Optimal Filtering. Prentice‐Hall, Englewood Cliffs, NJ
10 Arasaratnam I, Haykin S, Elliott RJ (2007) Discrete‐time nonlinear filtering algorithms using Gauss‐Hermite quadrature. Proc IEEE 95(5):953–977
11 Arceo‐Miquel L, Shmaliy YS, Ibarra‐Manzano O (2009) Optimal synchronization of local clocks by GPS 1PPS signals using predictive FIR filters. IEEE Trans Inst Meas 58(6):1833–1840
12 Athans M, Wishner RP, Bertolini A (1968) Suboptimal state estimation for continuous‐time nonlinear systems from discrete noisy measurements. IEEE Trans Autom Contr AC‐13:504–514
13 Aysal TC, Barner KE (2007) Meridian filtering for robust signal processing. IEEE Trans Signal Process 55(8):3949–3962
14 Banavar RN, Speyer JL (1991) A linear‐quadratic game approach to estimation and smoothing. IEEE Am Contr Conf, Boston, MA, USA, 26–28 June 1991
15 Bar‐Shalom Y, Li XR, Kirubarajan T (2001) Estimation with applications to tracking and navigation: theory algorithms and software, Wiley‐Interscience, New York
16 Basu AK (2005) Introduction to Stochastic Process. Alpha Science, Harrow, UK
17 Battistelli G, Chisci L (2014) Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability. Automatica 50(3):707–718
18 Bellman R (1960) Introduction to Matrix Analysis. McGraw‐Hill, New York
19 Bello P (1964) Time‐frequency duality. IEEE Trans Inform Theory IT‐10:18–33
20 Bierman GJ (1977) Factorization Methods for Discrete Sequential Estimation. Academic, New York
21 Box GEP (1953) Non‐normality and tests on variances. Biometrika 40(3‐4):318–335
22 Boyd S, Ghaoui LE, Feron E (1994) Linear Matrix Inequalities in System and Control Theory. SIAM, Philadelphia
23 Bruckstein A., Kailath T (1985) Recursive limited memory filtering and scattering theory. IEEE Trans Inform Theory 31(3):440–443
24 Bryson AE, Johansen D (1965) Linear filtering for time‐varying systems using measurements containing colored noise. IEEE Trans Autom Contr 10(1):4–10
25 Bryson AE, Henrikson LJ (1968) Estimation using sampled data containing sequentially correlated noise. J. Spacecraft Rockets 5(6):662–665
26 Butcher JC (2003) Numerical Methods for Ordinary Differential Equations. John Wiley & Sons, New York
27 Campbell TG, Neuvo Y (1991) Predictive FIR filters with low computational complexity. IEEE Trans Circ Syst 38(9):1067–1071
28 Carmen C (1999) Ordinary Differential Equations with Applications. Springer‐Verlag, New York
29 Castro‐Tinttori P, Ibarra‐Manzano O, Shmaliy YS (2012) Implementation of digital FIR filters with polynomial impulse responses. Circ Syst Signal Process 31(2):611–626
30 Castro‐Tinttori P, Ibarra‐Manzano O, Shmaliy YS (2012) Computationally efficient FIR filtering of polynomial signals in DFT domain. Circ Syst Signal Process 31(6):2153–2166
31 Caverly RJ, Forbes JR, (2019) LMI properties and applications in systems, stability, and control theory. arXiv:1903.08599x2, 12 Jun 2019
32 Chaffee JW (1987) Relating the Allan variance to the diffusion coefficients of a linear stochastic differential equation model for precision oscillators. IEEE Trans Ultrason Ferroel Freq Contr 34(6):655–658
33 Chang G (2014) On Kalman filter for linear system with colored measurement noise. J Geodesy 88(12):1163–1170
34 Chang XH (2014) Robust Output Feedback H‐infinity Control and Filtering for Uncertain Linear Systems. Springer‐Verlag, Berlin
35 Chen YL, Chen BS (1994) Minimax robust deconvolution filters under stochastic parametric and noise uncertainties. IEEE Trans Signal Process 42(1):32–45
36 Chui CK, Chen G (1987) Kalman Filtering with Real‐Time Applications. Springer‐Verlag, Berlin
37 Costa OLV, Fragoso MD, Marques RP (2005) Discrete‐Time Markov Jump Linear Systems. Springer, London
38 Cox H (1964) On the estimation of state variables and parameters for noisy dynamic systems. IEEE Trans Autom Contr AC‐9:5–12
39 Darouach M (2009) unbiased filtering for linear descriptor systems via LMI. IEEE Trans Autom Contr 54(8):1966–1972
40 Daum F (2005) Nonlinear filters: Beyond the Kalman filter. IEEE A&E Syst Mag 20(8):57–69
41 Ding SX (2013) Model‐Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. Springer, Berlin
42 Dong Z, You Z (2006) Finite‐horizon robust Kalman filtering for uncertain discrete time‐varying systems with uncertain‐covariance white noises. IEEE Signal Process Lett 13(8):493–496
43 Doucet A, Godsill S, Andrieu C (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Statist Comput 10(3):197–208
44 Del Moral P 1996() Non linear filtering: interacting particle solution. Markov Processes Related Fields 2(4):555–580
45 Doob JL (1953) Stochastic Processes, Wiley, New York
46 Du C, Xie L (2002) Control and Filtering of Two‐Dimensional Systems. Springer, Berlin
47 Feller W (1968) An Introduction to Probability Theory and its Applications. Vol 1, 3rd Ed, Wiley, New York
48 Feller W (1970) An Introduction to Probability Theory and its Applications. Vol 2, Wiley, New York
49 Fitzgerald R (1971) Divergence of the Kalman filter. IEEE Trans Autom. Cont. 16(6):736–747
50 Fu JB, Sun J, Gao F, Lu S (2014) Maneuvering target tracking with improved unbiased FIR filter. Int Radar Conf (Radar), Lille, 13–17 October 2014
51 Fu M, de Souza CE, Xie L (1992) estimation for uncertain systems. Int J Robust Nonl Contr 2:87–105
52 Gantmacher FR (1959) Applications of the Theory of Matrices. Tr. by Brenner JI, Wiley‐Interscience, New York
53 Gauss CF (1809) Theory of Motion of the Heavenly Bodies Moving About the Sun in Conic Sections: A Translation (2004) of Theoria Motus. Mineola, Dover, New York
54 Gelb A (ed) (1974) Applied Optimal Estimation. MIT Press, Cambridge, USA
55 Geromel JC (1999) Optimal linear filtering under Parameter uncertainty. IEEE Trans Signal Process 47(1):168–175
56 Gershon E, Shaked U, Yaesh I (2005) Control and Estimaiton of State‐multiplicative Linear Systems. Springer‐Verlag, London
57 Ghaoui LE, Calafiore G (2001) Robust filtering for discrete‐time systems with bounded noise and parametric uncertainty. IEEE Trans Autom Contr 46(7):1084–1089
58 Gibbs BP (2011) Advanced Kalman Filtering, Least'Squares and Modeling: A Practical Handbook. John Wiley & Sons, Hoboken
59 Golub GH, van Loan CF (1996) Matrix Computations. 3rd Ed, John Hopkins Univ Press
60 Gonzalez JG, Arce GR (2001) Optimality of the myriad filter in practical impulsive‐noise environments. IEEE Trans Signal Process 49(2):438–441
61 Gonzalez G, Nava R, Escalante‐Ramirez B (2018) A comparative study on discrete Shmaliy moments and their texture‐based applications. Math. Probl Eng Vol 2018, art. 1673283:1–17
62 Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/non‐Gaussian Bayessian state estimation. IEE Proc 140(2):107–113
63 Grewal MS, Andrews AP (2008) Kalman Filtering: Theory and Practice using MATLAB. 3rd Ed, John Wiley & Sons, Hoboken
64 Gubner JA (2002) Probability and Random Processes for Electrical and Computer Engineering. Cambridge Univ. Press, Cambridge
65 Haddad WM, Bernstein DS (1988) Robust, reduced‐order, nonstrictly proper state estimation via the optimal projection equations with guaranteed cost bounds. IEEE Trans Autom Contr 33(6):591–595
66 Han SH, Kwon WH, Kim PS (2001) Receding‐horizon FIR filters for continuous‐time state‐space models without a priori initial state information. IEEE Trans Autom Contr 46(5):766–770
67 Han S, Kwon WH (2007) ‐ FIR smoothers for deterministic discrete‐time state‐space signal models. IEEE Trans Autom Contr 52(5):927–931
68 Han S, Kwon BK, Kwon WH (2009) Minimax FIR smoothers for deterministic continuous‐time state space models. Automatica 45(6):1561–1566
69 Hanlon P, Maybeck PS (2000) Characterization of Kalman filter residuals in the presence of mismodeling. IEEE Trans Aero Elect Syst 36(1):114–131
70 Hassibi B, Sayed AH, Kailath T (1999) Indefinite‐Quadratic Estimation and Control: A Unified Approcah to and Theories. SIAM, Philadelphia
71 Heinonen P, Neuvo Y (1988) FIR‐median hybrid filters with predictive FIR substructures. IEEE Trans Acoust Speech Signal Process 36(6):892–899
72 Hu JS, Yang CH (2011) Second‐order extended filter for nonlinear discrete‐time systems using quadratic error matrix approximation. IEEE Trans Signal Process 59:3110–3119
73 Huber PJ (1964) Robust estimation of a local parameter. Ann Math Statist 35(1):73–101
74 Huber PJ (1981) Robust Statistics. Jonh Wiley & Sons, New York
75 IEEE Standard 1139‐2008 (2009) Definitions of Physical Quantities for Fundamental Frequency and Time Metrology – Random Instabilities, IEEE, Piscataway, NJ
76 IEEE Standard 1588‐2002 for a precision clock synchronization protocol for networked measurement and control systems, IEEE, Piscataway, NJ
77 ITU‐T Recommendation G.810 (1996) Definitions and terminology for synchronization networks, Geneva, Switzerland
80 Jordan DW, Smith P (1999) Nonlinear Ordinary Differential Equations: An Introduction to Dynamical Systems. 3rd ed. Oxford Univ. Press, New York
81 Julier S, Uhlmann J, Durrant‐Whyte HF (1995) A new approach for filtering nonlinear systems, Amer Contr Conf, Seattle, Washington, 21–23 June 1995
82 Julier S, Uhlmann J, Durrant‐Whyte, HF (2000) A new method for the nonlinear transformation of means and covariances in nonlinear filters. IEEE Trans Autom Contr 45(3):477–482
83 Kailath T, Sayed AH, Hassibi B (2000) Linear Estimation. Prentice Hall, Upper Saddle River
84 Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME–J Basic Eng 82(D):35–45
85 Kalman RE, Bucy RS (1961) New results in linear filtering and prediction theory. Trans ASME–J Basic Eng 83(1):95–108
86 Kassam SA (1985) Signal Detection in Non‐Gaussian Noise. Springer‐Verlag, New York
87 Kassam SA, Poor HV (1985) Robust techniques for signal processing: a survey, Proc. IEEE 73(3):433–481
88 Kay SM (1993) Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice‐Hall, Upper Saddle River
89 Khargonekar PP, Petersen IR, Zhou K (1990) Robust stabilization of uncertain linear systems: quadratic stabilizability and control theory. IEEE Trans Autom Contr 35(3):356–361
90 Kitagawa G (1987) Non‐Gaussian state‐space modeling of nonstationary time series. J Amer Statist Ass 82(400):1032–1041
91 Knill O (2009) Probability and Stochastic Processes with Applications. Overseas Press India, New Delhi
92 Kushner H (1967) Nonlinear filtering: The exact dynamical equations satisfied by the conditional mode. IEEE Trans Autom Contr 12(3):262–267
93 Kwon BK, Han S, Kwon WH (2007) Minimum variance FIR smoothers for continuous‐time state space signal models. IEEE Signal Process Lett 14(12):1024–1027
94 Kwon BK, Han S, Kwon OK, Kwon WH (2007) Minimum variance FIR smoothers for discrete‐time state space models. IEEE Signal Process Lett 14(8):557–560
95 Kwon BK, Choi JW, Park JH, Han S, Kwon WH (2009) A Best lag size of minimum variance FIR smoothers. IEEE Signal Process Lett 16(4):307–310
96 Kwon BK, Han S (2014) An optimal fixed‐lag FIR smoother for discrete time‐varying state space models. J Inst Contr Robot Syst 20(1):1–5
97 Kwon OK, Kwon WH, Lee KS (1989) FIR filters and recursive forms for discrete‐time state‐space models. Automatica 25(5):715–728
98 Kwon OK, Ryu HS, Yoo KS (1996) Robust FIR filter for uncertain systems. 13th Trienial World Congress, San Francisco, USA, 30 June–5 July 1996
99 Kwon OK, de Souza CE, Ryu HS (1996) Robust FIR filter for discrete‐time uncertain systems. 35th Conf Dec Contr, Kobe, Japan, 11‐13 Dec 1996
100 Kwon WH, Lee KS, Kwon OK (1990) Optimal FIR filters for time‐varying state‐space models. IEEE Trans Aero Elect Syst 26(6):1011–1021
101 Kwon WH, Suh YS, Lee YI, Kwon OK (1994) Equivalence of finite memory filters, IEEE Trans Aero Elect Syst 30(3):968–972
102 Kwon WH, Lee KS, Lee JH (1994) Fast algorithms for optimal FIR filter and smoother of discrete‐time state‐space models. Automatica 30(3):489–492
103 Kwon WH, Kim PS, Park P (1999) A receding horizon Kalman FIR filter for discrete time‐invariant systems. IEEE Trans Autom Contr 44(9):1787–1791
104 Kwon WH, Kim PS, Han SH (2001) Best linear unbiased FIR filters for continuous‐time state‐space models, Asian J Control 3(1):1–9
105 Kwon WH, Kim PS, Han S (2002) A receding horizon unbiased FIR filter for discrete‐time state space models. Automatica 38(3):545–551
106 Kwon WH, Han S (2005) Receding Horizon Control: Model Predictive Control for State Models. Springer, London
108 Lastre‐Dominguez CM, Shmaliy YS, Ibarra‐Manzano O, Vazquez‐Olguin MA (2019) Denoising and features extraction of ECG signals in state space using unbiased FIR smoothing. IEEE Access 7:152166–152178
109 Lee YS, Han SH, Kwon WH (2006) FIR filters for discrete‐time state space models. Int J Contr Autom Syst 4(5):645–652
110 Lei B, Xu G, Feng M, Zou Y et al (2017) Classification, Parameter Estimation and State Estimation. John Wiley & Sons, Hoboken
111 Lewis FL, Xie L, Popa D (2008) Optimal and Robust Estimation with an Introduction to Stochastic Control Theory. CRC Press, Boca Raton
112 Li F, Shi P, Wu L (2017) Control and Filtering for Semi‐Markovian Jump Systems, Springer Int Publ, Cham, Switzerland
113 Li H, Fu M (1997) A linear matrix inequality approach to robust filtering. IEEE Trans Signal Process 45(9):2338–2350
114 Li T, Sun S, Sattar TP, Corchado JM (2000) Fight sample degeneracy and impovereshment in particle filters: A review of inteligent approaches Art Intell 128(1–2):99–141
115 Lim J (2014) Tutorial–game theory‐based extended filtering approach to nonlinear problems in signal processing. Dig Sign Process 34:1–15
116 Liu JS, Che, R (1998) Sequential Monte Carlo methods for dynamic systems. J Amer Stat Ass 93(443):1032–1044
117 Liu Q, Wang Z, He X (2019) Stochastic Control and Filtering over Constrained Communication Networks. Springer Nature, Switzerland
118 Ma L, Wang Z, Bo Y (2019) Nonlinear Control and Filtering for Stochastic Networked Systems. CRC Press, Boca Raton
119 Makhoul J (1975) Linear prediction: A tutorial review. Proc IEEE 63(4):561–580
120 Marquez‐Figueroa S, Shmaliy YS, Ibarra‐Manzano O (2020) Optimal extraction of EMG signal envelope and artifacts removal assuming colored measurement noise Biomed Sign Process Contr 57(101679:1–8
121 Martin RD, Masreliez CJ (1975) Robust estimation via stochastic approximation. IEEE Trans Inf Theory 21(3):263–271
122 Martin RD, Mintz M (1983) Robust filtering and prediction for linear systems with uncertain dynamics: a game‐theoretic approach. IEEE Trans Autom Contr AC‐28(9):888–896
123 Martin RD, Raftery AE (1987) Robustness, computation and non Euclidean models, J Am Stat Assoc 82(400):1044–1050
124 Masreliez CJ, Martin RD (1975) Robust Bayesian estimation for the linear model and robustifying the Kalman filter. IEEE Trans Autom Contr AC‐22(3):361–371
125 Maybeck PS (1979) Stochastic Models, Estimation, and Control. Academic Press, New York
126 Maybeck P (1982) Stochastic Models, Estimation and Control. Vol. 2, Academic Press, New York
127 Meditch JS (1973) A survey of data smoothing for linear and nonlinear dynamic systems. Automatica 9(2):151–162
128 Miller JH, Thomas JB (1972) Detectors for discrete‐time signals in non‐Gaussian noise. IEEE Trans Inf Theory 18(2):241–250
129 Mohamed SMK, Nahavandi S (2012) Robust finite‐horizon Kalman filtering for uncertain discrete‐time systems. IEEE Trans Autom Contr 57(6):1548–1552
130 Moore JB (1973) Discrete‐time fixed‐lag smoothing algorithms. Automatica 9(2):163–173
131 Morales‐Mendoza LJ, Gamboa‐Rosales H, Shmaliy YS (2013) A new class of discrete orthogonal polynomials for blind fitting of finite data, Signal Processing. 93(7):1785–1793
132 Moroney MJ (1951) Facts from Figures. Penguin, London
133 Nagpal K, Abedor J, Poola K (1994) An LMI approach to peak‐to‐peak gain minimization: filtering and control. Am Contr Conf, Baltimore, Maryland, 29 June–1 July 1994
134 Nikias CL, Shao M (1995) Signal Processing With Alpha‐Stable Distributions and Applications. Wiley, New York
135 Nisar MD (2011) Minimax Robustness in Signal Processing for Communications, Shaker Verlag, Aachen
136 Norgaard M, Poulsen NK, Ravn O (2000) New developments in state estimation for nonlinear systems. Automatica 36(11):1627–1638
137 Olfati‐Saber R (2005) Distributed Kalman filter using embedded consensus filters. 44th IEEE Conf Dec Contr 2005, Seville, Spain, 12‐15 Dec 2005
138 Olfati‐Saber R, Shamma JS (2005) Consensus filters for sensor networks and distributed sensor fusion. 44th Conf Dec Contr, Seville, Spain, 12‐15 Dec 2005
139 Olfati‐Saber R (2007) Distributed Kalman filtering for sensor networks. 46th IEEE Conf Dec Contr, New Orleans, LA, 12‐14 Dec 2007
140 Øksendal B (2014) Stochastic Differential Equations: An Introduction with Applications. 6th Ed, Springer, New York
141 Ortega‐Contreras J, Pale‐Ramon E, Shmaliy YS, Xu Y (2021) A novel approach to H2 FIR prediction under disturbances and measurement errors. IEEE Sign Process Lett 28:150–154
142 Packard A, Doyle J (1990) Quadratic stability with real and complex perturbations. IEEE Trans Autom Contr 35(2):198–201
143 Pak JM, Ahn CK, Shmaliy YS, Lim MT (2015) Improving reliability of particle filter‐based localization in wireless sensor networks via hybrid particle/FIR filtering. IEEE Trans Ind Inf 11(5):1089–1098
144 Palhares RM, Peres PLD (2000) Robust filtering with guaranteed energy‐to‐peak performance–an LMI approach. Automatica 36:851–858
145 Papoulis A (2002) Probability, Random Variables, and Stochastic Processes 5th Ed, McGraw‐Hill, New York
146 Pearson K (1902) On the systematic fitting of curves to observations and measurements. Biometrika 1(3):265–303
147 Petovello MG, O'Keefe K, Lachapelle G, Cannon ME (2009) Consideration of time‐correlated errors in a Kalman filter applicable to GNSS. J. of Geodesy 83(1):51–53
148 Pieczynski W (2003) Pairwise Markov chains. IEEE Trans Pattern Anal Machine Intel 25(5):634–639
149 Pomarico‐Franquiz J, Shmaliy YS (2014) Accurate self‐localization in RFID tag information grids using FIR filtering. IEEE Trans Ind Inf 10(2):1317–1326
150 Pomarico‐Franquiz J, Granados‐Cruz M, Shmaliy YS (2015) Self‐localization over RFID tag grids excess channels using extended filtering techniques. IEEE J Sel Topics Sign Process 9(2):229–238
151 Quab Z, Han S, Kwon WH (2007) Robust FIR filters for linear continuous‐time state‐space models with uncertainies. 26th Chinese Contr Conf, Zhangjiajie, Hunan, 26‐31 July 2007
152 Quab Z, Han S, Kwon WH (2007) A robust FIR filter for linear discrete‐time state‐space signal models with uncertainies IEEE Sign Process Lett 14(8):553–556
153 Ramirez‐Echeverria F, Sarr A, Shmaliy YS (2014) Optimal memory for discrete‐time FIR filters in state‐space. IEEE Trans Sign Process 62(3):557–561
154 Rauch HE, Tung F, Striebel CT (1965) Maximum likelihood estimates of linear dynamic systems. AIAA J 3(8):1445–1450
155 Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman Filter: Particle Filters for Tracking Applications. Arctech House, Norwood
156 Ross SM (1996) Stochastic Processes John Wiley & Sons, New York
157 Sage AP, Melsa JL (1971) Estimation Theory with Applications to Communications and Control. McGraw‐Hill, New York
158 Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Analyt Chem 36(8):1627–1639
159 Scherer C, Weiland S (2015) Linear Matrix Inequalities in Control. University of Stuttgart, Stuttgart
160 Schick IC, Mitter SK (1994) Robust recursive estimation in the presence of heavy‐tailed observation noise. Ann. Stat 22(2):1045–1080
161 Schweppe FC (1973) Uncertain Dynamic Systems. Prentice‐Hall, Englewood Cliffs
162 Shakibaei Asli BH, Flusser J (2017) New discrete orthogonal moments for signal analysis. Sign Process 141:57–73
163 Shen X, Deng L (1997) Game theory approach to discrete filter design. IEEE Trans Sign Process 45(4):1092–1095
164 Shen X, Deng L (2014) A new solution to induced FIR filtering problem based on two matrix inequalities. Int J Contr 87(2):404–409
166 Shmaliy YS (2006) An unbiased FIR filter for TIE model of a local clock in applications to GPS‐based timekeeping. IEEE Trans Ultrason Ferroel Freq Contr 53(5):862–870
168 Shmaliy YS, Muñoz‐Diaz J, Arceo‐Miquel L (2008) Optimal horizons for a one‐parameter family of unbiased FIR filters. Dig Sign Process 18(5):739–750
169 Shmaliy YS, Muñoz‐Diaz J, Arceo‐Miquel L (2008) Optimal horizons for a one‐parameter family of unbiased FIR filters/ Dig Sign Process 18(5):739–750
170 Shmaliy YS (2009) GPS‐based Optimal FIR Filtering of Clock Models. Nova Science Publ, New York
171 Shmaliy YS (2009) An unbiased ‐step predictive FIR filter for a class of noise‐free discrete‐time models with independently observed states. Sign Image Video Process 3(2):127–135
172 Shmaliy YS (2009) Unbiased FIR filtering of discrete time polynomial state space models. IEEE Trans Sign Process 57(4):1241–1249
173 Shmaliy YS (2010) Linear optimal FIR estimation of discrete time‐invariant state‐space models. IEEE Trans Sign Process 58(6):3086–3096
174 Shmaliy YS, Morales‐Mendoz, L (2010) FIR smoothing of discrete‐time polynomial models in state space. IEEE Trans Sign Process 58(5):2544–2555
175 Shmaliy YS (2011) An iterative Kalman‐like algorithm ignoring noise and initial conditions IEEE Trans Sign Process 59(6):2465–2473
176 Shmaliy YS, Ibarra‐Manzano O (2012) Time‐variant linear optimal finite impulse response estimator for discrete state‐space models. Int J Adapt Contr Sign Process 26(2):95–104
177 Shmaliy YS, Ibarra‐Manzano O (2012) Optimal finite impulse response estimation of linear models in receiver channels with imbedded digital signal processing units. IET Sign Process 6(4):281–287
178 Shmaliy YS (2012) Suboptimal FIR filtering of nonlinear models in additive white Gaussian noise. IEEE Trans Sign Process 60(10):5519–5527
179 Shmaliy YS, Zhao S, Ahn CK (2017) Unbiased FIR filtering: an iterative alternative to Kalman filtering ignoring noise and initial conditions. IEEE Contr Syst Mag 37(5):70–89
180 Shmaliy YS, Lehmann F, Zhao S, Ahn CK (2018) Comparing robustness of the Kalman, , and UFIR filters. IEEE Trans Sign Process 66(13):3447–3458
181 Shmaliy YS, Neuvo Y, Khan S (2018) Review of unbiased FIR filters, smoothers, and predictors for polynomial signals. Front Sign Process 2(1):1–29
182 Shmaliy YS, Zhao S, Ahn CK (2019) Optimal and unbiased filtering with colored process noise using state differencing. IEEE Sign Process Lett 26(4):548–551
183 Shmaliy YS, Zhao S, Ahn CK (2020) Kalman and UFIR state estimation with coloured measurement noise using backward Euler method. IET Sign Process 14(2):64–71
184 Sima V (1996) Algorithms for Linear Quadratic Optimization. Marcel Dekker, New York
185 Simon D (2006) Optimal State Estimaiton: Kalman, , and Nonlinear Approaches. John Wiley & Sons, Hoboken
186 Simon D, Shmaliy YS (2013) Unified forms for Kalman and finite impulse response filtering and smoothing. Automatica 49(6):1892–1899
187 Simon MK (2006) Probability Distributions Involving Gaussian Random Variables. Springer, New York
188 Skelton RE, Iwasaki T, Grigoriadis KM (1998) A Unified Algebraic Approach to Linear Control Design. Taylor & Francis, London
189 Smith SW (1999) The Scientist and Engineer's Guide to Digital Signal Processing. 2nd Ed, California Tech Publ, CA
190 de Souza CE, Shaked U, Fu M (1995) Robust filtering for continuous time varying uncertain systems with deterministic input signals. IEEE Trans Sign Process 43(3):709–719
191 Stark H, Woods JW (1994) Probability, Random Processes, and Estimation Theory for Engineers. Prentice Hall, Upper Saddle River
192 Stein SR, Filler RL (1988) Kalman filter analysis for real time applications of clocks and oscillators. 42th Ann Freq Contr Symp, Baltimore, Maryland, 1‐3 June 1988
193 Stratonovich RL (1963) Topics in the Theory of Random Noise. Vol. 1, Gordon & Breach, New York
194 Sun S, Deng Z (2004) Multi‐sensor optimal information fusion Kalman filter. Automatica 40(6):1017–1017
195 Sun J, Fu JB, Wang J (2014) Improved maneuvering target tracking method based on unbiased finite impulse response (UFIR) filter. US Patent 103 500 455 A, Jan. 8 2014
196 Tan Z, Soh YC, Xie L (1999) Envelope‐constrained FIR filter design. Circ Syst Sign Process 18(6):539–551
197 Toda M, Patel RV (1980) Bounds on estimation errors of discrete‐time filters under modeling uncertainty. IEEE Trans Autom Contr AC‐25(6):1115–1121
198 Trench WF (1961) A general class of discrete time‐invariant filters. J Soc Ind Appl Math 9(3):405–421
199 Uhlmann JK (1994) Simultaneous map building and localization for real time applications II. Eng Sc Report, Oxford University, UK
200 Van Antwerp JG, Braatz RD (2000) A tutorial on linear and bilinear matrix inequalities. J Process Contr 10:363–385
201 Van der Merwe R, Doucet A, de Freitas N, Wan E (2000) The unscented particle filter, Tech Rep CUED/F‐INFENG/TR 380, Cambridge University Eng Dept
202 Uribe‐Murcia K, Shmaliy YS, Ahn CK, Zhao S (2020) Unbiased FIR filtering for time‐stamped discretely delayed and missing data. IEEE Trans Autom Contr 65(5):2155–2162
203 Uribe‐Murcia K, Shmaliy YS, Andrade‐Lucio JA (2021) Unbiased FIR, Kalman, and game theory filtering under Bernoulli distributed random delays and packet dropouts Neurocomputing 442:89–97
204 Vazquez‐Olguin M, Shmaliy YS, Ahn CK, Ibarra‐Manzano O (2017) Blind robust estimation with missing data for smart sensors using UFIR filtering. IEEE Sensors J 17(6):1819–1819
205 Vazquez‐Olguin M, Shmaliy YS, Ibarra‐Manzano O (2017) Distributed unbiased FIR filtering with average consensus on measurements for WSNs. IEEE Trans Ind Inf 13(3):1440–1447
206 Vazquez‐Olguin M, Shmaliy YS, Ibarra‐Manzano O (2020) Distributed UFIR filtering over WSNs with consensus on estimates. IEEE Trans Ind Inf 16(3):1645–1654
207 Verdú S, Poor HV (1984) On minimax robustness: a general approach and applications. IEEE Trans Inf Theory 30(2):328–340
208 Vidyasagar M (1986) Optimal rejection of persistent bounded disturbances. IEEE Trans Autom Contr AC‐31(6):527–534
209 Vincent T, Abedor J, Nagpal K, Khardonekar PP (1996) Discrete‐time estimators with guaranteed peak‐to‐peak performance. 13th Triennial World Congress, San Franciaco, 30 June–5 July 1996
210 Wang C, Walker RA, Moody MP (2007) Single antenna attitude algorithm for nonuniform antenna gain patterns. J Spacecraft Rockets 44(1):221–229
211 Wang D, Shi P, Wang W (2013) Robust Filtering and Fault Detection of Switched Delay Systems, Springer, London
212 Watson JT, Grigoriadis KM (1997) Optimal unbiased filtering via linear matrix inequalities. Am Contr Conf, Albuquerque, New Mexico, 4–6 June 1997
213 Wilson D (1989) Convolution and Hankel operator norms for linear systems. IEEE Trans Autom Contr 34(1):94–97
214 Xie L, de Souza CE, Fu M (1991) estimation for discrete‐time linear uncertain systems. Int J Robust Nonlinear Contr 1(2):111–123
215 Xie L, Soh YC, de Souza CE (1994) Robust Kalman filtering for uncertain discrete‐time systems. IEEE Trans Autom Contr 39(6):1310–1314
216 Xu J, Xie L (2014) Control and estimation of piecewise affine systems. Elsevier, Cambridge
217 Xu Y, Shmaliy YS, Zhang YD, Shen T, Sun M (2021) INS/UWB‐based quadrotor localization under colored measurement noise. IEEE Sensors J (to be published)
218 You SH, Ahn CK, Shmaliy YS, Zhao S (2020) Fusion Kalman and weighted UFIR state estimator with improved accuracy. IEEE Trans Ind Electron 67(12):10713–10722
219 Yuan JT, Stuller JA (1994) Order‐recursive FIR smoothers. IEEE Trans Sign Process 42(5):1242–1246
220 Zaknich A (2005) Pronciples of Adaptive Filters and Self‐learning Systems. Springer‐Verlag, London
221 Zhao S, Shmaliy YS, Huang B, Liu F (2015) Minimum variance unbiased FIR filter for discrete time‐variant systems. Automatica 53(3):355–361
222 Zhao S, Shmaliy YS, Liu F (2016) Fast Kalman‐like optimal unbiased FIR filtering with applications. IEEE Trans Sign Process 64(9):2284–2297
223 Zhao S, Shmaliy YS (2016) Unified maximum likelihood form for bias constrained FIR filters. IEEE Sign Process Lett 23(12):1848–1852
224 Zhao S, Shmaliy YS (2016) Unified maximum likelihood form for bias constrained FIR filters. IEEE Sign Process Lett 23(12):1848–1852
225 Zhao S, Shmaliy YS, Shi P, Ahn CK (2017) Fusion Kalman/UFIR filter for state estimation with uncertain parameters and noise statistics. IEEE Trans Ind Electron 64(4):3075–3083
226 Zhao S, Shmaliy YS, Liu F (2017) On the iterative computation of error matrix in unbiased FIR filtering. IEEE Sign Process Lett 24(5):555–558
227 Zhao S, Shmaliy YS, Ahn CK (2018) Bias‐constrained optimal fusion filtering for decentralized WSN with correlated noise sources. IEEE Trans Sign Inf Process over Networks 4(4):727–735
228 Zhao S, Ahn CK, Shi P, Shmaliy YS, Liu F (2019) Bayesian state estimation for Markovian jump systems. IEEE Syst Man Cyber Mag 5(2):27–36
229 Zhao S, Shmaliy YS, Liu F (2021) Optimal FIR filter for discrete‐time LTV systems and fast iterative algorithm. IEEE Trans Circ Syst II, Express Briefs (to be published)
230 Zhao S, Shmaliy YS, Andrade‐Lucio JA (2021) Backward optimal FIR filtering and recursive forms for discrete LTV processes. Signal Process (to be published)
231 Zhao S, Shmaliy YS, Andrade Lucio JA, Liu F (2021) Multi‐pass optimal FIR filtering for processes with unknown initial states and temporary mismatches. IEEE Trans Ind Informat (to be published)
232 Zhou K, Doyle JC, Glover K (1996) Robust and Optimal Control. Prentice‐Hall, Upper Saddle River
233 Zhu X, Soh YC, Xie L (2002) Design and analysis of discrete‐time robust Kalman filters Automatica 38(6):1069–1077
234 Zhu C, Xia Y, Xie L, Yan L (2013) Optimal linear estimation for systems with transmission delays and packet dropouts. IET Sign Process 7(9):814–823