Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

Abstract

This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.

Dongyan Chen 1 and Yonglong Yu 1 and Long Xu 1 and Xiaohui Liu 2,3

Academic Editor:Zidong Wang

1, Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, China
2, Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK
3, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Received 4 January 2015; Accepted 25 January 2015; 4 October 2015

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The filtering problem has been a mainstream research topic in the control theory due to its wide and important engineering applications such as signal processing, econometrics communication, guidance, navigation, and control of vehicles [1-4]. Kalman filtering, also known as linear optimal quadratic estimation, has attracted much research interests due to its good filtering performance and simple filtering structure [5, 6]. In [7], based on the minimum mean square error (MMSE) principle and the projection theory, the traditional Kalman filtering algorithm has been proposed for a class of linear discrete stochastic systems. Subsequently, the Kalman filtering problems have been widely investigated for different systems [8, 9]. For the nonlinear model, the theoretical results of the extended Kalman filter (EKF) have been proposed and applied in many practical engineering problems [10-13]. For example, in [14], the EKF algorithm has been employed to deal with the mobile robot localization problem with intermittent measurements, where the cases of missing measurements and uncertainties have been addressed. For the microelectromechanical systems, a new terminal sliding-mode control scheme has been designed in [15] by using the EKF observer.

During the processes of signal measurement, transmission, and computation, the sensor delays are frequently encountered and are inevitable especially in the networked systems [16-21]. The existence of the sensor delays would deteriorate the filtering accuracy and even influence the control system performance [22-26]. Hence, it is not a surprise that a great number of results have been reported to handle the Kalman filtering problems with the sensor delays [8, 9, 27]. To mention a few, the optimal Kalman filtering problem has been investigated in [8] for linear discrete system with sensor delays, packet dropouts, and uncertain observations. It has been shown that a unified augmentation method has been proposed in [8] by applying the projection theory and recursive projection formula, which can reduce the amount of correlated parameters. Motivated by the method in [8], the optimal Kalman filtering algorithm has been given in [9] for the systems with random sensor delays. Based on the unbiasedness and MMSE of the optimal Kalman filtering, the recursive optimal Kalman filtering approaches have been developed in [27, 28] for linear stochastic systems with random sensor delays. Compared with the methods in [27, 28], the developed approach in [9] can reduce the amount of correlated parameters when tackling the optimal filtering problem for systems with random sensor delays.

Note that a great deal of effort has been devoted to address the problems of optimal Kalman filtering with one-step sensor delay in the past years [29, 30]. Nevertheless, it should be pointed out that randomly occurring two-step sensor delays are also encountered in some networked systems [31]. Recently, the case of the noisy observation measurements with random one-step or two-step sample delays has been investigated and a novel unscented filtering algorithm has been given in [31] for a class of nonlinear discrete-time stochastic systems. On the other hand, it is necessary to deal with the multiplicative noises when designing the Kalman filtering [32-34]. The optimal nonfragile Kalman-type filtering problem has been investigated in [32] for a class of systems with multiplicative noises, finite-step autocorrelated measurement noises, and multiple packet dropouts, where the state-dependent multiplicative noises have been used to account for the stochastic uncertainties. In [33], a new nonlinear filter has been constructed to attenuate the effects from the multiplicative noises and the signal quantization. In [34], the linear minimum mean square estimator has been designed for linear discrete-time systems with state and measurement multiplicative noises and Markov jumps on the parameters. It is worth pointing out that, however, the optimal Kalman filtering problem has not been investigated for linear stochastic systems with multiplicative noises and random two-step sensor delays yet.

Motivated by the above discussions, in this paper, we aim to discuss the problem of optimal Kalman filtering for linear discrete stochastic system with multiplicative noises and random two-step sensor delays. The state-dependent multiplicative noises are considered to account for the stochastic uncertainties. The phenomena of two-step sensor delays may happen in data transmission and are described by using three Bernoulli distributed random variables with known conditional probabilities. Based on the MMSE estimation principle, the optimal Kalman filtering problem has been discussed for system with multiplicative noises and random two-step sensor delays. Firstly, we consider a general case for the original system where [figure omitted; refer to PDF] . By using the state augmentation approach and the projection theory, the optimal Kalman filtering algorithm has been given for augmented system. Then, the optimal Kalman filtering for the original system can be obtained easily. Secondly, we discuss the initial case when [figure omitted; refer to PDF] ( [figure omitted; refer to PDF] ) and give some parameters to help algorithm developments. The main contributions of this paper can be highlighted as follows: (1) the system model is more general where the multiplicative noises and randomly occurring two-step sensor delays are considered simultaneously and (2) a new Kalman filter is designed to handle the addressed complex phenomena. Finally, an illustrative example is provided to verify the feasibility and effectiveness of the proposed result.

The rest of this paper is organized as follows. In Section 2, the problem addressed is formulated and some preliminaries are briefly introduced. In Section 3, a new Kalman filtering algorithm is proposed to deal with the systems with multiplicative noises and random two-step sensor delays and the explicit form of the filter gain is given. In Section 4, an illustrative example is used to show the effectiveness of the proposed filtering method. Finally, we provide the conclusions in Section 5.

Notations . The notations used throughout the paper are standard. [figure omitted; refer to PDF] and [figure omitted; refer to PDF] denote the [figure omitted; refer to PDF] -dimensional Euclidean space and the set of all [figure omitted; refer to PDF] matrices, respectively. For a matrix [figure omitted; refer to PDF] , the [figure omitted; refer to PDF] and [figure omitted; refer to PDF] represent its transpose and inverse, respectively. [figure omitted; refer to PDF] stands for the expectation of a stochastic variable [figure omitted; refer to PDF] . [figure omitted; refer to PDF] stands for a block-diagonal matrix with matrices [figure omitted; refer to PDF] on the diagonal. [figure omitted; refer to PDF] and [figure omitted; refer to PDF] represent the identity matrix and the zero matrix with appropriate dimensions, respectively. Matrices are assumed to be compatible with algebraic operations if their dimensions are not explicitly stated.

2. Problem Formulation and Preliminaries

In this paper, we consider the following class of discrete uncertain stochastic systems with multiplicative noises and random two-step sensor delays: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the system state vector to be estimated, [figure omitted; refer to PDF] is measured output, and [figure omitted; refer to PDF] is measurement received by the sensor. [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are uncorrelated white noises with zero means and variance matrices [figure omitted; refer to PDF] and [figure omitted; refer to PDF] . [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are multiplicative noises with zero means and unity covariances and are uncorrelated with other noise signals. [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] are known real time-varying matrices with appropriate dimensions.

The random variables [figure omitted; refer to PDF] obey the Bernoulli distribution and have the following statistical properties: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] are known positive scalars. Assume that [figure omitted; refer to PDF] are mutually independent of other noise signals.

Remark 1.

As in [31], for [figure omitted; refer to PDF] , if [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] in model 3, one has [figure omitted; refer to PDF] ; that is, the sensor receives the data at the time instant [figure omitted; refer to PDF] ; if [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] , one has [figure omitted; refer to PDF] ; that is, there exists the one-step time delay; if [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] , one has [figure omitted; refer to PDF] ; that is, there exists the two-step time delays. For special cases, when [figure omitted; refer to PDF] , the sensor receives the signal on time, [figure omitted; refer to PDF] with [figure omitted; refer to PDF] . When [figure omitted; refer to PDF] , the sensor receives the signal on time or the one-step sensor delay occurs, [figure omitted; refer to PDF] ; here [figure omitted; refer to PDF] , [figure omitted; refer to PDF] or [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] ; that is, [figure omitted; refer to PDF] . In other words, these Bernoulli distributed variables satisfy [figure omitted; refer to PDF] for all [figure omitted; refer to PDF] .

Assumption 2.

The initial state [figure omitted; refer to PDF] is uncorrelated with other noise signals, and [figure omitted; refer to PDF]

Without loss of generality, for [figure omitted; refer to PDF] , we can rewrite 3 as follows: [figure omitted; refer to PDF]

By defining [figure omitted; refer to PDF] , the systems 1, 2, and 6 can be rewritten as the following compact form: [figure omitted; refer to PDF] where [figure omitted; refer to PDF]

For convenience of the subsequent developments, set [figure omitted; refer to PDF] Then, it is easy to obtain that [figure omitted; refer to PDF]

The purpose of this paper is to design the optimal Kalman filter [figure omitted; refer to PDF] for the addressed discrete uncertain stochastic systems 1-3 based on the observation sequence [figure omitted; refer to PDF] . Noting the relationship between the original system and the augmented system, we know [figure omitted; refer to PDF] .

3. Main Results

In this section, by using the projection theory, the recursion of the Kalman filtering is derived and the explicit expression of the filter gain is given.

To facilitate the subsequent developments, we introduce the following definition and lemmas.

Definition 3 (see [8]).

Let [figure omitted; refer to PDF] be the state covariance matrix. Then, one has [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are time-varying stochastic matrices.

Motivated by the excellent results in [8], we can obtain the following lemmas which would be helpful for the further calculation.

Lemma 4.

According to the definition of the [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , one has [figure omitted; refer to PDF] where [figure omitted; refer to PDF]

Proof.

By using Definition 3 and noting the expressions of [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , one has [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is defined in 15. Then, the proof of this lemma is complete.

Lemma 5.

The state covariance matrix [figure omitted; refer to PDF] of system 7 satisfies the following recursion: [figure omitted; refer to PDF] with the initial value [figure omitted; refer to PDF] .

Proof.

It follows from 7 that [figure omitted; refer to PDF] The proof of this lemma is complete.

Now, we are ready to design the optimal Kalman filter for system 7-8 based on the observation sequence [figure omitted; refer to PDF] . By employing Lemmas 4 and 5, we have the following theorem.

Theorem 6.

The optimal Kalman filtering for system 7-8 is given as follows: [figure omitted; refer to PDF] where [figure omitted; refer to PDF]

Proof.

According to the projection theory, it is easy to obtain 19. Moreover, the filter gain matrix [figure omitted; refer to PDF] is calculated by [figure omitted; refer to PDF] Taking projection on both sides of 7 onto the linear space spanned by [figure omitted; refer to PDF] , we have [figure omitted; refer to PDF] From the projection theory, we have [figure omitted; refer to PDF] . Then, 20 can be obtained directly.

Set the innovation [figure omitted; refer to PDF] Taking projection on both sides of 8 onto the linear space spanned by [figure omitted; refer to PDF] , we have [figure omitted; refer to PDF] where the one-step prediction [figure omitted; refer to PDF] of the measurement noise is calculated by [figure omitted; refer to PDF] Here, the one-step prediction gain [figure omitted; refer to PDF] of the measurement noise is defined by [figure omitted; refer to PDF] Moreover, the two-step prediction [figure omitted; refer to PDF] of the measurement noise in 34 is computed by [figure omitted; refer to PDF] where the two-step prediction gain of the measurement noise is defined by [figure omitted; refer to PDF] From the projection theory, [figure omitted; refer to PDF] , where the symbol [figure omitted; refer to PDF] denotes the orthogonality. Then, it is not difficult to see that [figure omitted; refer to PDF] . Subsequently, substituting 34 and 36 into 33 yields [figure omitted; refer to PDF] Then, it follows from 32 and 38 that 21 is true.

The innovation [figure omitted; refer to PDF] can be rewritten as follows: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the one-step prediction error. Substitute 39 with [figure omitted; refer to PDF] into 35. Noting [figure omitted; refer to PDF] the one-step prediction gain [figure omitted; refer to PDF] of the measurement noise can be calculated [figure omitted; refer to PDF] When deriving 41, we have used the fact that [figure omitted; refer to PDF] and [figure omitted; refer to PDF] . Then, we have 22. Similarly, substituting 39 with [figure omitted; refer to PDF] into 37, one has 23.

Subsequently, we are in a position to obtain the filtering error covariance matrix [figure omitted; refer to PDF] and the prediction error covariance matrix [figure omitted; refer to PDF] . Subtracting 19 from [figure omitted; refer to PDF] , the filtering error equation can be obtained: [figure omitted; refer to PDF] Then, we have [figure omitted; refer to PDF] Notice that [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] are all uncorrelated with [figure omitted; refer to PDF] , we have [figure omitted; refer to PDF] Thus, 24 is obtained.

Similarly, the one-step prediction error equation can be obtained as follows: [figure omitted; refer to PDF] According to 45, we have the following equation: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] Noting [figure omitted; refer to PDF] , we have [figure omitted; refer to PDF] Then, it is concluded that 25 holds.

Next, we aim to derive the filter gain [figure omitted; refer to PDF] . Firstly, substitute 39 into 30. Secondly, by using [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , we obtain [figure omitted; refer to PDF] where [figure omitted; refer to PDF] . When deriving 49, we have used the fact that [figure omitted; refer to PDF] is uncorrelated with [figure omitted; refer to PDF] . Setting [figure omitted; refer to PDF] we have [figure omitted; refer to PDF] . By using 7 and noting [figure omitted; refer to PDF] , the term [figure omitted; refer to PDF] can be obtained as follows: [figure omitted; refer to PDF] Substituting 51 into 49 and noting [figure omitted; refer to PDF] , we have 26.

Furthermore, it follows from [figure omitted; refer to PDF] that [figure omitted; refer to PDF] Substituting 52 into 50, we can see that 27 is true.

Finally, we will derive the term [figure omitted; refer to PDF] in 28. According to 39, we have [figure omitted; refer to PDF] where [figure omitted; refer to PDF] [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are defined in 29. When deriving 53, we have used the fact that [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] is uncorrelated with [figure omitted; refer to PDF] . Up to now, the proof of Theorem 6 is complete.

So far, we have derived the Kalman filtering for the addressed linear stochastic systems with multiplicative noises and random two-step sensor delays. In the following, let us discuss the initial time instant.

Particularly, when [figure omitted; refer to PDF] , 3 becomes [figure omitted; refer to PDF] . In the augmented system 7-8, letting [figure omitted; refer to PDF] , we have [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] is defined in 29.

Similarly, when [figure omitted; refer to PDF] , 3 becomes [figure omitted; refer to PDF] . In the augmented system 7-8, letting [figure omitted; refer to PDF] , one has [figure omitted; refer to PDF] where [figure omitted; refer to PDF] and [figure omitted; refer to PDF] is defined in 29.

Remark 7.

It is worth mentioning that when [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , the developed optimal filtering is reduced to the traditional Kalman filtering algorithm. On the other hand, when [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , the proposed filtering algorithm is the optimal Kalman filtering with one-step sensor delay.

To help understand, the calculation process of the proposed optimal Kalman filtering scheme in Theorem 6 can be summarized as follows.

Algorithm 8 (Kalman filtering with multiplicative noises and random two-step sensor delays).

Step 1 . Give the initial values [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] .

Step 2 . Compute [figure omitted; refer to PDF] in turn.

Step 3 . When [figure omitted; refer to PDF] is obtained, compute [figure omitted; refer to PDF] in turn.

Step 4 . In general, calculate [figure omitted; refer to PDF] by 20.

Step 5 . Compute [figure omitted; refer to PDF] by 23. Substituting 23 into 22, we obtain [figure omitted; refer to PDF] . Then, we can obtain [figure omitted; refer to PDF] by substituting 22 and 23 into 21.

Step 6 . Calculate [figure omitted; refer to PDF] by 13 and compute [figure omitted; refer to PDF] by 17. By substituting [figure omitted; refer to PDF] into 14, we have [figure omitted; refer to PDF] .

Step 7 . Calculate [figure omitted; refer to PDF] by substituting 13 into 25.

Step 8 . Substituting 14, 22, 23, and 25 into 28, we obtain [figure omitted; refer to PDF] .

Step 9 . Compute [figure omitted; refer to PDF] by substituting 22, 23, 25, and 28 into 27.

Step 10 . Substituting 25, 27, and 28 into 26, we obtain [figure omitted; refer to PDF] .

Step 11 . By using 19 and 24, we calculate the optimal estimation [figure omitted; refer to PDF] and obtain [figure omitted; refer to PDF] . Then, letting [figure omitted; refer to PDF] , go back to Step 4 .

Remark 9.

In this paper, we have used the state augmentation approach and innovation analysis technique to design the optimal Kalman filter contaminated with multiplicative noises and randomly occurring two-step sensor delays. Compared with the existing results, these two phenomena addressed have constituted the main differences and have been explicitly reflected in the main results, such as the terms [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] . During the implementation of the proposed filtering algorithm, it is worth mentioning that more efforts should be made to derive the terms [figure omitted; refer to PDF] and [figure omitted; refer to PDF] in 27 and 28 due to the consideration of the randomly occurring sensor delays. From the above algorithm, it is easy to see that Steps 5-10 in Algorithm 8 are important especially those involved terms.

4. An Illustrative Example

In this section, a numerical example is proposed to show the feasibility and effectiveness of the proposed main results.

Consider the following system: [figure omitted; refer to PDF] where [figure omitted; refer to PDF] is the system state and [figure omitted; refer to PDF] and [figure omitted; refer to PDF] are uncorrelated white noises with zero means and variances [figure omitted; refer to PDF] and [figure omitted; refer to PDF] , respectively.

Let [figure omitted; refer to PDF] According to Theorem 6, the optimal recursive filter [figure omitted; refer to PDF] can be obtained. The values of the filter gains are given as in Table 1. The trajectories of the actual states [figure omitted; refer to PDF] and their estimates [figure omitted; refer to PDF] are plotted in Figures 1 and 2. Let MSE [figure omitted; refer to PDF] denote the mean square error for the estimations of [figure omitted; refer to PDF] and [figure omitted; refer to PDF] ; that is, MSE [figure omitted; refer to PDF] ( [figure omitted; refer to PDF] ), where [figure omitted; refer to PDF] is the number of simulation tests. Then, the [figure omitted; refer to PDF] of the proposed filtering algorithm are plotted in Figures 3 and 4.

Table 1: Filter gains [figure omitted; refer to PDF] (Case I).

[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]
[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]	[figure omitted; refer to PDF]

Figure 1: The trajectories of [figure omitted; refer to PDF] and [figure omitted; refer to PDF] (Case I).

[figure omitted; refer to PDF]

Figure 2: The trajectories of [figure omitted; refer to PDF] and [figure omitted; refer to PDF] (Case I).

[figure omitted; refer to PDF]

Figure 3: log(MSE1) (Case I).

[figure omitted; refer to PDF]

Figure 4: log(MSE2) (Case I).

[figure omitted; refer to PDF]

In order to further discuss the effects from the randomly occurring two-step sensor delays, we make the comparison where the different probabilities of the sensor delays (i.e., Case I: [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] ; Case II: [figure omitted; refer to PDF] , [figure omitted; refer to PDF] , and [figure omitted; refer to PDF] ) are considered. The corresponding simulations are given in Figures 5-8. According to the simulations, we can see that the filtering performance is indeed influenced by the probabilities of the sensor delays. From the simulations, we can conclude that the developed filtering scheme performs well to estimate the addressed system with multiplicative noises and random two-step sensor delays. The reason is that we have made additional efforts during the algorithm design to attenuate the effects from the multiplicative noises and randomly occurring two-step sensor delays.

Figure 5: The trajectories of [figure omitted; refer to PDF] and [figure omitted; refer to PDF] (Case II).

[figure omitted; refer to PDF]

Figure 6: The trajectories of [figure omitted; refer to PDF] and [figure omitted; refer to PDF] (Case II).

[figure omitted; refer to PDF]

Figure 7: log(MSE1) (Case II).

[figure omitted; refer to PDF]

Figure 8: log(MSE2) (Case II).

[figure omitted; refer to PDF]

5. Conclusion

The problem of the optimal Kalman filtering has been investigated for a class of linear discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities have been introduced to describe the phenomena of two-step sensor delays. Based on the innovation analysis approach and the recursive projection formula, for both the multiplicative noises and the random two-step sensor delays, a new optimal Kalman filtering has been proposed for the addressed linear stochastic system. Further research topics include the extension of the developed optimal filtering strategy to the prevalent event-triggered case [35], more networked induced phenomena as in [36], and the random delays modeled by the Markov chain [37]. Moreover, it would be interesting and important to deal with the stability analysis issue for the proposed filtering algorithm.

Acknowledgments

The authors would like to thank the associate editor and the anonymous reviewers for their detailed comments and valuable suggestions. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 11271103 and 11301118.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

[1] M. Darouach, H. S. Ali, "Optimal unbiased functional filtering in the frequency domain," Systems Science & Control Engineering , vol. 2, no. 1, pp. 308-315, 2014.

[2] J. Hu, Z. Wang, H. Gao, L. K. Stergioulas, "Probability-guaranteed _H∞ finite-horizon filtering for a class of nonlinear time-varying systems with sensor saturations," Systems & Control Letters , vol. 61, no. 4, pp. 477-484, 2012.

[3] H. R. Karimi, M. Zapateiro, N. Luo, "A linear matrix inequality approach to robust fault detection filter design of linear systems with mixed time-varying delays and nonlinear perturbations," Journal of the Franklin Institute , vol. 347, no. 6, pp. 957-973, 2010.

[4] H. Dong, Z. Wang, S. X. Ding, H. Gao, "Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems," Automatica , vol. 50, no. 12, pp. 3182-3189, 2014.

[5] M. Z. A. Bhotto, I. V. Bajic, "Constant modulus blind adaptive beamforming based on unscented Kalman filtering," IEEE Signal Processing Letters , vol. 22, no. 4, pp. 474-478, 2015.

[6] H. Rezaei, M. Reza, M. H. Sedaaghi, "Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems," Information Sciences , vol. 293, pp. 263-274, 2015.

[7] R. E. Kalman, "A new approach to linear filtering and prediction problems," Journal of Fluids Engineering , vol. 82, no. 1, pp. 35-45, 1960.

[8] J. Ma, S. Sun, "Optimal linear estimators for systems with random sensor delays, multiple packet dropouts and uncertain observations," IEEE Transactions on Signal Processing , vol. 59, no. 11, pp. 5181-5192, 2011.

[9] H.-X. Chen, S.-L. Sun, "Optimal linear estimators for networked control systems with finite consecutive packet dropouts," Control Theory and Applications , vol. 29, no. 10, pp. 1317-1324, 2012.

[10] T. Utz, C. Fleck, J. Frauhammer, D. Seiler-Thull, A. Kugi, "Extended Kalman filter and adaptive backstepping for mean temperature control of a three-way catalytic converter," International Journal of Robust and Nonlinear Control , vol. 24, no. 18, pp. 3437-3453, 2013.

[11] Z. Wang, X. Liu, Y. Liu, J. Liang, V. Vinciotti, "An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series," IEEE/ACM Transactions on Computational Biology and Bioinformatics , vol. 6, no. 3, pp. 410-419, 2009.

[12] J. Hu, Z. Wang, H. Dong, H. Gao, "Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: a survey," Mathematical Problems in Engineering , vol. 2013, 2013.

[13] K. Q. Nguyen, T. H. Nguyen, Q. P. Ha, "FPGA-based sensorless PMSM speed control using reduced-order extendedKalman filters," IEEE Transactions on Industrial Electronics , vol. 61, no. 12, pp. 6574-6582, 2014.

[14] H. Ahmada, T. Namerikawa, "Extended Kalman filter-based mobile robot localization with intermittent measurements," Systems Science & Control Engineering , vol. 1, no. 1, pp. 113-126, 2013.

[15] A. Ghanbari, M. Moghanni-Bavil-Olyaei, "Adaptive fuzzy terminal sliding-mode control of MEMS z-axis gyroscopewith extended Kalman filter observer," Systems Science and Control Engineering , vol. 2, no. 1, pp. 183-191, 2014.

[16] J. Hu, Z. Wang, Y. Niu, L. K. Stergioulas, " _{H ∞} sliding mode observer design for a class of nonlinear discrete time-delay systems: a delay-fractioning approach," International Journal of Robust and Nonlinear Control , vol. 22, no. 16, pp. 1806-1826, 2012.

[17] Y. Halevi, A. Ray, "Integrated communication and control systems. Part 1-analysis," Transactions of the ASME-Journal of Dynamic Systems, Measurement and Control , vol. 110, no. 4, pp. 367-373, 1988.

[18] L. Wang, G. Wei, H. Shu, "State estimation for complex networks with randomly occurring coupling delays," Neurocomputing , vol. 122, pp. 513-520, 2013.

[19] A. Ray, Y. Halevi, "Integrated communication and control systems: part II-design considerations," Journal of Dynamic Systems, Measurement and Control , vol. 110, no. 4, pp. 374-381, 1988.

[20] J. Liang, B. Shen, H. Dong, J. Lam, "Robust distributed state estimation for sensor networks with multiple stochastic communication delays," International Journal of Systems Science , vol. 42, no. 9, pp. 1459-1471, 2011.

[21] J. Nilsson, B. Bernhardsson, B. Wittenmark, "Stochastic analysis and control of real-time systems with random time delays," Automatica , vol. 34, no. 1, pp. 57-64, 1998.

[22] Y. Luo, G. Wei, X. Ding, Y. Liu, "Controller design for 2-D stochastic nonlinear Roesser model: a probability-dependent gain-scheduling approach," Journal of the Franklin Institute. Engineering and Applied Mathematics , vol. 351, no. 11, pp. 5182-5203, 2014.

[23] J. Hu, D. Chen, J. Du, "State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays," International Journal of General Systems , vol. 43, no. 3-4, pp. 387-401, 2014.

[24] J. Liang, J. Cao, "Global output convergence of recurrent neural networks with distributed delays," Nonlinear Analysis: Real World Applications , vol. 8, no. 1, pp. 187-197, 2007.

[25] J. Liang, Z. Wang, Y. Liu, X. Liu, "State estimation for two-dimensional complex networks with randomly occurring nonlinearities and randomly varying sensor delays," International Journal of Robust and Nonlinear Control , vol. 24, no. 1, pp. 18-38, 2014.

[26] L. Wang, G. Wei, W. Li, "Probability-dependent _H∞ synchronization control for dynamical networks with randomly varying nonlinearities," Neurocomputing , vol. 133, pp. 369-376, 2014.

[27] S. L. Sun, "Optimal linear estimation for networked systems with one-step random delays and multiple packet dropouts," Acta Automatica Sinica , vol. 38, no. 3, pp. 349-356, 2012.

[28] S. Sun, L. Xie, W. Xiao, N. Xiao, "Optimal filtering for systems with multiple packet dropouts," IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 55, no. 7, pp. 695-699, 2008.

[29] J. Hu, Z. Wang, B. Shen, H. Gao, "Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays," IEEE Transactions on Signal Processing , vol. 61, no. 5, pp. 1230-1238, 2013.

[30] D. Chen, L. Xu, "Optimal Kalman filtering for a class of state delay systems with randomly multiple sensor delays," Abstract and Applied Analysis , vol. 2014, 2014.

[31] A. Hermoso-Carazo, J. Linares-Perez, "Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems," Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems , vol. 33, no. 9, pp. 3705-3717, 2009.

[32] J. Feng, Z. Wang, M. Zeng, "Optimal robust non-fragile Kalman-type recursive filtering with finite-step autocorrelated noises and multiple packet dropouts," Aerospace Science and Technology , vol. 15, no. 6, pp. 486-494, 2011.

[33] J. Hu, Z. Wang, B. Shen, H. Gao, "Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements," International Journal of Control , vol. 86, no. 4, pp. 650-663, 2013.

[34] O. L. V. Costa, G. R. A. M. Benites, "Linear minimum mean square filter for discrete-time linear systems with Markov jumps and multiplicative noises," Automatica , vol. 47, no. 3, pp. 466-476, 2011.

[35] S. Trimpe, R. D'Andrea, "Event-based state estimation with variance-based triggering," IEEE Transactions on Automatic Control , vol. 59, no. 12, pp. 3266-3281, 2014.

[36] Z. Wang, H. Dong, B. Shen, H. Gao, "Finite-horizon _H∞ filtering with missing measurements and quantization effects," IEEE Transactions on Automatic Control , vol. 58, no. 7, pp. 1707-1718, 2013.

[37] M. J. Garcia-Ligero, A. Hermoso-Carazo, J. Linares-Perez, "Distributed fusion estimation in networked systems with uncertain observations and Markovian random delays," Signal Processing , vol. 106, pp. 114-122, 2015.