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In this article, the Tobit Kalman filtering problem is investigated for a class of discrete time‐varying fractional‐order systems in the presence of measurement censoring and stochastic nonlinearities under the Round‐Robin protocol (RRP). The fractional‐order dynamic model is described by the Grunwald–Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state‐dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol‐based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off‐line. Simulation results are provided to demonstrate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the Tobit Kalman filtering problem for a class of discrete time-varying systems subject to censored observations, integral measurements and probabilistic sensor failures under the Round-Robin protocol (RRP). The censored observations are characterized by the Tobit observation model, the integral measurements are described as functions of system states over a certain time interval required for data acquisition, and the sensor failures are governed by a set of uncorrelated random variables. The RRP is employed to decide the transmission sequence of sensors in order to alleviate undesirable data collisions. By resorting to the augmentation technique and the orthogonality projection principle, a protocol-based Tobit Kalman filter (TKF) is developed with the coexistence of integral measurements and sensor failures that lead to a couple of augmentation-induced terms. Moreover, the performance of the proposed filter is analyzed through examining the statistical property of the error covariance of the state estimation. Further analysis shows the existence of self-propagating upper and lower bounds on the estimation error covariance. A case study on ballistic roll rate estimation is presented to illustrate the efficacy of the developed filter. [ABSTRACT FROM AUTHOR]

Censored measurements arise frequently in engineering practice involving a large number of low-cost off-the-shelf sensors. On the other hand, sensor measurements often suffer from intermittent failures in data transmissions, and an effective way to improve the transmission reliability is to adopt the redundant channel transmission protocol. In this paper, the Tobit Kalman filtering problem is investigated for linear discrete time-varying systems with censored measurements, intermittent failures and time-correlated multiplicative measurement noises under the redundant channel transmission protocol. The Tobit regression model is first modified to take into account the complexities contributed by measurement noises, intermittent failures as well as the redundant channels. Then, an optimal Tobit Kalman filter is designed based on the modified Tobit regression model. In the developed algorithm for the filter design, several new terms are introduced to reflect addressed the complexities, all of which can be calculated recursively or off-line. Simulation results are provided to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of stability analysis for uncertain neutral-type neural networks with Markovian jumping parameters and interval time-varying delays. By separating the delay interval into multiple subintervals, a Lyapunov–Krasovskii methodology is established, which contains triple and quadruple integrals. The time-varying delay is considered to locate into any subintervals, which is different from existing delay-partitioning methods. Based on the proposed delay-partitioning approach, a stability criterion is derived to reduce the conservatism. Numerical examples show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Highlights • The Tobit Kalman filtering problem is studied for discrete time-varying systems. • Non-Gaussian Levy and time-correlated additive measurement noises are considered. • The measurement differencing method and Levy-Ito theorem are utilized. • A modified recursive filtering algorithm is designed where the unknown noise covariances are carefully calculated. • Simulation results are provided to illustrate the effectiveness of the proposed filter. Abstract The Tobit Kalman filter (TKF) is a powerful tool in solving the state estimation problem for linear systems with censored measurements. This paper is concerned with the Tobit Kalman filtering problem for discrete time-varying systems subject to non-Gaussian Lévy and time-correlated additive measurement noises. By referencing to the measurement differencing method, the time-correlation of the measurement noises is transformed into the cross-correlation between the equivalent measurement noise and the process noise. Then, by resorting to the Lévy-Ito theorem, the non-Gaussian Lévy measurement noises are transformed into equivalent Gaussian noises with unknown covariances. Based on the transformed Gaussian measurement noises, a modified recursive TKF is designed where the unknown noise covariances are carefully calculated. Simulation results are provided to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Compared with the traditional cooperative networks, the signed networks containing both cooperative and competitive interactions have prevailed. In light of such consideration, the problem of bipartite containment control for second-order multiagent systems (MASs) including multiple leaders over signed networks is investigated in this paper, in which time-varying delays and switching topologies are considered. Firstly, an augmented system method is explored such that the bipartite containment problem is formulated as the convergence problem of the product of an infinite number of sub-stochastic matrices. Then, by constructing a new leader–follower topology for each switching topology, the corresponding relationships between two topologies are used to simplify our model. Moreover, based on the properties of non-negative matrix and the technique of edge composition, a mild topological condition is developed for guaranteeing the bipartite containment collective behavior. Finally, numerical examples are presented for verifying the correctness of our conclusion. [ABSTRACT FROM AUTHOR]

This paper is concerned with the Tobit Kalman filtering problem for a class of discrete time-varying systems with both censored and fading measurements. The censored measurements are described by the Tobit measurement model and the fading measurements are characterized by the L th-order Rice fading channel model capable of accounting for not only the packet dropout but also the communication phenomenon. The measurement fading occurs in a random way where the fading probabilities are regulated by a set of mutually independent Gaussian random variables. By resorting to the state augmentation technique and the orthogonality projection principle, the Tobit Kalman filter (TKF) is designed in the presence of fading measurements. In the course of filter design, several state-augmentation-induced terms are introduced, all of which can be calculated recursively or off-line. A numerical example concerning the estimation of ballistic roll rates is provided to illustrate the usefulness of the proposed filter. [ABSTRACT FROM AUTHOR]