Recursive filtering for nonlinear systems, one of the core technologies of modern industrial systems, is an ever-increasing research topic from the control and computer communities. Some challenges from communication scheduling, limited bandwidth as well as security vulnerability have to be seriously handled though the applications of communication technologies bring into some conveniences. As such, it is of utmost significance in theory and great importance in applications to establish engineering-feasible recursive filtering algorithms for networked nonlinear systems. This paper focuses on the development of this topic and provides an up-to-date survey of the existing nonlinear filtering techniques. The introduction of three classes of communication protocols is first presented in great detail, and then comprehensive reviews and summaries of the nonlinear recursive filtering problems with Gaussian/non-Gaussian noises are elaborated according to different strategies responding to nonlinear functions or noises. Particularly, the reviews are layout from the extended Kalman filtering, the unscented/cubature Kalman filtering, the set-membership filtering as well as the $H_{\mathrm{\infty }}$ filtering. Furthermore, several challenging issues are raised to stimulate further related theoretical research and practical applications in this field.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

In this article, the distributed filtering problem is investigated for a class of nonlinear systems. Each individual sensing node provides the state estimate by using not only its own measurements but also its neighbors' information propagated according to the communication topology. With the purpose of mitigating the effects from possible abnormal data during the signal transmission, an innovation constraint with adaptively determined threshold is imposed on the transmitted innovation during the filter process. The aim of the addressed problem is to design a distributed filtering algorithm, which is capable of confining all the estimation errors within certain ellipsoidal regions with prescribed probability and achieving the required average disturbance attenuation specification. By virtue of a convex optimization method, sufficient conditions are derived for the existence of the requested filtering algorithm, and the desired filtering parameters are then obtained by iteratively solving the corresponding matrix inequalities. Within the established framework, two optimization problems are put forward to seek locally optimal filtering parameters. Finally, an illustrative numerical example is provided to demonstrate the applicability of the proposed filtering paradigm.

In this paper, we study the memory event-triggering $L_2$- $L_{\mathrm{\infty }}$ state estimation for a type of continuous stochastic neural networks (NNs) subject to time-varying delays. The information of some recent released packets is made use in the proposed triggering conditions to schedule the data propagation, thereby reducing communication frequency and saving energy. By taking into account network-induced complexities (i.e. transmission delays and random disturbances), we first formulate the evolutions of estimation error in an augmented form, and then propose the conditions under with the design goals could be met. By using certain novel Lyapunov–Krasovskii (L–K) functionals in combination with stochastic analysis technique, sufficient conditions have been provided for the existence of desired estimator, guaranteeing both the globally asymptotically mean-square stability and the prescribed $L_2$- $L_{\mathrm{\infty }}$ performance simultaneously. Moreover, the estimator gains are obtained by virtue of certain convex optimisation algorithms. Finally, we use an illustrative example to verify the obtained theoretical algorithm.

In this paper, an H _∞ estimation approach is given for an array of coupled stochastic complex networks with intermittent nonlinearity switching. A set of binary random variables are adopted to characterize the intermittent switching behavior of the involved nonlinearities. To effectively alleviate data collisions and save energy, the Round-Robin protocol is utilized to curb network congestions in data communication. For the coupled stochastic complex networks, we design a protocol-based H _∞ estimator that not only resists stochastic disturbances, but also ensures the exponential mean square stability of the desired error system under a given disturbance attenuation level. With the help of the Lyapunov stability and convex optimization theories, sufficient conditions are provided for the expected estimator. Simulations are provided to illustrate the reasonability of our H _∞ approach.

This paper is concerned with the consensus tracking problem for a class of nonlinear discrete-time multi-agent systems (MASs). The dynamic linearization method is used to approximate the nonlinear dynamics of the addressed MASs, resulting in an equivalent linear time-varying data model. With the purpose of mitigating the effects from limited communication bandwidth, a uniform-quantization-based encoding-decoding mechanism is exploited. A model-free adaptive distributed control protocol is put forward to deal with the tracking problem, which is totally data-driven without any requirement of model information except for I/O data. Finally, two illustrative simulation examples are utilized to demonstrate the effectiveness of the proposed control scheme.