Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey

summary:Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research

Fault estimation for time-varying systems with Round-Robin protocol

summary:This paper is concerned with the design problem of finite-horizon $H_\infty$ fault estimator for a class of nonlinear time-varying systems with Round-Robin protocol scheduling. The faults are assumed to occur in a random way governed by a Bernoulli distributed white sequence. The communication between the sensor nodes and fault estimators is implemented via a shared network. In order to prevent the data from collisions, a Round-Robin protocol is utilized to orchestrate the transmission of sensor nodes. By means of the stochastic analysis technique and the completing squares method, a necessary and sufficient condition is established for the existence of fault estimator ensuring that the estimation error dynamics satisfies the prescribed $H_\infty$ constraint. The time-varying parameters of fault estimator are obtained by recursively solving a set of coupled backward Riccati difference equations. A simulation example is given to demonstrate the effectiveness of the proposed design scheme of the fault estimator

Non-fragile estimation for discrete-time T-S fuzzy systems with event-triggered protocol

summary:This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on $[0,1]$, is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper

Scalable consensus filtering for uncertain systems over sensor networks with Round-Robin protocol

Alexander von Humboldt Foundation of Germany; China Postdoctoral Science Foundation. Grant Number: 2017M621242,2020T130092; Fundamental Research Funds for Provincial Undergraduate Universities of Heilongjiang Province of China. Grant Number: 2018QNL-05, KYCXTD201802; National Natural Science Foundation of China. Grant Number: 61873058, 61873148, 61933007, 62073070; Natural Science Foundation of Heilongjiang Province of China. Grant Number: F2018005; PetroChina Innovation Foundation. Grant Number: 2018D-5007-0302

Optimized state estimation for nonlinear dynamical networks subject to fading measurements and stochastic coupling strength: An event-triggered communication mechanism

summary:This paper is concerned with the design of event-based state estimation algorithm for nonlinear complex networks with fading measurements and stochastic coupling strength. The event-based communication protocol is employed to save energy and enhance the network transmission efficiency, where the changeable event-triggered threshold is adopted to adjust the data transmission frequency. The phenomenon of fading measurements is described by a series of random variables obeying certain probability distribution. The aim of the paper is to propose a new recursive event-based state estimation strategy such that, for the admissible linearization error, fading measurements and stochastic coupling strength, a minimum upper bound of estimation error covariance is given by designing the estimator gain. Furthermore, the monotonicity relationship between the trace of the upper bound of estimation error covariance and the fading probability is pointed out from the theoretical aspect. Finally, a simulation example is used to show the effectiveness of developed state estimation algorithm

Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities

Recursive Set-Membership State Estimation Over a FlexRay Network

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61873148, 61873169, 61903253 and 61933007); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany

Variance-Constrained H∞H_{\infty } finite-horizon filtering for multi-rate time-varying networked systems based on stochastic protocols

summary:In this paper, the variance-constrained $H_\infty$ finite-horizon filtering problem is investigated for a class of time-varying nonlinear system under muti-rate communication network and stochastic protocol (SP). The stochastic protocol is employed to determine which sensor obtains access to the muti-rate communication network in order to relieve communication burden. A novel mapping technology is applied to characterize the randomly switching behavior of the data transmission resulting from the utilization of the SP in muti-rate communication network. By using relaxation method, sufficient conditions are derived for the existence of the finite-horizon filter satisfying both the prescribed $H_\infty$ performance and the covariance requirement of filtering errors, and the solutions of filters satisfying the above indexes are obtained by using linear matrix inequalities. Finally, the validity and effectiveness of the proposed filter scheme are verified by numerical simulation