This article is concerned with the distributed fusion estimation problem for nonlinear systems without knowledge of noise statistical information. By using the Taylor expansion, the linearization errors are modeled by the state-dependent terms with uncertain parameters when constructing nonlinear estimation error systems. Then, based on the idea of bounded recursive optimization, a robust design approach is developed to obtain local nonlinear estimators and distributed fusion criterion such that the designed local/fusion estimators are stable. Notice that, by establishing different convex optimization problems, the gains of stable nonlinear local/fusion estimators can be directly obtained using the standard softwares. Finally, two illustrative examples are exploited to show the advantages and effectiveness of the proposed methods.

The coexistence of high-frequency phasor measurement units (PMUs) and conventional SCADA systems raises the challenge of heterogenous-source and multi-rate measurements which significantly degrades the performance of power system dynamic state estimation. In this work, a deep reinforcement learning (DRL) assisted robust cubature Kalman filtering (CKF) scheme is proposed to handle measurements from hybrid sources and with different time scales. In specific, a multi-rate measurement function reconstruction approach is designed with an independent discretization mechanism to lift the present limitation of requiring an integer multiple relationship of the sampling rates from multiple sources in most of existing works. Embedded with this discretization mechanism, a deep reinforcement learning assisted two-parameter linear exponential smoothing method is proposed to reconstruct the slow measurement model with online adjustable estimation parameters. A generalized correntropy loss criterion is also included in the robust CKF to counter the non-Gaussian noise and the noise distribution variation caused by the reconstruction. Comparisons results demonstrate that the proposed DRL-based robust CKF method can achieve better accuracy and robustness under various operating scenarios.

This paper aims to study the state estimation problem under the stochastic event-triggered (SET) schedule. A posterior-based SET mechanism is proposed, which determines whether to transmit data by the effect of the measurement on the posterior estimate. Since this SET mechanism considers the whole posterior probability density function, it has better information screening capability and utilization than the existing SET mechanisms that only consider the first-order moment information of measurement and prior estimate. Then, based on the proposed SET mechanism, the corresponding exact minimum mean square error estimator is derived by Bayes rule. Moreover, the prediction error covariance of the estimator is proved to be bounded under moderate conditions. Meanwhile, the upper and lower bounds on the average communication rate are also analyzed. Finally, two different systems are employed to show the effectiveness and advantages of the proposed methods.

This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multi-sensor fusion systems without noise statistical properties. In practice, the sensor-to-remote estimator channel and the smart sensor-to-fusion center channel of the communication network will be faced with some resource constraint problems. To meet the finite communication resources during the information transmission, an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified network framework to reduce communication traffic. Since the reduction of communication information will inevitably degenerate the estimation performance, two kinds of compensation strategies in terms of a unified model are proposed to restructure the untransmitted information. Then, the local/fusion estimators are designed based on the compensation information. Moreover, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then different robust recursive optimization problems are established to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also presented such that the square errors of the designed nonlinear estimators are asymptotically bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.

This paper is concerned with the distributed fusion estimation problem for nonlinear systems. Generally, optimal distributed weighted fusion criterion can be given by the form of analytic solution under the case of linear systems. However, this classical fusion criterion is not applicable to nonlinear systems because the error cross-covariances among local filters cannot be obtained. In this case, the statistical linear regression (SLR) method is firstly proposed to approximate the error cross-covariances among local Gaussian filters. To further reduce the performance loss caused by linearization errors in SLR, a measurement-dependent maximum likelihood criterion is developed to adjust the error cross-covariance matrices. Finally, a mobile robot localization example is employed to show the effectiveness and advantages of the proposed methods.