Guoguang Wen

This article studies component-wise information fusion for nonlinear multisensor systems under time-varying heavy-tailed noise and unknown local error cross-covariances. Shared feedback introduces dependence among local errors, while sensor disturbances affect the reliability of individual state components. First, an assumed-density variational Bayesian adaptive robust cubature Kalman filter (VB-ARCKF) is derived to provide local posterior moments and innovation statistics. Its hierarchical model combines a Student's t predicted-state prior with a Gaussian–generalized hyperbolic measurement-noise mixture. Then, the normalized innovation magnitude and temporal covariance deviation are combined with posterior variances to construct reliability-aware component weights. These weights uniquely minimize a strictly convex quadratic criterion. Under a conditional local second-moment assumption, coordinate-wise and full-matrix bounds are established without specifying the cross-covariances. A feedback covariance approximation is used for recursive feedback. Under the default contamination profile, paired closed-loop and identical-local-output comparisons yield lower position errors than posterior-variance and covariance-deviation weighting. Further simulations and public asynchronous range measurements examine covariance reporting and the dependence of fusion performance on local sensor quality.

This article is concerned with resilient state estimation for a class of complex network with the vulnerable communication channel from the sensor to estimator, where both transmitted input and output signals are susceptible to malicious attacks. To overcome these challenges, a novel set-membership filter framework integrated with an unknown input estimator is designed. This framework enables the joint estimation of states and unknown inputs and actively mitigates the adverse impact of corrupted input signals. Furthermore, it circumvents the practical difficulties of direct input measurement in applications. Through rigorous mathematical induction, sufficient conditions are derived to guarantee the state remains bounded within an ellipsoid centered at its estimation, even in the presence of malicious attacks. The input filter gain is determined to decouple the state estimation error from unknown input, while the state filter gain is optimized to minimize the ellipsoid. The proposed framework is subsequently extended to the complex networks with direct feedthrough. Finally, numerical simulations and battery experiments are conducted to validate the resilience and effectiveness of the designed set-membership estimators.

In this article, we investigate a Euclidean-distance-based distributed constrained optimal formation matching (EDCOFM) problem for open large-scale multiagent systems (OLSMASs), where the number of agents is large and variable. To address the open property of the multiagent system, we introduce the concept of a depository, which can provide additional agents or store redundant agents. When the number of agents is sufficient to achieve the formation configuration, a distributed formation matching algorithm for a large-scale multiagent system (DFMA-LSMAS) is proposed to search for the optimal location of the formation configuration within a designed constraint and the optimal matching relationship. Notably, the framework is applicable to open multiagent systems. When the number of agents is smaller than the requirement to achieve the formation configuration, and more than one agent needs to be provided from the depository, an unmatched phenomenon occurs, which results in the failure of the proposed algorithm. To address this, a disturbance-based approach is proposed to eliminate the phenomenon without impacting the optimal solution. When the number of agents is larger than the requirement to achieve the formation configuration, and some agents have to leave the system, a fair competition mechanism is proposed to selectthe remainder and their optimal matching relationship. This mechanism avoids multiple competitions in a centralized manner. Finally, several simulation results are provided to verify the proposed algorithms.

This article investigates the problem of a robust diffusion Kalman filter (RDKF) in a nonlinear system within multisensor networks. To address the challenge of model mismatch caused by unmodeled dynamics or external disturbances, a novel RDKF algorithm is proposed. Conventional DKF algorithms typically suffer from significant performance degradation when system models deviate from actual dynamics, due to their reliance on accurate model assumptions. To overcome this limitation, a model mismatch compensation mechanism is incorporated into the proposed RDKF, which actively corrects estimation failures induced by inaccurate models. Furthermore, to release the additional network communication burden introduced by the compensation mechanism, two selective neighbor communication strategies are developed, which are the round-robin-inspired (RR-inspired) periodic selection strategy and the optimal selection strategy preserving estimation quality. In addition, the theoretical analysis about the mean and mean-square stability is established, and the equivalence between local and global estimation stability is demonstrated, which guarantees the effectiveness of the designed global compensation mechanism for both global and local estimates. Finally, two simulation examples on target tracking are presented to demonstrate the performance comparison between the proposed RDKFs and the conventional DKF approaches in both linear and nonlinear systems.

This paper investigates the fully distributed observer-based dynamic double-event-triggered bipartite consensus tracking problem of fractional-order multi-agent systems (FOMASs) with input saturation under a directed graph. First, to address this complex challenge, a pull-based dynamic double-event-triggered mechanism (DDETM) with different event-triggered conditions and capable of operating independently is designed, which can effectively reduce communication costs and controller updates concurrently. Then, the low-gain feedback technique is used to solve the input saturation problem faced by FOMASs under a directed graph. Based on the estimated state information, a fully distributed control protocol with pull-based DDETM is proposed to ensure the achievement of bipartite consensus tracking for FOMASs. A noteworthy feature of this control protocol is its ability to achieve system stability without the need for global information. Correspondingly, the sufficient conditions for achieving bipartite consensus is obtained with the help of low gain feedback technology and Lyapunov stability theory. Moreover, the Zeno behavior is precluded. Finally, a simulation example is presented to illustrate the theoretical results.

This paper investigates the robust state estimation problem for non-cooperative target tracking to overcome the limitations of conventional tracking algorithms under model uncertainty and noise uncertainty. Although some existing algorithms employ the maximum correntropy criterion (MCC) to handle non-Gaussian noise, most of them generally assume precise state-space models and neglect the effect of uncertain noise parameters as well. To deal with this limitation, a robust fuzzy adaptive MCC-based unscented Kalman filter (UKF) algorithm is proposed, which enhances robustness through the simultaneous state and unknown input estimation (SSUIE), and adjusts the noise covariance via a fuzzy inference system (FIS) adaptively. First, an unknown input (UI) estimator is integrated into the MCC-based UKF to tackle the challenge of model uncertainty, thereby eliminating the reliance on a precise state-space model. Then, an FIS is employed to adjust noise covariance, which explicitly accounts for uncertain noise statistics, improving adaptability in challenging conditions. Notably, the FIS-based adjustment is a more efficient and flexible method compared to the interacting multiple model (IMM) and variational Bayesian (VB) methods, as it avoids the need for precise prior knowledge and high computational complexity. Finally, the simulation results demonstrate that our proposed algorithm can enable effective tracking of non-cooperative targets, even during orbital maneuvers and under uncertain noise conditions. This work provides an adaptive solution for enhancing estimation robustness against uncertain target behaviors and dynamic environments, with potential applications in autonomous systems and security defense systems.