Rusheng Wang

This article is concerned with the distributed target tracking problem with range-only measurement, where the statistical characteristics of the system noises are unknown. First, the nonlinear measurement equation is transformed into a linear model using a type of nonlinear transformation, which can avoid the linearization errors caused by the Taylor expansion and unscented transform. The noise statistical characteristic of the converted measurement noise is still unknown, but it can be derived as bounded noise. Then, by minimizing the upper bound of estimation error, the spectral radius inequalities are constructed to determine the optimal estimator gain sequence. Meanwhile, the stability determination is derived during the estimator design. Finally, a target tracking example is presented to show the effectiveness of the proposed method.

In this paper, a moving horizon estimation (MHE)-based state estimation problem is studied for asynchronous multi-rate nonlinear systems. First, the asynchronous multi-rate system is transformed into a synchronous system at measurement sampling points through pseudo-measurement synchronization modeling. Secondly, a MHE strategy with a time-discounted quadratic objective is proposed. Under the detectability assumption, the exponential stability of the proposed MHE is established via the Lyapunov method, and the corresponding linear matrix inequality (LMI) constraints are derived. Moreover, to address the model mismatch after synchronization, a deep learning-based framework is proposed to approximate and learn the weighting parameters of the MHE. Then, barrier-function regularization is introduced to enforce the aforementioned LMI feasibility conditions, keeping the learned weights within the feasible region throughout training. Finally, the result is illustrated by a target tracking example.

The linear minimum variance (LMV) fusion rule, while widely employed in multi-sensor Kalman filtering fusion, relies typically on the implicit assumption of a nonsingular collective error covariance matrix (ECM). In practice, however, redundant information among local Kalman filters can result in a singular collective ECM. This paper aims to elucidate the fundamental causes behind the potential singularity of the collective ECM. We establish a necessary and sufficient condition for collective ECM non-singularity through reachability analysis, and demonstrate that redundancy arises when state estimates depend exclusively on model prediction. Furthermore, we derive intuitive sufficient or necessary conditions expressed directly in terms of system parameters. These results provide theoretical guarantees for LMV fusion and practical guidelines for designing multi-sensor systems.

This letter investigates the distributed fusion estimation problem for uncertain systems, where noise statistics are unavailable. A scenario optimization framework is employed to handle model uncertainties, in which sampled uncertainty realizations are transformed into linear matrix inequality (LMI) constraints. By solving the resulting convex problems, local estimator gains are obtained, ensuring bounded mean-square error. Furthermore, an explicit upper bound for the fusion error is derived, and optimal fusion weights are determined through an LMI-based criterion. Finally, target tracking systems are provided to demonstrate the advantages and effectiveness of the proposed methods. The influence of the violation and confidence parameters on estimation accuracy and computational complexity is further analyzed.

This article investigates the speed tracking estimation problem for the subway system under uncertain bounded disturbance. Firstly, the nonlinear running model of the train with dynamic descriptions is formulated, which contains traction/braking force, basic resistance, gradient, and uncertain disturbance under different conditions. By modeling the linearization error as uncertain parameters, a convex optimization problem of Kalman-like nonlinear estimator is established. Then, a robust speed tracking method is designed based on linear matrix inequalities and bounded recursive optimization idea, and the sufficient conditions to ensure the bounded stability of the nonlinear estimator are derived. Therefore, an effective solution to the stability problem of the designed nonlinear estimator with unknown disturbance statistical characteristics is given. Finally, the actual operational data of Ningbo Metro Line 7 is employed to verify the effectiveness of the proposed method.

This paper is concerned with the eavesdropping problem for distributed multi-rate fusion estimation systems, where smart sensors have different sampling rates. To prevent information leakage, differential privacy strategies are introduced into the field of multi-rate fusion systems. In this study, we define the period of steady-state fusion weights and prove that its size is unaffected by predictive compensation. To guarantee the differential privacy, two output perturbation mechanisms are proposed, where the injected noise is constructed through null space matrices and Gaussian mechanisms. Additionally, two local perturbation mechanisms are proposed to mitigate eavesdropping risks during local estimate transmission to the fusion center. Finally, a target tracking system is constructed to validate the effectiveness of the proposed privacy protection methods.