Zhongyao Hu

This paper investigates a privacy-preserving distributed estimation problem. A coding scheme is proposed, which randomly transmits residuals, local estimates, or encrypted local estimates, to prevent privacy leakage. Recovering the current local estimate from the residual requires the previous estimate, leading to a chained structure in the transmitted messages. The eavesdropper fails to decrypt the encrypted local estimate, and consequently, their estimation error diverges due to this chain reaction. Theoretically, we derive a sufficient condition for the stability of legitimate nodes based on the Lyapunov theory. Under moderate conditions, we prove that the eavesdropper's estimation error tends to infinity, regardless of how many nodes are eavesdropped on or what type of linear unbiased fusion scheme is employed. Additionally, we derive a set of analytical coding parameters to satisfy the above conditions. Finally, simulations are conducted to verify the effectiveness of the proposed method.

This letter is concerned with the pose estimation problem on Lie groups. In general, robots naturally move on the special Euclidean Lie groups, which provides the motivation to model the measurement uncertainty on Lie algebras and project it onto Lie groups. Then, a Lie-group-based pose estimation method under inverse covariance intersection fusion is proposed, in which the obtained estimates are more accurate than in vector spaces. Since the unknown common information shared among the measurements is taken into account, the proposed Lie-group-based pose estimation method has the advantages of higher precision and better consistency. Finally, the effectiveness of the proposed method is verified through simulations.

This paper investigates the state estimation problem under binary sensors with inaccurate thresholds. A method for extracting zonotopic data from an inaccurate threshold model is proposed. The output of this method serves as measurements to construct a centralized zonotopic Kalman Filter (ZKF). By analyzing the characteristics of binary sensors, we further propose a threshold estimation technique to encapsulate the actual threshold within a zonotope. We demonstrate that the captured zonotope becomes increasingly tighter, thereby reducing the uncertainty of the threshold estimation. Additionally, through comparisons of estimation error bounds, we extend the proposed method to the sensor arrangement problem, addressing how to select the number of sensors and their thresholds. Circuit simulations validate the effectiveness of the proposed approach.

This paper investigates the problem of eventtriggered (ET) state estimation for multi-sensor systems. An intuitive example is utilized to demonstrate that processing each component of a vector separately can enrich implicit nontriggered information. Inspired by this, a sequential stochastic ET mechanism is proposed, which processes the measurement components one after the other. Particularly, to prevent the failure of the ET mechanism, observability decomposition is performed for each single-sensor subsystem. Then, the Bayes' theorem is utilized to derive the analytic form of the minimum meansquare error estimate. Moreover, the multi-sensor system being collectively detectable is proved to be a sufficient and necessary stability condition for the proposed method. Based on the stability result, we also analyze the relationship between the ET parameter and the communication rate, and provide a design scheme for the optimal ET parameter. Finally, the effectiveness and advantages of the proposed method are verified by a target tracking system.

Covariance intersection (CI) fusion is one of the most popular methods for combining estimates when the correlations among local estimation errors are unknown. Considering practical communication constraints, CI fusion tends to be performed in hierarchical and sequential forms, i.e., hierarchical CI (HCI) fusion and sequential CI (SCI) fusion. However, existing HCI and SCI fusion are sensitive to some uncertainties, i.e., the hierarchy structure and the fusion order, which make their fusion performances unreliable. To solve this problem, this article proposes hierarchy-structure-independent HCI fusion and fusion-order-independent SCI fusion by analogy with batch CI fusion, which can avoid possible negative effects caused by the aforementioned uncertainties. Finally, two simulations verify the effectiveness and advantages of the proposed methods.

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 state estimation problem for a class of interconnected dynamic systems, where several binary sensors are deployed to observe each subsystem. The judgement of switching instant for binary sensor is based on the certain estimates and sensed variables. In this case, two innovation sequences which combine thresholds and state estimates are given to extract valid information from binary measurement. Then, a distributed recursive estimator is given on the basis of weighting fusion criteria and bounded recursive optimization. The interconnected gains of state estimator are designed by minimizing the impacts of neighboring terms, while the optimal local gains and weighting fusion matrices are designed by constructing convex optimization problems. Moreover, the asymptotic stability of squared estimation error is proved by means of sequential analysis. Finally, two illustrative examples are employed to show the effectiveness of the proposed methods.

This article is concerned with the Kalman-like filtering (KLF) problem for linear and nonlinear dynamic systems observed by binary sensors. Binary sensors are a special class of sensors that output only one bit of data and have considerable advantages in terms of energy consumption and economic costs. However, it is difficult to directly use binary sensors to estimate system states since the available information is compressed to the extreme and is difficult to be extracted. To address this problem, this article proposes an uncertainty measurement model to capture the innovations generated from binary sensors by analyzing their characteristics. Based on the proposed model, the KLFs are constructed for linear and nonlinear dynamical systems. Then, to deal with the uncertainties induced by binary sensors, conservative error covariances with adjustable parameters are derived for the KLFs via matrix inequalities and unscented transform. The optimal filter gains and some adjustable parameters are obtained by minimizing the traces and the upper bounds of the conservative covariances, respectively. Finally, arterial O ₂ system and damped mass-spring system are employed to show the effectiveness and advantages of the proposed methods.