Guoqing Wang

The paper investigates the problem of distributed robust state estimation over wireless sensor networks for non- Gaussian systems under hybrid cyber-attacks, where both local measurements and exchanged information among neighboring nodes are subject to varying deception attacks. To overcome the limitations of the existing correntropy-based approach, an extended multi-kernel correntropy (MKC) is adopted to evaluate the similarity of different elements between stochastic vectors. Meanwhile, confronting the controversy of solving such maximum-a-posterior-like cost functions during the derivations of the posterior estimate, the half-quadratic optimization approach is employed to transform the maximization problem of non-convex functions into two convex optimization subproblems, and the final solution is obtained through an alternating iterative method. In addition, a novel inverse Kalman-type compensation mechanism is proposed within the MKC framework to compensate for the corrupted local exchanged estimations, which is then fused through the weighted average to yield the final estimation for each sensor of the distributed algorithm. Simulation results demonstrate that the proposed algorithms outperform related work in terms of estimation performance and robustness.

Global Navigation Satellite System (GNSS) receivers are highly susceptible to spoofing attacks, which significantly degrades positioning accuracy and even render the receiver inoperable. Although antenna array provides an effective spatial domain defense against spoofing, traditional uniform linear arrays are often limited by insufficient degrees of freedom. To address this critical limitation, this paper introduces a comprehensive anti-spoofing framework in GNSS receivers employing a coprime array. Our method first leverages the cyclostationarity of GNSS signals to mitigate noise prior to despreading. The cyclic correlation matrix is then vectorized to create a virtual array. A complete Toeplitz covariance matrix is reconstructed using sparse signal recovery, enabling high-precision direction estimation via the Root-MUSIC method. Signal powers are subsequently estimated from the interpolated virtual array. Finally, we construct a linearly constrained minimum variance beamformer using the reconstructed spoofing-plus-noise covariance matrix. This beamformer ensures precise spoofing nulling while preserving all authentic signals. Computer simulations have demonstrated that the proposed method not only precisely estimates the directions of source signals but also suppress the spoofing signal.

This article investigates the differentially private leader-following consensus control (DPLFCC) problem for multiagent systems (MASs) operating on two-time scales. A new co-design framework with a private preserving scheme and a consensus controller is constructed by building a unique time-scale-dependent Lyapunov function. To achieve the ultimate mean-square leader-following consensus while maintaining differential privacy, the proposed strategy establishes a new distributed consensus controller with noise control for each follower. The initial state of the follower can be made more private by adjusting the noise control gain. It should be pointed out that controller-solving criteria and privacy level performances are designed depending on the time-scale parameter, thereby eliminating the numerical stiffness caused by the two-time-scale property. Furthermore, the results are extended to the leader’s privacy-preserving situation. Finally, the effectiveness of the developed algorithm is illustrated by numerical simulation examples.

This paper investigates the state estimation problem for dual-time-scale cyber-physical systems (DTSCPSs) with asynchronous communication channels operating on slow and fast time scales, where measurement information is obtained from digital sensors with certain resolutions. Aiming to accommodate the limited communication resources and asynchronous data packet transmission for different sensors that collect measurement outputs for slow and fast states, respectively, a special asynchronous encoding-decoding protocol is proposed from sensors to estimators. It should be noticed that sensor resolution is taken into consideration in this paper, which is an essential attribute of digital sensors and characterizes the measurement and data processing accuracy. An ε-dependent Lyapunov function is established and a novel ε-dependent estimator is carried out, which guarantees the estimate error dynamics being exponentially ultimately bounded. Furthermore, ε-dependent criteria are proposed to calculate the estimator gains, which are free of numerical stiffness caused by the DTSP. A simulation example is provided to verify the effectiveness of the proposed methods.

The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily relies on the signal-to-clutter ratio (SCR) in a single observational channel, while feature space methods are overly sensitive to the number of pulse accumulations and rigidly apply outlier classifiers to define detection regions, without theoretical derivation. To address these limitations, this paper proposes a two-dimensional (2D) CFAR target detection method based on echo data from dual-polarization observational channels. First, statistical models of the amplitude distribution for horizontal–horizontal (HH) and vertical–vertical (VV) polarization sea clutter radar echoes are validated under identical observation conditions using measured data, and their correlations are analyzed. Then, the Copula function is introduced as a theoretical foundation to rigorously derive and extend the cell-averaging CFAR detector through strict mathematical formulations, transitioning from single statistics to 2D detection statistics. This leads to the proposed target detection method. Testing with measured data from publicly available datasets demonstrates that the proposed method effectively achieves adaptive false alarm control and significantly improves the detection performance compared to existing single-pulse one-dimensional CFAR detection methods.

In strong sea clutter conditions, floating target echo signals are easily overwhelmed. Conventional mode decomposition and reconstruction methods struggle to reliably identify and select the modes that actually contain the target components. This paper proposes a spectral mode reconstruction method based on an adaptive selection criterion for target frequency intervals. Target modes are identified by combining the Doppler frequency shift of the target and the statistical spectral characteristics of sea clutter. An evaluation framework centered on relative feature gain and feature detection probability is then developed to validate the effectiveness of the proposed method. Experimental results on measured data demonstrate that the reconstructed signals significantly outperform the original signals on every metric evaluated, effectively suppressing sea clutter and enhancing target components.

In this article, we propose the robust recursive Kalman filter along with its Rauch-Tung-Striebel smoother designed for systems with correlated noises at the same epoch, where the control inputs and measurements are subjected to hybrid random disturbances. Given the challenge of correlated noises coupled with unknown disturbance signals in practical applications, we utilize the correlation coefficient between noises to reconstruct the process equation for decorrelation. To tackle the potential disturbances introduced during the decorrelation process and further enhance the estimate accuracy under complicated noise scenarios, a mixture similarity measure (MSM) is proposed to suppress the unexpected random disturbances and noises. Within the MSM framework, we derive the proposed robust filtering and fixed-internal smoothing algorithms through fixed-point iterations and Jensen's inequality. Furthermore, we demonstrate the optimality and convergence of the proposed algorithms by leveraging properties of the Hessian matrix and the Banach theorem. Simulations on target tracking examples illustrate the superiority of the proposed algorithms over related methods, even in scenarios involving non-Gaussian correlated noise.