Dynamic systems in practice are often nonlinear and subject to complex disturbances such as sensor outliers, which invalidate the Gaussian noise assumptions underlying conventional Kalman filters. To address the limitations of conventional Kalman filters under such conditions, recent studies have explored nonlinear filters based on the maximum correntropy criterion (MCC), which exploit both second-order and higher-order moments of the innovation for enhanced robustness. However, their performance is highly sensitive to the choice of kernel scale, and existing strategies—whether fixed offline or empirically adjusted—fail to adapt to unknown disturbances of varying intensities. This paper proposes a novel unsupervised adaptive maximum correntropy unscented Kalman filter (AMCUKF), which introduces three major contributions: (1) Online Kernel Scale Adaptation—the kernel scale is modeled as a latent random variable governed by an inverse Gamma distribution, and variational Bayesian inference is applied to jointly estimate the system state and kernel scale in a recursive manner; (2) Theoretical Consistency and Closed-Form Solutions—the algorithm maintains consistency with traditional MCUKF but achieves dynamic adaptability through the conjugacy of Gaussian and inverse Gamma priors, enabling efficient closed-form updates; and (3) Validated Performance under Non-Gaussian Noise—comprehensive tests on three representative scenarios, including nonlinear numerical systems with stationary/non-stationary t-distributed and Cauchy measurement noise, a single-target tracking task, and real-world battery state-of-charge (SoC) estimation using the public LG 18650 HG2 dataset. Compared with UKF, EMCUKF and FMCUKF, the proposed AMCUKF achieves about 20–60% lower RMSE and 30–70% lower worst-case error across heavy-tailed noise simulations, while maintaining comparable computational cost and convergence within 1–3 iterations. In the real-world SoC estimation task, it further reduces the maximum estimation error from over 7% to below 1%, demonstrating strong practical robustness.

Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, existing methods obtain measurements only from partial features in the network and use it only once for image reconstruction. They ignore there are low, mid, and high-level features in the network [38] and all of them are essential for high-quality reconstruction. Moreover, using measurements only once may not be enough for extracting richer information from measurements. To address these issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all level features for achieving an efficient sensing and Measurements Reuse Block (MRB) to reuse measurements multiple times on multi-scale. Finally, we conduct a series of experiments on three benchmark datasets to show that our model can significantly outperform state-of-the-art methods. Code is available at: https://github.com/fze0012/MR-CCSNet.

To solve the problem of multi-target tracking with heavy-tailed process noise and measurement noise, a Student's t mixture δ-generalized labeled multi-Bernoulli ( δ-GLMB) filter is proposed for nonlinear systems. A third-degree Spherical-Radial rule is utilized to calculate the probability density functions of the prediction and update of target states for nonlinear multi-target models. The performance of the proposed Student's t mixture δ-GLMB filter for nonlinear systems is compared with the Sequential Monte Carlo δ-GLMB (SMC- δ-GLMB) filter through simulation experiments. Simulation results demonstrated that the proposed filter can achieve a good trade-off between efficiency and tracking accuracy.

The study addresses the sensor control problem for multi‐target tracking via delta generalised labelled multi‐Bernoulli ( ‐GLMB) filter, and proposes two novel single‐sensor control schemes. One is that the Rényi divergence is used as the objective function to measure the information gain between the predicted and posterior densities of the ‐GLMB filter, and it is superior for the overall performance of the system. Since most of the sensor control schemes, including the scheme the authors proposed, are faced the curse of computation, thus the other novel scheme is proposed. This scheme, in which the sum of the statistical distances between the predicted states of targets and sensor is used as the objective function, evades the updated step of the multi‐target filter, when computing the objective function for each admissible action. Moreover, these two sensor control schemes are applied to a distributed multi‐sensor system, in which the proposed schemes are used for each sensor node and the generalised covariance intersection method is used to compute the fused multi‐target posterior density. Finally, they adopt the sequential Monte‐Carlo method in bearing and range multi‐target tracking scenarios to illustrate the effectiveness of the proposed methods.

In practical system, the sensor biases may jump abruptly. An adaptive on-line algorithm is presented in this paper for this situation. The algorithm can detect the jump onset time and estimate the jump level base on General Likelihood Ratio (GLR) test. The Monte Carlo results show, our algorithm can adaptively estimate the bias jump level well and the estimation error will not increase remarkably as other previous registration algorithms. The bias estimation error also converges to the Cramer-Rao lower bound (CRLB) after the jumping.