Search Results (119)

Multipartite Gaussian Einstein–Podolsky–Rosen (EPR) steering is a key resource for quantum networks, but in practice it is strongly degraded by channel loss and excess noise. This motivates the need to distill multipartite Gaussian EPR steering across all relevant mode partitions. Here we propose and analyze a measurement-based noiseless linear amplification (NLA) protocol that distills Gaussian EPR steering in a four-mode square cluster state transmitted through lossy and noisy channels. Starting from a CV cluster shared by a transmitted node A and three local nodes (B, C, and D), we reconstruct the covariance matrix of the Gaussian cluster state and evaluate Gaussian steering monotones for all $(1+1)$, $(1+2)$, and $(1+3)$ bipartitions before and after applying measurement-based NLA. We show that appropriate conditioning on the noisy mode or on selected relay nodes systematically restores and enhances directional steering, extends both one-way and two-way steerable regions, and preserves the monogamy constraints characteristic of Gaussian graph states. Taken together, these results show that measurement-based NLA provides a practical route to distributing robust multipartite steering in CV cluster architectures, thereby strengthening the foundations for continuous-variable quantum information processing. Full article

The application of extended object tracking (EOT) in unmanned aerial vehicles (UAVs) has increasingly gained attention in recent years. However, EOT is often corrupted by heavy-tailed measurement noise due to outliers, which can be caused by factors such as UAV interference or partial object occlusion. Student’s t distribution (STD) is widely adopted for modeling this type of noise, and the estimation accuracy of EOT is highly dependent on prior knowledge of the noise. Although existing methods typically assume such prior knowledge is available, this assumption often fails in practice. Furthermore, the fact that the posterior of the measurement noise is estimated leads to coupling. This coupling, which cannot be adequately resolved by existing methods, prevents the direct derivation of variational Bayesian (VB) inference. We propose an adaptive EOT approach that employs a decoupling model to address unknown outliers in UAV tracking. Then, a novel dual-extended distortion model from sensor’s FoV is proposed to address the coupling. Subsequently, the measurement likelihood is formulated as a hierarchical structure, where the degrees of freedom (DoF) and measurement noise covariance matrix (MNCM) are modeled by Gamma and inverse Wishart (IW) distributions, respectively. The hierarchical structure allows the model to account for unknown noise characteristics. Based on these models, we derive an approach recursively for estimation. Finally, the performance of the proposed approach is validated with both simulated and real-world datasets. The results demonstrate the superior effectiveness and robustness of our approach. Full article

In the Arctic region, the navigation and positioning accuracy of shipborne and autonomous underwater vehicle (AUV) integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) solutions is severely degraded due to poor satellite geometry, frequent ionospheric disturbances, non-Gaussian measurement noise, and strong multipath effects, as well as long-term INS-based dead-reckoning for AUVs when GNSS is unavailable underwater. In addition, the sparse ground-based augmentation infrastructure and the lack of reliable reference trajectories and dedicated test ranges in polar waters hinder the validation and performance assessment of existing marine navigation systems, further complicating the achievement of accurate and reliable navigation in this region. To improve the positioning accuracy of the GNSS/INS shipborne navigation system, this paper adopts a tightly coupled GNSS/INS navigation approach. To further enhance the accuracy and robustness of tightly coupled GNSS/INS positioning, this paper proposes an improved Adaptive Robust Extended Kalman Filter (IAREKF) algorithm to effectively suppress the effects of gross errors and non-Gaussian noise, thereby significantly enhancing the system’s robustness and positioning accuracy. First, the residuals and Mahalanobis distance are calculated using the Adaptive Robust Extended Kalman Filter (AREKF), and the chi-square test is used to assess the anomalies of the observations. Subsequently, the observation noise covariance matrix is dynamically adjusted to improve the filter’s anti-interference capability in the complex Arctic environment. However, the state estimation accuracy of AREKF is still affected by GNSS signal degradation, leading to a decrease in navigation and positioning accuracy. To further improve the robustness and positioning accuracy of the filter, this paper introduces a sliding window mechanism, which dynamically adjusts the observation noise covariance matrix using historical residual information, thereby effectively improving the system’s stability in harsh environments. Field experiments conducted on an Arctic survey vessel demonstrate that the proposed improved adaptive robust extended Kalman filter significantly enhances the robustness and accuracy of Arctic integrated navigation. In the Arctic voyages at latitudes 80.3° and 85.7°, compared to the Loosely coupled EKF, the proposed method reduced the horizontal root mean square error by 61.78% and 21.7%, respectively. Full article

Background/Objectives: Precise identification of seizure onset zones (SOZs) and their propagation pathways is essential for effective epilepsy surgery and other interventional therapies and is typically achieved through invasive electrophysiological recordings such as intracranial electroencephalography (EEG). Previous research has demonstrated that analyzing information flow patterns, particularly in high-frequency oscillations (>80 Hz) using parametric and Wilson algorithm (WL)-based nonparametric Granger causality (GC), is valuable for SOZ identification. In this study, we analyzed scalp EEG recordings from epilepsy patients using an alternative nonparametric GC approach based on spectral density matrix factorization via the Janashia–Lagvilava algorithm (JLA). The aim of this study is to evaluate the effectiveness of JLA-based matrix factorization in nonparametric GC for noninvasively identifying seizure onset zones from ictal EEG recordings in patients with drug-resistant epilepsy. Methods: Two regions of interest (ROIs) in pairs were isolated across different time epochs in six patients referred for presurgical evaluation. To apply the nonparametric Granger causality (GC) estimation approach to the EEG recordings from these regions, the cross-power spectral density matrix was first computed using the multitaper method and subsequently factorized using the JLA. This factorization yielded the transfer function and noise covariance matrix required for GC estimation. GC values were then obtained at different prediction time steps (measured in milliseconds). These estimates were used to confirm the visually suspected seizure onset regions and their propagation pathways. Results: JLA-based spectral factorization applied within the Granger causality framework successfully identified SOZs and their propagation patterns from scalp EEG recordings, demonstrating alignment with positive surgical outcomes (Engel Class I) in all six cases. Conclusions: JLA-based spectral factorization in nonparametric Granger causality shows strong potential not only for accurate SOZ localization to support diagnosis and treatment, but also for broader applications in uncovering information flow patterns in neuroimaging and computational neuroscience. Full article

Coded aperture X-ray computed tomography (CAXCT) measures coded X-ray projections to reconstruct the inner structure of an object. Coded apertures, which determine the point spread function, can be designed to improve the reconstruction quality, but most approaches are computationally expensive, leading to very small images. In this paper, a sparse covariance matrix estimation approach is introduced to minimize the information loss sensed by projections corresponding to large tomographic images. The covariance matrix representing the map of the overlapping information of the projections is obtained by using block matrix multiplication and sparse estimation. A heuristic variant algorithm with a noise factor is presented to search the combinations of D projections leading to maximum non-overlapping information acquisition, where D is the number of unblocking elements on the coded apertures. Numerical experiments with simulated datasets show that the optimization performance of the proposed method is comparable to that of state-of-the-art methods with small images. Further, for the analyzed cases, coded aperture optimization was performed with $512 \times 512$ images by analyzing coefficients smaller than $0.02\%$ in the covariance matrix. Full article

Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation is one of the key methods for achieving precise positioning in complex urban environments. However, in some scenarios such as urban canyons, overpasses, and foliage occlusion, GNSS signals are frequently attenuated or interrupted, leading to degraded positioning accuracy when relying solely on INSs. To address this limitation, this study developed an improved GNSS/INS-integrated navigation algorithm based on a hybrid framework that combines a Robust Adaptive Kalman Filter (RAKF) with a Radial Basis Function (RBF) neural network. The RAKF allows a multi-criterion optimization strategy to be created to adaptively adjust the measurement noise covariance matrix according to GNSS data quality indicators such as PDOP, the number of satellites, and signal quality factors. This enhances the filter’s robustness and outlier detection capability under degraded GNSS conditions. Meanwhile, the RBF network is trained to predict pseudo-position increments, which substitute missing GNSS measurements during signal outages to maintain continuous navigation. Real-world vehicular experiments were conducted to evaluate the proposed RBF-aided RAKF (RBF-RAKF) against three other methods: the Extended Kalman Filter (EKF), standard RAKF, and RBF-aided Kalman Filter (RBF-KF). The experimental results demonstrate that during GNSS outages the proposed method achieved root mean square (RMS) positioning errors of 0.94, 1.02, and 0.21 m in the north, east, and down directions, respectively, representing improvements of over 90% compared with conventional filters. Moreover, the algorithm maintained meter-level horizontal accuracy and sub-meter vertical precision under severe GNSS signal degradation. These results confirm that the proposed RBF-RAKF algorithm provides stable and high-precision navigation performance in challenging urban environments. Full article

Efficient fusion of navigation sensor data with different output frequencies and data types is critical for ensuring that vehicle-mounted integrated navigation systems consistently provide stable, reliable navigation solutions in complex dynamic operational environments. To address the degradation of estimation accuracy caused by the noise characteristics mismatch of sensor measurement, an information fusion framework based on federated Kalman filter (FKF) framework is designed by incorporating an improved variational Bayesian-based adaptive Kalman filter (IVBAKF) as the core estimation module of local filters. IVBAKF mitigates the impact of uncertain measurement noise from navigation sensors through effectively estimating the measurement noise covariance matrix (MNCM) by leveraging an adaptive forgetting factor. The adjustment strategy for the forgetting factor employs a predefined mapping function derived from the squared Mahalanobis distance (SMD) of the measurement innovation, which serves as an indicator for detecting anomalies in measurement noise within the FKF, thereby enhancing the tracking capability for the MNCMs. The effectiveness of the proposed algorithm is validated through Monte Carlo simulation-based comparative experiments. The simulation results demonstrate that compared to the FKF-based baseline algorithm with nominal covariance matrices, the proposed algorithm achieves an average reduction of 43.21% in the Root Mean Square Errors (RMSEs) of the estimated navigation parameters in scenarios characterized by uncertain and time-varying measurement noise. Thus, the robustness of the proposed algorithm against complex measurement noise conditions is verified. Full article

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

Robust robot manipulation hinges on effective state estimation. The VRT 6 robot leverages an inertia measurement unit with triaxial gyroscopes, magnetometers, and accelerometers, as well as a position sensor, but these sensors are plagued by noise that demands rigorous filtering. To tackle this, an adaptively scaled unscented Kalman filter was employed. The filter’s scaling parameter was meticulously optimized using density- and moment-based techniques, as both system properties and estimated state impact this crucial parameter. A Maximum Likelihood Estimation (ML) substantiates the enhanced quality of the estimated velocity and acceleration, on par with the position estimate. Minimizing measurement prediction error (MMPE) also shows better results with less RMSE when compared to fixed-kappa values, and the quality of position estimates is higher with the increase in the domain of the scaling parameter. By carefully selecting the adaptive scaling parameters’ range to minimize sigma point weights and ensure the positive definiteness of the covariance matrix, this enhanced UKF method achieved markedly superior state estimates compared to standard UKF implementations. Full article

In underwater environments, the presence of multipath effects can cause measurement outliers in acoustic sensors, leading to reduced estimation accuracy for integrated navigation. To address this issue, this paper proposes a sliding window variational Kalman filter based on Student’s t-distribution (SWVKF-ST) to improve state estimation accuracy. First, this method makes use of Student’s t-distribution to model heavy-tailed noise and adopts the inverse Wishart distribution as the prior for noise covariance, thereby enhancing robustness against heavy-tailed distributions. On this basis, the state variables and measurements within the sliding window are jointly estimated using the variational Bayesian framework, which helps mitigate the impact of unknown noise characteristics on state estimation. In addition, this method constructs multiple fading factors to prevent the degradation of estimation accuracy caused by excessive adjustment of the predicted error covariance matrix. Finally, the simulations and actual experiment validate that the SWVKF-ST outperforms the compared filters, achieving higher filtering precision and stronger robustness to outliers. The method effectively reduces the uncertainty in the measurement noise covariance matrix and demonstrates excellent adaptability in complex underwater environments. Full article

Under large azimuth misalignment conditions, the initial alignment of strapdown inertial navigation systems (SINS) is challenged by the nonlinear characteristics of the error model. Traditional particle filter (PF) algorithms suffer from the inappropriate selection of importance density functions and severe particle degeneration, which limit their applicability in high-precision navigation. To address these limitations, this paper proposes an adaptive mixed-order spherical simplex-radial cubature particle filter (MLG-AMSSRCPF) algorithm based on matrix Lie group representation. In this approach, attitude errors are represented on the matrix Lie group SO(3), while velocity errors and inertial sensor biases are retained in Euclidean space. Efficient bidirectional conversion between Euclidean and manifold spaces is achieved through exponential and logarithmic maps, enabling accurate attitude estimation without the need for Jacobian matrices. A hybrid-order cubature transformation is introduced to reduce model linearization errors, thereby enhancing the estimation accuracy. To improve the algorithm’s adaptability in dynamic noise environments, an adaptive noise covariance update mechanism is integrated. Meanwhile, the particle similarity is evaluated using Euclidean distance, allowing the dynamic adjustment of particle numbers to balance the filtering accuracy and computational load. Furthermore, a multivariate Huber loss function is employed to adaptively adjust particle weights, effectively suppressing the influence of outliers and significantly improving the robustness of the filter. Simulation and the experimental results validate the superior performance of the proposed algorithm under moving-base alignment conditions. Compared with the conventional cubature particle filter (CPF), the heading accuracy of the MLG-AMSSRCPF algorithm was improved by 31.29% under measurement outlier interference and by 39.79% under system noise mutation scenarios. In comparison with the unscented Kalman filter (UKF), it yields improvements of 58.51% and 58.82%, respectively. These results demonstrate that the proposed method substantially enhances the filtering accuracy, robustness, and computational efficiency of SINS, confirming its practical value for initial alignment in high-noise, complex dynamic, and nonlinear navigation systems. Full article

Simultaneous Localization and Mapping (SLAM) is an effective method for estimating and correcting the pose of the mobile robot. However, a large amount of external noise and observed outliers in complex unknown environments often lead to a decrease in the estimation accuracy and robustness of the SLAM algorithm. To improve the performance of the Square Root Unscented Kalman Filter SLAM (SRUKF-SLAM), this paper proposes the Maximum Correntropy Square Root Unscented Kalman Filter SLAM (MCSRUKF-SLAM) algorithm. The method first generates an estimate of the predicted state and covariance matrix through the Unscented Transform (UT), and then obtains the square root matrix of the covariance through Cholesky and QR decomposition to replace the original covariance, effectively preserving the positive definiteness of the covariance and improving the accuracy of the algorithm. Moreover, the proposed MCSRUKF-SLAM recharacterizes measurement information at the cost of the Maximum Correntropy (MC) instead of the Minimum Mean Square Error (MMSE), effectively improving the SLAM algorithm’s ability to suppress non-Gaussian noise. The simulation results show that compared with EKF-SLAM, UKF-SLAM, SRUKF-SLAM, and MCUKF-SLAM, the accuracy of the proposed MCSRUKF-SLAM in Gaussian mixture noise improves by 81.8%, 80.9%, 78.7%, and 63.6%, and the accuracy of the proposed MCSRUKF-SLAM in colored noise improves by 50.3%, 39.9%, 38.2%, and 36.3%. Full article

Accurate and robust relative pose estimation is the first step in ensuring the success of an active debris removal mission. This paper introduces a novel method to detect structural markers on the European Space Agency’s Environmental Satellite (ENVISAT) for safe de-orbiting using image processing and Convolutional Neural Networks (CNNs). Advanced image preprocessing techniques, including noise addition and blurring, are employed to improve marker detection accuracy and robustness from a chaser spacecraft. Additionally, we address the challenges posed by eclipse periods, during which the satellite’s corners are not visible, preventing measurement updates in the Unscented Kalman Filter (UKF). To maintain estimation quality in these periods of data loss, we propose a covariance-inflating approach in which the process noise covariance matrix is adjusted, reflecting the increased uncertainty in state predictions during the eclipse. This adaptation ensures more accurate state estimation and system stability in the absence of measurements. The initial results show promising potential for autonomous removal of space debris, supporting proactive strategies for space sustainability. The effectiveness of our approach suggests that our estimation method, combined with robust noise adaptation, could significantly enhance the safety and efficiency of debris removal operations by implementing more resilient and autonomous systems in actual space missions. Full article

The topic of state estimation using multibody models combined with Kalman filters has been an active field of research for more than 15 years now. Through state estimation, virtual sensors can be used to increase the knowledge of a system, measuring variables that cannot be obtained through conventional sensors. This is useful for control purposes or updating the state of a digital twin of a system. One of the most tricky questions with the different approaches tested in the literature is the parameter tuning of the filters, in particular, the covariance matrix of the plant noise. This work presents a new method which includes a shaping filter to whiten the plant noise combined with an adaptive algorithm to adjust the plant noise parameters. This new method is tested and compared with methods already described in the literature using the three-simulation method. The new method is at least as accurate as the best hand-tuned filters in most of the situations evaluated, and improves the accuracy of previously presented adaptive methods. All the methods and mechanisms tested in this paper are available in an open source library written in matlab called MBDE. Full article