Systems and Control Papers of the Week

This study evaluates the feasibility of workstation-level BLE localization in a classroom with 40 workstations and four beacons.

Here, using WAAM as a foundational case study of a complex cyber-physical system, we provide a systematic review of the intelligent strategies required for end-to-end geometry control.

This paper develops an observer-assisted continuous sliding-mode tracking scheme for an inspection-class ROV.

Interferometric Synthetic Aperture Radar (InSAR) provides high spatial resolution but is limited by sparse temporal sampling, whereas the Global Navigation Satellite System (GNSS) offers dense temporal sampling but with restricted spatial coverage. To address these limitations, this study introduces a spatio-temporal fusion method combining InSAR and GNSS, based on baseline deformation field–residual decoupling (BR-STKF), specifically applied to the Banji Coal Mine in the Huainan Mining Area, Anhui Province.

Industrial electric forklifts are characterized by repetitive start-stop operation, abrupt load variations associated with acceleration and lifting, regenerative braking, and limited rest periods, which pose significant challenges for accurate real-time SOC estimation. Therefore, this study presents a systematic experimental and simulation-based evaluation of SOC estimation techniques.

The ensemble Kalman filter (EnKF) is widely used for data assimilation in high-dimensional systems, but its performance often deteriorates for strongly nonlinear dynamics due to the structural mismatch between the Kalman update and the underlying system behavior. In this work, we propose a latent autoencoder ensemble Kalman filter (LAE-EnKF) that mitigates this limitation by reformulating the assimilation problem in a learned latent space with stable linear dynamics.

To this end, this paper proposes a residual-corrected variable-interval Kalman filtering (RC-VKF) method for channel load estimation.

However, conventional multi-state constraint Kalman filter (MSCKF)-based visual–inertial odometry (VIO) uses landmark-based measurements with null-space projection and often delays visual updates until feature tracks end, while point-based systems may provide insufficient geometric constraints under low texture, motion blur, and illumination variations. To overcome these limitations, we extend our preliminary conference work, POPL-VIO, into a complete state estimation framework built upon pose-only (PO) representations for both point and line features.

A quasi-global triple-collocation analysis of CYG_DA using SMOS and ASCAT SM products shows widespread skill increases (median ΔR 2 ≈ 0.04) and reductions in error variance (mean ≈ −22%) relative to CNTL, particularly across semi-arid and seasonally vegetated regions.

Since the differencing operation introduces correlation between the process noise and the equivalent measurement noise, the state transition matrix and the associated noise covariance matrices are jointly reconstructed to establish an equivalent filtering model suitable for colored-noise conditions.

A widely applied approximation algorithm uses a collapsing step to average out the most dated regime and truncates the regime path.

This study proposes a pedestrian dead reckoning (PDR)-constrained SM framework with two implementations: extended Kalman filter fusion (EKF-PDR/SM) using heading-orthogonal candidate search and anisotropic SM measurement uncertainty, and PDR-driven SM using candidate propagation, visibility scoring, and GNSS-proximity weighting.

Abstract Aiming at the challenge of balancing suppression of dynamic interference and the preservation of effective observation constraints in complex dynamic scenes for LiDAR-Inertial Odometry (LIO), a dynamic robust LIO framework based on spatiotemporal consistency perception and adaptive observation noise covariance (STAC-LIO) is proposed.

However, existing approaches often suffer from limitations in estimation accuracy, physical consistency, and cross-scenario adaptability, which hinder their practical deployment and compromise the long-term operational sustainability of battery systems. To address these challenges, this study proposes a SOC estimation framework that integrates the strengths of physics-based modeling and data-driven learning, serving as an advanced sustainability monitoring tool.

This study proposes a real-time visual monitoring technique for landslide creep based on platform attitude self-calibration.