For manoeuvring surface target tracking in the presence of glint noise, Huber-based Kalman filters have been widely regarded as effective. However, when the proportion of outlier measurements is high, their numerical stability and estimation accuracy can deteriorate significantly. To address this issue, we propose a Robust Cubature Kalman Filter with the Current Statistical (RCKF_CS) model. Inspired by the Huber equivalent weight function, an adaptive factor incorporating a penalty strategy based on a smoothing approximation function is introduced to suppress the adverse effects of glint noise. The proposed method is then integrated into the Cubature Kalman Filter framework combined with the Current Statistical model. Unlike conventional Huber-based approaches, which process measurement residuals independently in each dimension, the proposed method evaluates the residuals jointly to improve robustness. Numerical stability analysis and extensive simulation experiments confirm that the proposed RCKF_CS achieves improved numerical robustness and filtering performance, even under strong glint noise conditions. Compared with existing Huber-based filters, the proposed method enhances filtering performance by 2.66% to 10.18% in manoeuvring surface target tracking tasks affected by glint noise.

In the dredging industry, the automation and accuracy of the Dredging Perception System (DPS) are vital for operational efficiency and environmental safety. Current DPS implementations face challenges with sensor fault tolerance, leading to system unreliability and increased false alarm rates that can disrupt dredging operations. We propose a Hybrid Redundancy Sensor Fault Tolerance (HRSFT) strategy that integrates matching physical sensors (PS) with two distinct types of virtual sensors (VS) driven by multi-sensor association and time-series prediction models. The HRSFT employs a voting-cold storage strategy to address the false alarm issues commonly associated with single virtual sensor systems. Through experimental validation, the HRSFT strategy has demonstrated its capability to provide accurate replacement information during both single and multi-sensor failure scenarios, effectively managing abnormal sensor data and enhancing the operational reliability of the DPS. The implementation of the HRSFT strategy significantly improves the accuracy and stability of the DPS, suggesting a substantial advancement in sensor fault tolerance that could be applied to similar systems in various industries, leading to safer and more reliable operations.