The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter.

Functional wetting surfaces have excellent prospects in applications including self-cleaning, anti-fog, anti-icing, corrosion resistance, droplet control, and friction power generation. Laser micromachining technology is an advanced method for preparing such functional surfaces with high efficiency and quality. To fully exploit the potential of laser micromachining and the related hybrid methods, a wide spectrum of knowledge is needed. The present review systematically discusses the process capabilities and research developments of laser micromachining and its hybrid methods considering the research both in basic and practical fields. This paper outlines the relevant literature, summarizes the characteristics of functional wetting surfaces and also the basic scientific requirements for laser micromachining technology. Finally, the challenges and potential applications of superhydrophobic and superoleophobic surface are briefly discussed. This review fills the gap in the research literature by presenting an extended literature source with a wide coverage of recent developments.

Time of arrival (TOA) is a widely utilized method for positioning lightning return strokes, with its accuracy contingent upon the arrival times of signals from different detection sites. Typically, the peak value method is employed to directly extract the peak times of lightning electromagnetic pulse (LEMP) waveforms. By correlating these peak times with the coordinates of the sites, the spatiotemporal parameters of the LEMP can be determined. However, due to the dispersion phenomenon of broadband LEMP signals during propagation, the positioning accuracy of the peak method is relatively low. This paper introduces a novel lightning positioning technique that leverages continuous wavelet transform (CWT) for narrowband feature extraction. Specifically, narrowband signal characteristics were derived through CWT applied to simulation and measured data obtained from six detection sites. Subsequently, positional analysis was performed on both datasets. The results demonstrate that compared to traditional peak value methods, the proposed approach significantly enhances horizontal positioning accuracy for lightning; specifically, positioning error for simulation data decreased from 94.7 m to 5.6 m, while it reduced from 121 m to 9.2 m for practical measured data.