Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.

In the field of modern navigation and positioning, the ground-based eLoran system, serves as a vital backup to the global navigation satellite system (GNSS), which is crucial for numerous key applications. Signal demodulation, integral to eLoran’s precision timing and information transmission, significantly affects system performance. Aiming at the pulse position modulation (PPM) characteristics of eLoran signals, this paper introduces an innovative phase spectrum smoothing demodulation (PSSD) algorithm, crafted to improve demodulation performance under complex noisy and interference-laden conditions. Following a systematic review of existing demodulation techniques in eLoran, this paper details the theoretical foundation, key steps, and significant impact of parameter selection for the PSSD algorithm. Then, the unique advantages in dealing with noise, continuous wave, and skywave interference are analyzed and verified. Through extensive experimental validation under various SNR and interference conditions, the PSSD algorithm shows significant superiority in demodulation performance compared with the traditional envelope phase detection (EPD) algorithm. The effectiveness of the PSSD algorithm in interference mitigation and its stable performance across diverse conditions confirm its potential to meet the high-precision timing requirements of eLoran systems, contributing to the advancement of modern communication systems.

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.