Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.     

MC-LRF based pose measurement system for shipborne aircraft automatic landing

Due to the portability and anti-interference ability, vision-based shipborne aircraft automatic landing systems have attracted the attention of researchers. In this paper, a Monocular Camera and Laser Range Finder (MC-LRF)-based pose measurement system is designed for shipborne aircraft automatic landing. First, the system represents the target ship using a set of sparse landmarks, and a two-stage model is adopted to detect landmarks on the target ship. The rough 6D pose is measured by solving a Perspective-n-Point problem. Then, once the rough pose is measured, a region-based pose refinement is used to continuously track the 6D pose in the subsequent image sequences. To address the low accuracy of monocular pose measurement in the depth direction, the designed system adopts a laser range finder to obtain an accurate range value. The measured rough pose is iteratively optimized using the accurate range measurement. Experimental results on synthetic and real images show that the system achieves robust and precise pose measurement of the target ship during automatic landing. The measurement means error is within 0.4° in rotation, and 0.2% in translation, meeting the requirements for automatic fixed-wing aircraft landing.Received 5 July 2022; revised 19 August 2022; accepted 27 September 2022.     

面向功能的测试设备驱动器设计与实现

在各种定制化测试设备中,由于很多仪器驱动不符合现有的标准,从而降低了测试软件的开发效率和质量。为此,提出了一种面向功能的测试设备驱动器的设计与实现方法。通过对特定类别测试设备所需实现功能的分析,获得了脱离硬件环境的功能项目集合,并建立了规范化的设备驱动接口。在驱动组件的内部,封装了对硬件仪器的控制,并描述了实际测试设备的功能实现。测试设备驱动器为上层软件提供了统一的开发和运行基础,便于测试程序针对这些虚拟化的功能接口进行开发,避免了硬件的差异性对业务逻辑的影响。应用实例表明,该方法提高了测试程序的移植性和开发质量,尤其适用于各种系列化的、含有非标准驱动程序的测试软件的开发。     

双基推进剂包覆层界面脱粘敲击检测系统设计

双基推进剂橡胶包覆层界面脱粘降低了推进剂工作性能，使其运行存在潜在的安全隐患。针对此问题，设计并开发了一套由信号处理组件、自动化控制组件和智能诊断组件构成的敲击检测系统，旨在通过自动化控制与智能敲击诊断技术相结合，更加全面的获取待测件的状态，提高检测的准确性和可靠性。实验结果表明，敲击检测系统可以识别橡胶包覆层脱粘缺陷，并且当敲击检测分辨率为3~10 mm时，脱粘缺陷检测准确率≥87.5%；BP神经网络隐含层神经元数设置6或7时，故障识别效果良好，K-means聚类算法对敲击检测数据故障确诊率≥90%。综上所述，敲击检测系统具有较高的检测分辨率和准确率，可以实现对双基推进剂包覆层粘接质量的客观评价。     

变角度铺丝构件内嵌缺陷精确定位算法

采用自动纤维铺放技术生产变角度铺丝构件时,在通过平移参考路径方法获得完整铺层过程中剪丝操作将导致构件中产生间隙、重叠形式的内嵌缺陷。将参考路径设计为更具通用性和设计自由度的B样条曲线。针对开边柱面铺丝构件,通过延长参考路径、丝束等距、丝带平移、丝束剪断、缺陷定位步骤,提出了不同剪断策略下的内嵌缺陷精确定位算法,该算法适用于任意覆盖参数。最后开发了相应Matlab程序验证算法的有效性,并可视化铺丝构件中内嵌缺陷的分布情况。结果表明算法可以精确确定不同覆盖参数下内嵌缺陷的位置,可为后续有限元精细模型建立提供理论依据。     

基于可重构标定板的激光与视觉联合标定方法

目前,激光雷达与视觉传感器的联合标定方法包含动态在线标定与静态离线标定两大类.动态在线标定对标定的环境有较高的要求,且标定结果不稳定;静态离线标定通常采用标定板,因而对标定板的要求较高,手动选择与线拟合都易引入或放大误差,故提出了一种基于可重构标定板的激光雷达与视觉传感器的联合标定方法.首先,对传统的标定板进行拆分与重...     

一种针对多光谱遥感图像的桥梁快速识别算法

提出了一种针对多光谱图像中桥梁的识别算法。首先,根据水体和背景地物在不同光谱波段的亮度差异,计算多光谱图像的水体指数得到水体增强图,搜索其具有明显双峰的直方图得到最优阈值,实现河流的完整提取；其次,利用桥梁的存在会导致局部水体的光谱异常,沿河流中间线进行潜在桥梁区域的快速提取；再进一步利用桥梁长度以及与河流的空间关系进行鉴别,有效剔除虚警。利用 SPOT4遥感影像进行实验,结果表明本文算法运算量小,对于多个桥梁的识别具有很好的实用性。