ID 原文 译文
12794 该研究成果对干扰场景下提高导航信号捕获性能提供了重要的模型支撑。 The research results provde important model support for improvng signal acquistion performance in interference scenarios.
12795 针对传统箔条干扰性能受指向、极化、雷达视角等因素影响严重的问题!提出了一种坐标轴构型的新型箔条结构,并采用电磁仿真软件计算其在不同频率、极化和入射角度情况下的散射特性。 The jamming performance of traditional chaff is seriously affected by factors such as chaff direction, polarization and radar angle. To address this problem, this paper proposes a new type of chaff with Cartesian-coordinate structure, and use the electromagnetic simulation software to calculate its scattering characteristics at different frequenci es, polarizations and incident angles.
12796 该类箔条由考虑了缩短效应的三根传统箔条相互正交连接组成三维直角坐标轴形状，连接点为三根传统箔条的中点。 This type of chaff is composed of three tradtional chaffs that orthogonally connect to each other in consderation of shortenng effect to form Cartesan-coordnate-structure. The connection point is the midpoint of three traditional chaffs.
12797 仿真实验和暗室测量结果表明：针对X波段(9. 5〜10. 5 GHz)雷达设计的这种新型箔条，当雷达波以不同线极化方式从不同角度入射时，单个坐标轴型箔条的雷达散射截面积(radar cross section，RCS)(其是干扰效能的主要量度)变化幅度不超过3 dB,在5。的角度制作误差范围内仍具有较稳定的RCS；单个坐标轴型箔条 的平均RCS比相同质量的传统箔条高约2.61 dB，但是对圆极化雷达的同极化干扰能力较弱；数量为5万且呈随机分布的坐标轴型箔条云RCS保持在ー10 dBsm以上，并且在线极化和入射角度上具有较好的稳定性，具备掩护典型隐身目标的能力。 The results of s imulation experiment and microwave anechoc chamber measurement show that: for this new chaff designed for X-band (9. 5 GHz—10. 5 GHz) radar, when the radar wave is incident from different angles with different linear polarizations, the radar cross section (RCS, the main measure of inter-ference efficiency) of a single Cartes i an-coord i nateostructure chaff does not change more than 3 dB,and is still relatively stable within 5 of angle production error; the average RCS of a single Cartesian-coordinatestructure chaff is about 2. 61 dB higher than the traditiona! chaff with the same quality, but this chaff has a weaker abllity to interfere with the cc-polarization of the circularly polarized radar ; for a Cartesiar- coordinate structure chaff cloud wi th a number of 50 000 and random distribution, its RCS is maintained above -10 dBsm, and has good stabllity in linear polarizations and incident angles, which shows that it has the ablty to cover typcal stealth targets.
12798 随着人工智能与合成孔径雷达(synthetic aperture radar, SAR)技术的发展,基于卷积神经网络(convolutional neural network, CNN)的SAR图像自动目标识别技术取得了一定的突破。 With the development of artificial intelligence and synthetc aperture radar (SAR) , some breakthroughs have been made in convolutional neural network (CNN) based SAR automatc target recognition (ATR).
12799 然而，由于飞机自身结构以及SAR成像机制的复杂性,在复杂环境大场景SAR图像中对飞机目标进行快速准确的检测依然存在挑战。 However, due to the complexty of aircraft's structures and SAR imaging mechansms, detecting aircrafts fast and accurately in complex large scene SAR images is stlll challenging.
12800 为提升算法的检测能力,本文对现有检测算法的处理流程进行了分析与总结,并提出了一种复杂环境大场景SAR图像飞机目标快速检测算法。 To improve detecting performance of algorithms, this paper summarizes the processing flows of current detection algorithms, and proposes a fast detecton algorthm for aircrafts in complex large-scene SAR mages.
12801 算法优化了整体检测流程,设计了基于灰度特征的机场区域精细化提取和基于CNN的飞机目标粗检测两大子模块,并采用了 YOLOv3网络对机场区域以及飞机目标分别进行初步的提取与检测。 The method optimizes the whole processmg scheme, designs grayscale based airport refining extraction and CNN based arcraft detecton modules as well as using YOEOv3to extract airport areas and detect arcrafts.
12802 实验结果表明,本文算法对复杂环境大场景SAR图像中的飞机目标具有高效的检测能力。 Experimental results illustrate that the proposed method could detect aircrafts efficiently in complex large-scene SAR images.
12803 随着高分辨率星载合成孔径雷达(synthetic aperture radar, SAR)系统的研制和使用，利用 SAR图像实现快速准确的舰船目标识别分类成为了海上目标侦察监视的重要手段。 With the development and application o the high resoluton synthetc aperture radar (SAR) system, the rapid and accurate recognition and classification o ship targets based on SAR images has become an important means of maritime target reconnaissance and surveillance.