基于阴影特征的SAR对抗方法研究

发布时间：2018-06-18 19:48

本文选题：SAR对抗 + 卷积神经网络　；参考：《电子科技大学》2017年硕士论文

【摘要】：SAR全天候、全天时等特点使其在军事领域具有光学成像系统无法替代的优势。而随着SAR军事作用的提升,针对SAR的干扰和抗干扰技术也成为了SAR研究的重要课题。SAR欺骗式干扰通过干扰机模拟虚假的回波,在SAR图像上形成欺骗目标,严重影响SAR图像解译的可靠性。由于SAR一般为侧视成像,SAR图像中目标阴影特征明显。因此,本文针对SAR欺骗式干扰,开展了基于阴影特征的SAR对抗方法研究,具体内容及创新如下:1.阐述了SAR基本原理,SAR欺骗式干扰的实现方式以及SAR图像的阴影特征。说明了SAR欺骗式干扰本质是干扰回波和真实回波的叠加,难以实现对虚假目标阴影特征的模拟,因此可以在图像域从阴影识别的角度进行SAR抗欺骗式干扰。简要概述了识别所用到的神经网络的基本原理,对神经网络参数训练常用的反向传播算法进行了说明。2.对LeNet-5结构的卷积神经网络模型进行改进以用于SAR目标分类。将LeNet-5结构的卷积神经网络的卷积层和全连接分类层的激活函数分别改为ReLU和softmax函数,池化层采用最大采样,对网络参数调节改善SAR目标分类效果。由于SAR公开数据库中没有欺骗式干扰下的数据,本文为此研究了基于电磁仿真软件的SAR成像仿真。通过计算雷达照射下的目标表面电磁流分布,回波仿真和成像处理得到复杂目标及其在欺骗式干扰下的成像结果,为基于识别的抗欺骗式干扰研究提供了样本库。3.提出了基于图像域SAR目标分类以及抗欺骗式干扰方法。传统SAR抗欺骗式干扰通过对发射信号进行复杂调制抑制干扰回波积累成像,无法对已经受欺骗式干扰的图像进行抗干扰。本文首先将SAR图像通过卷积神经网络时完成对图像的分类,此时图像边缘信息明显,能够较好的分辨出目标类型。但是真实目标阴影特征相对较弱,难以被学习和区分。接下来结合大津法和形态学运算对分类后的图像进行多值化处理,多值化后的目标边缘信息有所损失但阴影特征得到了增强,这时将图像通过新的网络实现真假目标的判定从而判断SAR图像是否已经受到干扰并标记出欺骗目标。4.从弹射式和欺骗式两个角度提出了两种主动式阴影消除的SAR欺骗式干扰方法。弹射式方面,SAR成像中目标与其阴影方位向位置相同,在距离向阴影有所延后,阴影这一位置特点与弹射式干扰效果类似。因此本文通过对干扰机和弹射点位置的计算,将背景弹射至目标阴影处,实现了对目标阴影的消除。欺骗式干扰方面,获取雷达参数后欺骗式干扰可以实现在特定位置产生干扰目标。本文通过真实目标计算出目标阴影位置,根据阴影位置信息进行欺骗式干扰调制,产生干扰回拨,从而在真实目标的阴影处叠加背景,完成对目标阴影的消除。两种干扰方法都是通过消除真实目标的阴影特征令其与欺骗式干扰产生的虚假目标类似,使得干扰目标更具欺骗性,真假混淆达到干扰对方SAR图像解译的目的。
[Abstract]:SAR all weather, all day and so on make it have the advantage that optical imaging system can not be replaced in the military field. With the improvement of the military role of SAR, the interference and anti-jamming technology for SAR have also become an important subject of the research of SAR,.SAR deception jamming through the jammer to simulate false echoes, forming a deception target on the SAR image. The reliability of SAR image interpretation is seriously affected. Since SAR is generally side view imaging, the feature of target shadow in SAR image is obvious. Therefore, this paper studies the SAR countermeasures based on the shadow feature for the SAR deception jamming. The specific content and innovation are as follows: 1. the basic principle of SAR, the realization of the SAR deception jamming and the SAR image are described. It shows that the essence of SAR deception jamming is the superposition of the interference echo and the real echo. It is difficult to realize the simulation of the shadow feature of the false target. Therefore, the SAR anti deception jamming can be carried out from the angle of the shadow recognition in the image domain. The basic principle of the neural network used in recognition is briefly outlined, and the parameters of the neural network are trained. The common reverse propagation algorithm is practiced to show that.2.'s convolution neural network model of LeNet-5 structure is improved to be used for SAR target classification. The convolution layer of convolution neural network of LeNet-5 structure and the activation function of all connected classification layer are changed to ReLU and softmax functions respectively. The pool layer adopts the maximum sampling, and the network parameters are adjusted and modified. The effect of good SAR target classification. Because there is no deceptive interference in the SAR open database, this paper studies the SAR imaging simulation based on electromagnetic simulation software. By calculating the electromagnetic flow distribution of the target surface under the radar radiation, the echo simulation and imaging processing, the complex target and the imaging results under the deception jamming are obtained. The study of anti deception jamming based on recognition provides a sample library.3. proposed based on the image domain SAR target classification and the anti deception jamming method. The traditional SAR anti deception jamming can not interfere with the deception jamming image through the complex modulation suppression jamming echo accumulation imaging of the transmitted signal. The image is classified by the convolution neural network. At this time, the image is classified by the convolution neural network. At this time, the image edge information is obvious, and the target type can be distinguished well. But the real target shadow features are relatively weak and difficult to be learned and distinguished. Then, the images after the sub class are multivalued and multivalued after SAR and morphologic operations are combined. The target edge information is lost, but the shadow feature is enhanced, then the image is judged by the new network to determine the true and false targets, and the SAR image has been disturbed and the deception target.4. is marked out of the two active shadow elimination SAR deception jamming method from the projectile and deception. In the aspect of shooting, the target of SAR imaging is the same as the position of the shadow orientation, and the shadow is similar to the projectile jamming effect after the distance to the shadow. Therefore, this paper, by calculating the position of the jammer and ejection point, ejected the background to the shadow of the target, and realized the elimination of the shadow of the target. After obtaining radar parameters, deception jamming can achieve the interference target in a specific position. This paper calculates the target shadow position through the real target, makes the deception jamming modulation according to the shadow location information, produces the interference back and dial, and then superposes the back scene in the shadow of the real target, and completes the elimination of the shadow of the target. Two kinds of interference parties are completed. By eliminating the shadow characteristics of the real target, the method is similar to the false target produced by deception jamming, making the interference target more deceptive, and the true and false confusion can interfere with the target of the SAR image interpretation.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN974

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