参照化流形空间融合学习的敏感特征提取与异常检测方法

针对遥测振动信号冲击强、响应周期短、共振频带宽和小样本等特点导致异类模式识别率低的问题,提出基于参照化流形空间融合学习的敏感特征提取与异常检测方法。采用多尺度分析方法将信号正交无遗漏地分解到各尺度带中,提取多尺度特征构造高维特征集；以相同的正常信号样本结合相同类型的异常样本建立专属参照化模型单元,采用线性流形学习获取各参照化模型单元多尺度流形特征差异,增强异常特征的敏感性。融合各参照化模型单元的投影矩阵对原始特征集进行升维再学习,获取低维多尺度敏感流形特征；输入到分类器实现对未知样本状态辨识。实测信号处理结果验证了算法的有效性。     

The prediction formula and a risk-based sensor scheduling method in target detection with guiding information

Sensor scheduling is used to improve the sensing performance in the estimation of targets' states. However, few papers are on the sensor scheduling for target detection with guiding information. This letter can remedy this deficiency. A risk-based target detection method with guiding information is provided firstly, based on which, the sensor scheduling approach is aiming at reducing the risk and uncertainty in target detection, namely risk-based sensor scheduling method. What should be stressed is that the Prediction Formula in sensor scheduling is proposed. Lastly, some examples are conducted to stress the effectiveness of this proposed method.     

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.     

卫星通信干扰信号的检测方法

卫星通信系统较易受到各种干扰，尤其是人为干扰，为了抑制干扰，需对信号进行正确的干扰检测。就几种常见的卫星干扰信号，对其进行 Matlab仿真实验，研究不同干扰类型信号的特点，提出一种改进的CME算法用于检测干扰信号是否存在。与能量检测法和传统的CME方法相比，它具有较好的检测性能。仿真结果表明，提出的CME方法可以有效用于干扰检测。     

雷达预警探测系统装备体系结构优化

优化的装备体系结构是全面提升雷达预警探测系统的体系对抗能力、夺取战场信息优势的可靠保证。在明确了武器装备体系结构优化的主要内容和流程的基础上,对雷达预警探测系统装备体系结构优化中的能力需求分析、体系结构建模、体系效能评估、优化方法等相关问题进行了探讨,对于理清装备体系结构优化的工作思路,深入研究装备体系结构优化的相关方法和技术有一定的借鉴意义。     

基于遭遇角决策的并行双模切换舵减摇控制

结合舵减摇控制中通用线性模型与模糊线性模型的优缺点,采用了一种根据遭遇角的不同自动切换控制器设计模型的并行双模切换舵减摇控制方案。为在线自动辨识遭遇角,提出基于横摇功率谱密度柱状图的神经网络辨识方法。仿真结果表明,该方法辨识精度较高,且比传统重心法节省了在线计算量与存储数据库所需的存储空间,简化了辨识流程。     

激光探测技术实用化研究——一种新型手持式微型激光探测装置

一种激光探测技术的实用化研究,通过转动衰减片调节盘和调节放大电路的增益,既可探测固体激光器发出的兆瓦级激光,又可探测半导体激光器等发出的瓦级、微瓦级激光并进行声光报警。     

大攻角飞行导弹自动驾驶仪反馈线性化设计

针对大攻角空空导弹飞行过程中复杂的非线性空气动力学特性,提出了采用反馈线性化进行控制器设计的方法.在完成导弹俯仰通道的非线性建模的基础上,采用反馈线性化理论对导弹模型进行精确线性化,然后,对线性化后的系统给出了变结构控制系统设计.最后,通过仿真验证了所设计的控制器具有良好的动态性能及较好的工程参考价值.     

一种基于G0分布的SAR图像快速CFAR检测方法

杂波统计模型是决定CFAR检测算法性能的关键因素.G0分布能对单视和多视SAR图像中均匀、一般不均匀和极不均匀区域精确建模,但存在参数估计复杂、阈值表达式难以求解的问题,限制了其实用性.针对这些问题,分别采用矩估计法和二分法来完成参数的估计和阈值的求取,并通过目标区域预筛选和迭代计算等手段进一步提高了计算效率,得到了一种兼顾检测效果和效率的快速CFAR检测方法.实验结果验证了该方法的有效性.     

SAR/GMTI动目标检测软件平台的结构设计与实现

该软件平台旨在对SAR/GMTI动目标检测算法的仿真、测试与评估,是一个运用MFC技术结合opencv库的复杂系统。针对平台中对算法、数据维护困难问题,提出一种新型的MVADE软件模型。该模型对MVC中的Model参照MVP模式采用责任分离思想进行优化,做到视图、业务逻辑、算法、数据四层的松散解耦从而降低平台的维护难度。目前初步实现了软件架构的搭建和场景仿真,丛林区域检测,DPCA等算法的嵌入,测试结果验证了该软件模型的有效性。