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。     

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。     

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.     

面向ACTD的主战坦克战法仿真方法及应用

主战坦克在论证研制阶段开展战场运用的战法研究,对于其定型和列装意义重大.提出一种基于CGF的战法仿真方法,并建立相应的战法仿真模型.最后,利用所研制的仿真系统,结合新型主战坦克的一种新战法进行了实例研究,并针对结果进行分析.该思路及仿真系统为新型主战坦克战法研究提供了简便高效的手段,可以满足面向先期概念演示验证中战法研究的需要.     

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。     

基于Hough变换的叶簇环境下SAR/GMTI动目标检测算法

叶簇环境下,当动杂波比较复杂时,常规的目标检测算法CFAR和DPCA不能有效地滤除杂波。提出了一种基于Hough变换的叶簇环境下SAR/GMTI动目标检测算法(HD),该算法在叶簇杂波比较复杂的情况下能够有效滤除动杂波,提取出动目标。首先,介绍了叶簇扰动环境下的场景建模,并且给出了建模分析和建模场景仿真;其次,给出了HD算法推导过程;最后,在建模基础上对该算法和常规算法进行了对比仿真实验。实验结果表明,该算法在较低信噪比情况下也能保持较高的检测概率。     

“以人为本”理念在军校的实践

以“以人为本”为核心的科学发展观在指导院校教育发展中有十分重要的意义。从该理念的形成与发展出发，分析其注重生命意识和情感意志、凸现个性精神与理性权力、培养主体意识和创新精神等特点，进而探索军校人文教育的特性及实践。