动平台雷达组网系统探测效能评估与仿真

利用不同平台雷达进行动态组网为系统效能实时评估带来了难度。为了正确实时描述系统探测效能,建立了雷达组网系统探测动态效能模型,并选取了探测范围、发现概率和探测精度等主要指标来评估系统的探测效能,同时设计开发了仿真软件,为雷达合理部署和平台运动路线优化提供决策依据。仿真结果表明平台雷达组网效能评估仿真系统具有较好的实用性。     

雷达组网反隐身的优化布站问题研究

用雷达组网抗击空中隐身目标是现代防空雷达的一个重要发展方向,根据隐身目标雷达截面积的分布特性,将雷达对隐身目标的探测范围进行了简化,建立了雷达对隐身目标探测范围的理想化模型.从雷达探测区域补盲的角度探讨了雷达组网优化布站问题,两部雷达组网时,探讨了来袭方向确定和不确定时的优化布站,进而探讨了3部雷达组网时两个副站对一个主站进行探测区域补盲的优化布站问题,通过理论推导和仿真,得出了一些有益的结论.     

基于电子侦察和雷达组网的信息协同方法研究

利用电子侦察系统和雷达组网系统的特点,将电子侦察系统纳入雷达组网系统,针对系统中的信息使用和融合问题进行研究,给出了解决方法.通过该方法可以使电子侦察系统和雷达组网系统融于一体,使系统具有更强的抗干扰能力、持续工作能力、生存能力、探测能力以及在复杂战场环境中更高的探测精度.     

基于组网雷达的探测资源调配研究

为提升组网雷达探测资源的使用效率,提出了一种基于组网雷达的探测资源调配方法;基于整数规划算法选取探测雷达,结合多参数融合手段对探测方案进行优势排序,最终形成满足探测任务需求的最优资源调配方案,能够快速而准确地生成最优雷达组合;给出最优探测方案而无须手动调配,实现了组网雷达在探测过程中系统调配过程自动优化。仿真实验表明,该方法设计有效、可行,可用于实际作战中雷达探测资源的调配。     

对空情报雷达组网技术优化研究

本报告从三方面讨论了对空情报雷达组网技术的优化问题。首先，指出了对空情报雷达组网技术优化的必要性。其次，介绍了对空情报雷达组网的基本形式和系统模型及其优化问题。最后，对优化的对空情报雷达网的关键技术进行了探讨。     

微波光子雷达组网技术

雷达组网是目前雷达领域的研究热点之一。通过对空间区域进行多角度、多频段、多域协同探测,雷达组网能够获取目标的多维度信息,并利用数据的融合处理进一步地提升系统的目标探测与识别性能。随着系统对信号质量、信息传输链路带宽以及系统同步精度等需求的日益增长,基于微波光子技术实现信号产生、传输和处理的雷达组网正逐渐获得研究者的关注。本文分析了微波光子雷达组网关键技术的工作原理,介绍了近年来国内外微波光子雷达组网的发展现状,并探讨了其未来的演进方向。     

雷达组网系统对抗有源干扰方法综述

雷达干扰和抗干扰是现代电子战的主要内容之一，是作战双方围绕雷达展开的电磁博弈。受限于有限资源，单基地雷达不足以对抗复杂电子干扰。雷达组网系统将异地部署的多部雷达连接起来对特定区域进行协同探测和信息融合，具有覆盖范围广、获取信息全、测量精度高的优势。更重要的是，雷达组网系统可以通过多视角观测和协同控制，形成对单一干扰机的体系优势。首先简要介绍了雷达组网系统的发展趋势，分析了其抗干扰能力；随后从对抗压制干扰、对抗欺骗干扰和资源管控3个方面对现有雷达组网系统对抗有源干扰方法进行了归纳总结；最后对雷达组网系统对抗有源干扰的发展趋势进行了展望。     

一种雷达组网融合实时处理系统设计与实现

雷达组网对于提高雷达系统目标探测及抗干扰能力具有重要的意义。设计并实现了一种基于ADSPTS201的组网雷达数据融合实时处理系统。主要阐述了双ADSPTS201并行处理系统的结构、算法量的估计与结果分析。系统可满足集中式组网融合和分布式组网融合的实时处理需求。该系统还具有灵活性和可扩展性,可满足组网雷达信号处理的多样性...     

雷达组网系统中评估探测精度的方法

针对雷达组网系统探测精度的考核问题,提出了一种评估雷达组网系统探测精度的方法.基于最大似然估计法、最小二乘估计法及加权最小二乘估计法,推导了衡量雷达组网系统探测精度的3种分析评估方法.通过性能对比,选择性能最优的最大似然估计评估法运用到某雷达群组网探测系统实际检飞考核试验中,获得了较好的评定结果.     

组网雷达对隐身飞机的探测概率研究

设计了隐身飞机采取S 机动通过分层部署式、环形雷达网的作战场景,得到了飞机相对每部雷达的动态雷达散射截面积;然后,采用swerlingI 型雷达检测模型,计算了单部雷达的检测概率;最后,采用秩K 判断准则,得到不同秩下,组网雷达的联合探测概率和虚警概率。当秩K =2 时的六雷达组网系统,具有较高的检测概率,较低的雷达虚警概率。该文对于雷达组网对隐身飞机的探测、隐身飞机突防具有一定的指导意义。     

一种多基地雷达检测概率的全局优化方法

提出一种多基地雷达检测概率的全局优化方法.首先讨论在多个接收站具有不同接收信噪比的条件下,对接收站判决进行融合后,目标总的检测概率和虚警概率的变化;然后以目标总的检测概率为目标函数,对各个接收站的恒虚警率检测门限进行调整,实现了多基地雷达检测概率的优化.     

基于并行DSP的高分辨目标检测系统设计

研究了基于位置相关的高分辨检测方法的改进和基于并行ADSP_TS101芯片的高性能并行数字信号处理(DsP)系统的硬件平台的实现。提出了利用脉内非相干积累和设定K_(max)门限进行预检测的方法,优化了基于位置相关的高分辨检测方法的第2门限检测,并成功应用于并行DSP硬件平台中。试验表明,该硬件平台能够实现宽带雷达目标的实时高分辨检测。     

Targeted prostate biopsy using statistical image analysis

In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.     

Design and Realization of Software for Guard Against DDoS Based on Self-Similar and Optimization Filter

1IntroductionWith the development of network,security of net-work is more and more serious[1]flooding-based Dis-tributed Denial-of-Service(DDoS)attack presents avery serious threat to the stability of the internet[2~3].The detection of DoS/DDoS attack mai…     

提高波前探测精度的高阶矩方法

夏克-哈特曼波前传感器测量畸变波前的探测精度主要取决于光斑质心的测量精度。提出了一种提高光斑质心探测精度的新方法,在优化的探测窗口内使用高阶矩方法计算光斑的质心。首先,在整个子孔径内,通过一阶矩方法获得光斑的近似中心,然后以这个近似中心为中心,包含整个光斑,建立一个矩形窗口,并在该窗口内通过高阶矩方法重新计算光斑的质心。通过该改进的方法,在优化的探测窗口外,噪声的影响基本被消除;在优化的探测窗口内,噪声的影响也因为光斑权值比重的增大而削弱。实验结果证明:与传统方法相比,新方法提高了光斑质心测量的精度、重复性和稳定性。     

A real-time microprocessor QRS detector system with a 1-ms timingaccuracy for the measurement of ambulatory HRV

The design, test methods, and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (EGG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively     

一种优化的神经网络树异常入侵检测方法

本文提出了一种基于优化神经网络树(ONNT)的异常检测方法,在提高异常检测精确率的同时,增强异常检测模型学习结果的可理解性、可解释性。ONNT是一种具有二叉树结构的混合学习模型,二叉树的节点分裂遵循信息增益率准则;其中间节点嵌入了结构简单的感知器神经网络,能够根据当前节点上给定的子样本集和教师信号,选择较小的特征子集构建相对简单的局部决策曲面。本文提出的异常检测方法包括两个方面的性能优化:1）通过优化神经网络树(NNT)的中间节点,降低局部决策曲面的复杂度,从而使中间节点能在可接受的计算代价内表示成低复杂度的布尔函数或规则集,为实现学习结果的可解释性提供基础;2）通过优化学习模型的整体结构,降低所有中间节点的规则析取式的前件复杂度,从而提高学习结果的可理解性。实验的数值结果表明,与基于NNT的异常检测方法相比,本文提出的方法能够以简单的中间节点和相对精简的整体结构提高检测结果的可解释性和可理解性;与其他同类方法相比,基于ONNT的异常检测方法具有较高的检测精确率,且在一定程度上给出了对异常检测具有重大影响的一些特征信息。      

基于优化Inception V1的视频火焰超像素检测方法

针对传统火焰检测模型的检测准确度较低和速度慢等问题,提出一种优化的卷积神经网络和超像素分割算法的视频火焰区域检测方法。首先使用火焰图像数据集对模型进行训练和验证,采用卷积核堆叠替换的方法改进Inception模块的结构;其次采用小卷积核替换的方法改进网络的前端结构,并将Focal-Loss函数作为损失函数以提高模型的泛化能力;然后设计InceptionV1模型的参数复杂度优化实验,生成优化的火焰检测网络结构;最后将超像素分割算法提取的火焰超像素语义信息输入优化的InceptionV1模型中,并进一步执行视频火焰区域的定位检测。实验结果表明,所提方法能够增强视频火焰的非线性特征提取能力,火焰检测准确度高于96%,检测速度较原始模型提升2.66倍。     

A regional distance regression network for monocular object distance estimation

Monocular pipelines are convenient and cost-effective solutions for object distance estimation in 3D vision. Current methods for monocular object distance estimation either perform inaccurately or require heavy work on data collection. In this paper, we propose a network with R-CNN based structure to implement object detection and distance estimation simultaneously. We append an efficient branch to integrate the information of camera extrinsic parameters with RGB data in our network. Further, optimized multi-scale feature is utilized to enrich the representation power of deep feature, hence to enhance the estimation accuracy. Finally, several regression methods are explored to improve distance estimation results. We train and validate our network on KITTI object dataset, and compare with other methods to show that our method is accurate and easy to train. To prove the generality of our method under other scenarios, we construct a dataset of surveillance scenes, and conduct similar experiments on this dataset.     

Analysis and Optimization of Sensing Reliability for Relay-Based Dual-Stage Collaborative Spectrum Sensing in Cognitive Radio Networks

Aiming at multiuser cognitive radio networks in a harsh electromagnetic environment, cooperative spectrum sensing with single cooperation can hardly achieve to the desired detection performance. In this paper, a relay-based dual-stage collaborative spectrum sensing model (DCSS) that combines the distributed method with the centralized method is proposed. Furthermore, the optimality of the detection performance of DCSS is investigated in an efficient and feasible way. The optimal voting rule value and the optimal energy detection threshold are also derived by minimizing the detection error rate of the entire network. Finally, an efficient fast sensing algorithm for a large-scale cognitive radio network is deduced, which requires the minimal number, and not all, of cognitive radio users for DCSS while satisfying the target detection error rate bound. The simulated results indicate that when compared to the normal single cooperation method, the optimized DCSS method can reduce the number of cognitive radio user by 65 % while still meeting the detection error rate requirement of less than 1 %.