基于残差特性分析的野值检测与剔除方法

在导航和控制工程实际应用中,野值的在线处理一直是难点之一.现有的利用残差的野值判别方法往往建立在最优滤波的前提上,在处理速度、效果和实用性上有所局限,难以适用于这一范围.在分析残差信号特征的基础上,利用残差变化率的变化规律,使用莱特准则对野值进行判断.仿真计算和实际工程应用表明,该方法算法简单、效果明显,能够满足工程实际应用的要求,很大程度上消除了野值对滤波精度的影响.     

UMVE对数单元平均恒虚警检测器性能分析

利用无偏最小方差估计(UMVE)算法分析了UMVE对数单元平均恒虚警检测器的性能.它的前沿和后沿均采用UMVE算法产生局部估计,再对两者求和得到背景功率水平.在SwerlingⅡ模型下,给出UMVE对数单元平均恒虚警检测器虚警概率和检测概率解析表达式.与对数单元平均恒虚警检测器相比,UMVE对数单元平均恒虚警检测器的性能得到了改善.     

非相干Rice杂波中的恒虚警检测

 地杂波的统计特性常常可以用Rice模型来描述,其物理基础是认为地杂波由一些大的固定散射体引起的稳定分量和大量小的随机分布的运动散射体引起的瑞利起伏分量所合成。文献[2]研究了稳定分量不相干时Rice杂波中离散时间最佳检测的估值器——相关器结构,但无显式解,实现有困难。文献[3]导出了Rice杂波中SwerlingⅡ目标的离散时间检测的似然比检测器结构。在此基础上,本文给出了一种修正平方律结构的似然比检测器,并和通常的平方律检测器作了性能比较。     

基于加权融合的多信源弹道数据实时野值检测方法

研究多信源弹道数据实时处理中斑点野值的检测问题.通过计算各信源数据的实时精度,给出了具有自适应加权系数的加权融合方法,实现了对目标状态参数较高精度的估计,从而实时、准确、高效地检测各信源数据野值.仿真结果表明,本方法可以快速、有效地检测多信源数据的斑点野值,解决因数据切换带来的台阶跳跃问题.     

使用开关中值滤波方法剔除遥测数据野值

野值剔除是进一步分析遥测数据的前提,根据参数的变化规律,从数据中找出疑似野值,然后使用开关中值滤波方法（Switching Median Filter,SMF）处理疑似野值。从理论上阐述新方法的合理性与可行性。对开关中值滤波方法与现有几种常用方法进行较为详细的对比分析。理论分析与算例结果表明,该方法合理有效,并易于理解,计算简单。     

基于子空间投影的未知背景航拍高光谱图像恒虚警目标检测

针对航拍高光谱图像中未知背景地物特征条件下小目标的检测问题,给出一种检测算法。利用目标的低概率特性,通过模糊聚类获取高光谱图像中背景的光谱特性;然后将高光谱数据向背景光谱信号的正交子空间及目标信号子空间投影以抑制背景和噪声信号;最后在特征层利用广义似然比检验构造出具有恒虚警特性的检测器,完成融合检测过程。理论分析和实验结果表明了算法的有效性。     

航天靶场外测数据野值点的准实时诊断方法研究

野值点的检测与诊断在航天测控工程中有重要意义。本文根据测量数据序列容错滤波技术,建立了三组不但能用于事后高精度处理也可实时在线处理的野值点检测方法。仿真计算结果证实,这些方法不但能以100％准确率检出采样序列中孤立型的野值点,还可快速有效地检出存在于序列中的各野值斑点,准确率达到98％以上(与斑点长度和滑动窗长度有关)。     

一种限定虚警概率的PN码捕获自适应门限估计算法

提出了一种限定虚警概率的PN码捕获的自适应门限估计算法,首先在对判决变量的统计特性分析的基础上,计算出了判决门限的有偏估计量;然后分析了估计偏差对捕获系统检测概率和虚警概率的影响;最后,计算机仿真表明,在限定虚警概率的前提下,捕获系统在高斯白噪声信道和瑞利衰落信道下具有较高的检测概率,自适应门限的估计方法易于实现,且适合工程应用。     

非一致环境中相关信号有序统计恒虚警检测器及其性能研究

根据有序统计（ＯＳ）理论和恒虚警（ＣＦＡＲ）检测方法,提出了一种非一致环境下瑞利相关信号检测理论和分析方法,并利用上述分析方法对单窗ＯＳ－ＣＦＡＲ和双窗最大逻辑（ＭＸ）ＯＳ－ＣＦＡＲ的检测性能进行了分析．试验仿真结果给出了两种检测器在不同环境下的检测性能,有力地验证了上述分析方法的有效性．     

基于SVR的导航传感器自适应野值检测方法

针对Lagrange插值法无法处理连续野值的问题，提出了一种基于改进支持向量回归(Support Vector Regression, SVR)的导航传感器自适应野值检测方法。该方法结合了支持向量回归利用小样本数据就能够准确建模和3σ准则计算简易的优点，利用支持向量回归在线建立舰船的运动模型对测量值进行实时预测，并利用3σ准则自适应地计算阈值，然后通过比较阈值与预测残差来判别测量值是否为野值点。该方法可以自动地学习舰船的运动趋势，建立舰船的真实运动模型，而且不受连续野值点的影响，能够在没有其他传感器辅助的条件下完成野值检测。海试实测数据表明，提出的方法对离散和连续的野值点均具有较好的检测效果，同时可以更好地估计传感器的真实测量值。     

基于双边截断的双参数海上风电站 SAR图像 CFAR检测

文章提出了 1种基于双边截断的双参数海上风电站 SAR图像 CFAR检测器 DTCS-TPCFAR,目的是提高在具有多个目标海上区域和石油泄漏区域等环境下对海上风电站的检测性能。DTCS-TPCFAR所提出的双边截断杂波的方法,能够同时消除高强度和低强度异常值的干扰,同时保留真实的杂波样本。通过使用最大似然估计计算双边截断后样本的均值和标准差,然后通过这 2个参数估计值计算出截断阈值,最后再结合指定的虚警率(Probability of False Alarm,PFA)来对测试单元(Test Cell,TC)进行判断,完成最终的目标检测。这也是首次将 CFAR检测器用于检测海上风电站。文章通过 Sentinel-1数据集来验证该方法的有效性。实验结果表明,文章所提出的算法在相同指定虚警率下,具有更高的检测率(Detection Rate,DR)和更低的误报率(False Alarm Rate,FAR)。     

非高斯杂波下自适应雷达目标检测新方法

在球不变随机向量(SIRV)非高斯杂波背景下,研究了多脉冲相参雷达目标的自适应检测问题。假设杂波具有相同的协方差矩阵结构和可能相关的纹理分量,提出了新的协方差矩阵估计器,并获得了相应的自适应归一化匹配滤波器(ANMF)。理论分析表明,在估计杂波分组大小与实际情况匹配时,所获得的ANMF对杂波功率水平和协方差矩阵结构均具有恒虚警率(CFAR)特性。仿真结果表明:当估计的杂波分组大小失配时,所获得的ANMF具有近似CFAR特性,并进一步分析了不同参数变化对所提检测器性能的影响。与已有的ANMF相比,所获得的ANMF具有更好的检测性能,且迭代次数更小,其相对于已知杂波协方差矩阵的最优归一化匹配滤波器(NMF)的检测损失也更小,具有很好的实际应用前景。     

腐蚀条件下基于Gerber模型的DFR推导及实测

为了研究等寿命曲线模型的选取对细节疲劳额定值计算结果的影响,针对六种典型航空材料对比了Gerber模型和Goodman模型对于高周疲劳数据的拟合精度;推导基于Gerber模型的DFR计算公式、腐蚀折算系数CC的表达式;针对2024-T3铝合金(表面阳极化)进行了预腐蚀0 h、6 h、12 h、24 h、36 h和72 h的疲劳实验并分析预腐蚀72 h的疲劳断口。结果表明:Gerber模型适用于LY12CZ等铝合金,并且在N_95/95>10~5次时,基于Gerber模型的DFR法才能发挥延性材料的潜能;随着预腐蚀时间增长,2024-T3铝合金DFR值下降,基于Gerber模型计算的DFR分别为84.251 MPa、84.721 MPa、79.683 MPa、80.745 MPa、77.026 MPa和74.996 MPa,腐蚀折算系数CC为1.006、0.946、0.958、0.914、0.890,拟合得到DFR随预腐蚀时长的变化曲线是DFR=84.251[lg(t+10)]^-0.15578;断口分析发现预腐蚀产生的蚀坑和材料中的夹杂物会加速疲劳裂纹的形成和扩展,导致结构的疲劳性能降低,但与裸材相比,阳极化过的试件的DFR在腐蚀环境中下降趋势减缓。     

基于声发射的铝蜂窝板超高速撞击损伤模式识别方法

为通过声发射技术识别铝合金蜂窝板超高速撞击(HVI)的损伤状态,提出一种基于神经网络的损伤模式识别方法。通过超高速撞击实验获取声发射信号,结合精确源定位技术、时频分析技术、小波分析技术及模态声发射技术,提出了10个与损伤相关的特征参数,通过非参数检验分析其与损伤的关系,设计了一种基于贝叶斯正则化BP神经网络的超高速撞击损伤模式识别方法。建立最优网络模型,通过不同参数组合识别能力分析,优选出2种特征参数组合,通过非同源样本对其损伤模式识别能力进行验证。结果表明:传播距离与损伤模式无关,却是识别损伤模式的重要参数;125~250kHz频域的自动加窗小波能量比会降低损伤模式的识别能力;采用贝叶斯正则化的BP神经网络可以较好地识别蜂窝板超高速撞击损伤模式,参数组合为传播距离、上升时间、持续时间、截止频率、4个自动加窗小波能量比及小波能量熵,共9个参数,对任意选取非同源样本识别错分率仅为9.38%。     

A new skeleton based flying bird detection method for low-altitude air traffic management

In low-altitude air traffic management, non-cooperation targets are the greatest threat to security of low-flying aircraft. Among various aviation fatalities, flying bird is the main factor with the highest risk and directs economic losses amounted to nearly 10 billion US dollars each year. Therefore, Flying Bird Detection (FBD) has attracted considerable attention in low-altitude air traffic management. In this paper, we propose a skeleton based FBD method via describing bird motion information with a set of key poses. To overcome the variability of birds, the skeleton feature is selected as a relatively fixed and common characteristic for the pose appearance of flying bird. Based on the geometric topology among some key parts of bird body, a set of key poses can be described by some extracted skeleton features, which are used to represent the bird motion information. Aimed at robustly handling with the pose variations, multiple pose-specific classifiers are individually trained to learn the representative poses of the flying bird. At the detection stage, the flying bird skeleton features are combined with extracted key-pose sets to perform the flying bird classification task from each image. Afterwards, the key-frame pose-change set and the consistency of the classification results from sequent images are employed to validate the final detection results. Experiments on flying bird datasets demonstrate the effectiveness and efficiency of the proposed method.     

基于VBGMM-DCNN的列车卫星定位欺骗干扰检测方法

面向基于全球导航卫星系统的铁路列车定位实施欺骗干扰的主动检测，在卫星定位解算层次，运用深度学习建模学习方法的优势，提出一种基于变分贝叶斯高斯混合模型-深度卷积神经网络（variational Bayesian Gaussian mixture model-deep convolutional neural network, VBGMM-DCNN）的列车卫星定位欺骗干扰检测方法。该方法首先提取能够充分体现欺骗干扰对定位解算过程作用影响的卫星观测特征参数，构建干扰检测特征矢量；然后，采用VBGMM模型拟合经过预处理的特征向量的概率分布，得到二维概率密度图；最后，将概率密度图用于DCNN模型实施欺骗干扰的检测决策。结合现场实验所得运行场景数据，利用实验室搭建的欺骗干扰测试环境实施了干扰注入测试与检验，结果表明,欺骗干扰检测性能随着DCNN网络深度的增加而提升，相对于常规有监督决策方法F1值最高提升44.68%。基于VBGMM-DCNN的欺骗干扰检测能够适应测试验证中运用的列车运行特征及定位观测条件，所达到的检测性能优于对比算法。     

基于HHT的航空直流串行电弧特征提取方法

由于飞机内部布线空间有限、电弧故障存在发生时间地点随机以及特征不明显等问题,导致检测困难。本文基于航空270 V高压直流（HVDC）系统开展直流串行电弧故障特征提取方法研究,采用希尔伯特黄变换（HHT）提取电弧电流交流分量的时域和频域特征量。选择HHT的固有模态函数IMF5瞬时幅值的峰峰值和标准差作为识别电弧故障的时域特征,与原始信号中提取的时域特征量对比,正常和电弧特征量的区分度更大;选择HHT的固有模态函数IMF1+IMF2、一定频带范围内的瞬时幅值计算得到的谐波功率和作为区分正常和电弧情况的频域特征量。与常用的快速傅里叶变换（FFT）方法相比,HHT三维时频谱能够反映信号的局部特征,HHT方法计算得到的正常和电弧特征量之间的区分度更大,电弧和正常特征量的比值最高可达346。基于HHT的电弧故障特征提取方法能够更好地区分正常和电弧情况,有助于提高电弧故障的检测率,降低虚警率,具有重要的工程应用价值。     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

A novel visual attention method for target detection from SAR images

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.     

A method for automatic detection and characterization of plasma bubbles using GPS and BDS data

Detecting and characterizing Total Electron Content (TEC) depletion is important for studying the ionospheric threat due to the Equatorial Plasma Bubble (EPB) when applying the Ground-Based Augmentation System (GBAS) at low latitudes. This paper develops a robust method to automatically identify TEC depletion and derive its parameters. The rolling barrel algorithm is used to automatically identify the TEC depletion candidate and its parameters. Then, the depletion candidates are screened by several improved techniques to distinguish actual depletions from other phenomena such as Traveling Ionospheric Disturbance (TID) or abnormal data. Next, based on the depletion signals from three triangular receivers, the method derives EPB parameters such as velocity, width and gradient. The time lag and front velocity are calculated based on cross-correlation using TEC depletions and the geometrical distribution of three triangular receivers. The width and gradient of slope are then determined by using TEC depletion from a single receiver. By comparison, both the station-pair method and proposed method depend on the assumption that the EPB morphology is frozen during the short time when the plasma bubble moves between the receivers. However, our method relaxes the restriction that the baseline length should be shorter than the width of slope required by the station-pair. This relaxation is favorable for studying small-scale slope of depletions using stations of a longer baseline. In addition, the accuracy of the width and gradient is free of impact from hardware biases and small-scale disturbance, as it is based only on the relative TEC variation. The method is demonstrated by processing Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) data on 15 August, 2018, in a solar minimum cycle.