Study on Dim Target Detection and Discrimination from Sea Clutter

Dim target detection from sea clutter is one of the difficult topics in ocean remote sensing application. By aiming at the shortcoming of false alarms when using track before detect （TBD） based on dynamic programming, a new discrimination method called statistics of direction histogram （SDH） is proposed, which is based on different features of trajectories between the true target and false one. Moreover, a new series of discrimination schemes of SDH and Local Extreme Value method （LEV） are studied and applied to simulate the actually measured radar data. The results show that the given discrimination is effective to reduce false alarms during dim targets detection.

Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex backgrounds.These non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified samples.However,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time consuming.To address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets.Specifically,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are investigated.Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model.Then,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training process.These strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive training.Experimental results demonstrate that the proposed method outperforms traditional tritraining.

Novel discrimination method of digital deceptive jamming in mono-pulse radar

A new polarization measurement algorithm by using the sum and difference beam differential property of mono-pulse radar is given.Based on the generation mechanism differences between the target scattering and multi-false-target jamming,the signal models of real targets and digital deceptive false target jamming for sum and delta channel are presented.The polarization discrimination parameters are designed,and the discrimination method and its performance are discussed.This novel method does not need the accurate estimation of the absolute value of full target polarization scattering matrix,but only requires the relative estimation of the orthogonal polarized component of the targets.Without the need to add additional polarization channels,the proposed method is more suitable for engineering realization.The simulation experiment verifies that the correctly identifying probability can be better than 90%.

Kernel-based fisher discriminant analysis for hyperspectral target detection

A new method based on kernel Fisher discriminant analysis (KFDA) is proposed for target detection of hyperspectral images. The KFDA combines kernel mapping derived from support vector machine and the classical linear Fisher discriminant analysis (LFDA), and it possesses good ability to process nonlinear data such as hyperspectral images. According to the Fisher rule that the ratio of the between-class and within-class scatters is maximized, the KFDA is used to obtain a set of optimal discriminant basis vectors in high dimensional feature space. All pixels in the hyperspectral images are projected onto the discriminant basis vectors and the target detection is performed according to the projection result. The numerical experiments are performed on hyperspectral data with 126 bands collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental results show the effectiveness of the proposed detection method and prove that this method has good ability to overcome small sample size and spectral variability in the hyperspectral target detection.

一类基于模糊推理的具有机动自适应的目标跟踪算法

针对变结构多模型算法在机动目标跟踪中对目标机动不确定性、量测不确定性自适应能力不足的问题,提出一种基于模糊推理的机动自适应目标跟踪算法.设计一种基于模糊推理的双级机动判别模型,利用模型概率信息和主模型滤波残差加权范数进行主模型可信度和机动判别推理;并将双级机动判别引入基于可能模型集的期望模式扩增方法(EMA-LMS)框架,提出一种模糊推理EMA-LMS算法,实现对模型集自适应的参数和策略的在线调节,从而生成更加接近目标真实运动模式的期望模型,并更好地对模型进行取舍.仿真结果表明,本文算法能够有效增强算法对目标机动和量测不确定的自适应性,提高跟踪精度.

基于注意力机制的BiGRU战场目标意图判别

针对现代战场环境信息复杂化、多样化,战场目标具有隐蔽性、欺骗性等各种问题,提出一种基于双向门控循环单元(BiGRU)的战场目标意图识别方法。为了使网络更好的关注信息重点,提高目标意图识别的准确率,在BiGRU中加入了注意力机制,注意力机制可以使模型更加关注与预测结果有紧密联系的部分,忽略相关性不高的地方,同时采用Zoneout机制来减少网络过拟合。实验结果得出,结合注意力机制的BiGRU战场目标意图判别方法准确率高达97.1%,比基础GRU算法准确率提高12%。

基于相位变化率的体目标与点源合成目标回波分辨

针对水下体目标与多点源合成回波信号的分辨问题,本文基于体目标与多点源合成回波信号的形成机理差异,表征分析了影响水下目标回波相位形成的关键因素及水下体目标与多点源合成回波相位形成的差异性,基于2类回波信号相位变化率的特征差异,提出并建立了基于相位变化率的体目标回波与多点源合成回波的分辨方法。通过Benchmark基准潜艇缩比模型和多点源合成目标模型的特性仿真和湖上对比测试对2类回波信号的分辨方法进行了验证,仿真和测试数据分析结果表明:通过对比同方位角度范围内的回波相位变化率,可以对体目标回波与多点源合成回波进行分辨,验证了基于相位变化率的水下体目标与多点源合成回波分辨方法的可行性。

长时视觉跟踪中基于双模板Siamese结构的目标漂移判定网络

在长时视觉跟踪中,大部分目标丢失判定方法需要人为确定阈值,而最优阈值的选取通常较为困难,造成长时跟踪算法的泛化能力较弱。为此,该文提出一种无需人为选取阈值的目标漂移判定网络(DNet)。该网络采用Siamese结构,利用静态模板和动态模板共同判定跟踪结果是否丢失,其中,引入动态模板有效提高算法对目标外观变化的适应能力。为了对所提目标漂移判定网络进行训练,建立了样本丰富的数据集。为验证所提网络的有效性,将该网络与基础跟踪器和重检测模块相结合,构建了一个完整的长时跟踪算法。在UAV20L, LaSOT,VOT2018-LT和VOT2020-LT等经典的视觉跟踪数据集上进行了测试,实验结果表明,相比于基础跟踪器,在UAV20L数据集上,跟踪精度和成功率分别提升了10.4%和7.5%。

基于波束空间交替投影极大似然的拖曳式诱饵分辨判别方法

机载平台在遭遇威胁时,通过释放拖曳式诱饵假目标干扰进行自卫.载机和拖曳式诱饵在雷达波束宽度内,且径向距离小于距离分辨率时,雷达无法通过常规方法对载机目标和拖曳式诱饵进行分辨测量.针对数字相控阵雷达同拖曳式诱饵对抗的问题,提出一种基于波束空间交替投影极大似然(BSAP-ML)方法.首先通过阵元空间到波束空间处理,减少数字相控阵维度;然后通过交替投影方法对目标和拖曳式诱饵的角度进行迭代测量;最后通过基于角度向量的判别方法对载机目标和诱饵进行判断识别.仿真结果表明,该方法可用于数字相控阵雷达,对载机和拖曳式诱饵进行有效的超分辨角度测量和判别.

曾普华从“癌毒驱动-态靶调治-阴阳复衡”论治结直肠癌

随着中西医结合治疗的不断发展,结直肠癌的治疗手段逐渐多元化及复杂化。临床中往往根据病理、分期、基因表达等,灵活采用多种方法联合或配合进行治疗。由于西医治疗体系难以评价中医证候的影响,单纯的分证论治不足以构建肠癌的中医诊疗体系。因此基于“癌毒驱动-态靶调治-阴阳复衡”的诊治思路,通过探讨结直肠癌与“癌毒-态靶”理论的密切联系,深入了解其关键病机,正确把握其态靶——即结直肠癌患者的发病整体态势及其具体症状或异常指标,采取相应的治疗措施十分重要。应用以人为本、宏观调态、微观打靶的辨证论治思路,总结出结直肠癌热毒、痰湿、虚寒、气血亏虚4种常见证靶,再分别选用相应靶方调整患者整体内环境,同时选择对应靶点靶药,使态靶同调,达到精准治疗、调整阴阳平衡、恢复稳态的治疗目的。

基于RMDLPP的雷达空中目标分类

针对鉴别局部保持投影(discriminant locality preserving projections, DLPP)在窄带雷达目标数据降维时出现的类内离散度矩阵奇异和对孤立点敏感进而导致类别之间可分性弱的问题,提出了基于鲁棒性边界DLPP(robust margin DLPP, RMDLPP)的窄带雷达空中目标分类方法。首先,在计算样本之间距离时将两样本点的欧氏距离与同类样本均值相关联。然后,挑选一定数量的边界样本点进行处理并对优化DLPP目标函数进行降维。最后,使用高性能分类器对降维后的数据进行区分,实现对空中目标的分类。通过对X波段对空警戒雷达实测数据的对比实验表明,所提方法具有更好的分类准确率和对孤立点的鲁棒性。

FAST DETECTING TARGET GROUPS IN SAR IMAGES

A successful algorithm for detecting target groups is presented. Firstly, A global Constant False Alarm Rate (CFAR) detector is utilized to locate the potential target regions, and then the features are com- puted for target discrimination based on voting mechanism. Finally, Target groups are extracted. The results of experiments show the validity of this algorithm.

Target detection and recognition in SAR imagery based on KFDA

Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

基于特征分解卷积神经网络的SAR图像目标检测方法

真实场景的高分辨率合成孔径雷达(SAR)图像大多是复杂的,对于地物场景来说,其背景中存在草地、树木、道路和建筑物等杂波,这些复杂背景杂波使得传统SAR图像目标检测算法的结果包含大量虚警和漏警,严重影响了SAR目标检测性能。该文提出一种基于特征分解卷积神经网络(CNN)的SAR图像目标检测方法,该方法在特征提取模块对输入图像提取特征后,通过特征分解模块分解出鉴别特征和干扰特征,最后将鉴别特征输入到多尺度检测模块进行目标检测。特征分解后去除的干扰特征是对目标检测不利的部分,其中包括复杂背景杂波,而保留的鉴别特征是对目标检测有利的部分,其中包括感兴趣目标,从而有效降低虚警和漏警,提高SAR目标检测性能。该文所提方法在MiniSAR实测数据集和SAR飞机检测实测数据集(SADD)上的F1-score值分别为0.9357和0.9211,与不加特征分解模块的单步多框检测器相比,所提方法的F1-score值分别提升了0.0613和0.0639。基于实测数据集的实验结果证明了所提方法对复杂场景SAR图像进行目标检测的有效性。

多视图交叉一致性学习的半监督水面目标检测

为缓解基于半监督学习的水面目标检测对有限标注样本过拟合的问题,提高无标注样本中目标提取的有效性,提出了基于多视图交叉一致性学习的半监督水面目标检测算法。首先,该算法通过数据增强的方式为训练样本生成不同的视图以丰富数据集的多样性;然后,利用所提出的多视图目标判别器为无标注样本在线生成伪标签,有助于提取无标注样本的有效信息;最后,利用所提出的多视图交叉一致性学习使同一目标实例的不同视图的输出实现交叉一致性正则化,以促进检测模型学习判别性的特征从而降低过拟合的风险。在海上和内河数据集上的实验结果表明:文中所提算法能够提高特征提取的判别性,对多类别的水面目标检测精度达到91.0%,比全监督检测算法提高了18.7%,比其他半监督检测算法提高了3.8%以上;在检测速度上,该算法达到13.1帧/s,基本满足实时性要求。所提算法通过多视图交叉一致性学习提高特征的判别性和缓解检测模型的过拟合风险,有助于提高半监督水面目标检测的性能。

阵不变量匹配主动声呐目标深度辨识

水面/水下目标深度辨识可以在主动声呐探测过程中快速筛选感兴趣目标。根据目标多径返回信号到达时间与目标深度的关联性,该文提出了基于水平线阵的主动脉冲响应匹配目标深度辨识方法,该方法利用主动信号传播特性结合阵不变量理论提取脉冲响应,并与拷贝声场匹配实现目标深度估计。在校正了目标多径回波的方位变化后,该方法提取的时域特征较为稳定,并且只在时延估计距离处进行深度搜索。通过水平阵收集的宽带实验数据,在连续的多次回波中实现了对水面/水下目标92%的辨识成功率,验证了该方法在浅海环境下的目标深度估计能力。

Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

考虑消费者偏好的竞争性企业电子优惠券定向投放策略研究

针对生产异质产品的竞争性企业电子优惠券投放问题,本研究考虑消费者偏好异质,剖析基于消费者偏好的电子优惠券定向投放对产品效用的影响机理,构建发放相同面值、不同面值优惠券的决策模型,并进一步讨论产品质量差异下企业的最优优惠券促销策略。研究结果表明:1)优惠券面值与产品价格、质量呈正相关关系,而定向能力水平与消费者价格敏感度呈负相关,与质量敏感度的关系受产品差异的影响;2)优惠券歧视性定价改变了企业的价格、定向能力策略,针对价格敏感型消费者的优惠券面值高于统一定价策略下的优惠券面值,而针对质量敏感型的电子优惠券面值是低于统一定价策略下电子优惠券面值;3)相较于相同面值优惠券策略,不同面值优惠券策略使得企业能够获取更大利润,尤其对于产品或者品牌处于劣势的企业。

均匀双圆环阵虚拟成阵方法研究

针对在航空声呐系统有限空间内双圆环阵因孔径受限而导致的目标分辨力和空间分辨率不足的问题,提出了一种可以实现双圆环阵孔径扩展的虚拟成阵方法。该方法利用双圆环阵模态域信号模型和双直线阵阵元域信号模型之间的关系,设计了采样矩阵和相位旋转矩阵,将均匀双圆环阵转换为均匀双直线阵。考虑了实际双圆环阵中的阵元位置误差、阵元间耦合、观察角度误差等的影响,利用最小二乘准则提出了一种将实际双圆环阵近似转换为均匀双直线阵的优化模型,并给出了迭代求解方法。与合成孔径方法相比,该方法是对基阵阵列流形的一种预处理,对使用过程中基阵的运动特性没有特殊要求;与模态域转换方法相比,该方法可以体现目标双直线阵的阵元间隔和两线阵间距,充分利用了双直线阵相对于单直线阵的优势。仿真实验和水池实验结果表明,双圆环阵的孔径得到了扩展,虚拟阵波束图比双圆环阵波束图的主瓣更窄且旁瓣更低,有效提高了目标分辨力和空间分辨率。

利用多普勒信息判断单线阵低速目标左右舷的方法研究

采用常规单线拖曳阵列作为双基地主动声呐的接收平台时,会存在左右舷模糊。常规左右舷判决方法需要声呐探测平台进行转弯等复杂机动,目标定位与跟踪的实时性和连续性难以保证。文章对利用回波多普勒效应实时判断低速目标左右舷的方法进行了研究,给出了目标低速运动的条件,并分析了多普勒响应可分辨特性与声呐平台位置、运动状态的关系。通过仿真实验验证了该方法的可行性与有效性。海试数据处理结果表明,声源船仅具漂流速度时即可通过回波多普勒信息有效判别静止浮台目标的左右舷。该方法可以作为双基地主动声呐探测与定位系统中的辅助手段。