Theoretical analysis of the surface temperature regulation of an infrared false target subjected to periodical ambient conditions

Infrared false target is an important mean to induce the infrared-guided weapons,and the key issue is how to keep the surface temperature of the infrared false target to be the same as that of the object to be protected.One-dimensional heat transfer models of a metal plate and imitative material were established to explore the influences of the thermophysical properties of imitative material on the surface temperature difference（STD） between the metal plate and imitative material which were subjected to periodical ambient conditions.It is elucidated that the STD is determined by the imitative material’s dimensionless thickness（dim*,） and the thermal inertia(Pim).When dim* is above 1.0,the STD is invariable as long as Pim is a constant.And if the dimensionless thickness of metal plate（d,m*） is also larger than 1.0,the STD approaches to zero as long as Pimis the same as the thermal inertia of metal plate（Pm）.When dim* is between 0.08 and 1,the STD varies irregularly with Pim and dim*.However,if dm* is also in the range of 0.08-1,the STD approaches to zero on condition that Pim=Pm and dim*= dm*.If dim*,is below 0.08,the STD is unchanged when Pimdim* is a constant.And if dm* is also less than 0.08,the STD approaches to zero as long as Pimdim* = Pmdm*.Furthermore,an applicationoriented discussion indicates that the imitative material can be both light and thin via the application of the phase change material with a preset STD because of its high specific heat capacity during the phase transition process.

New CFAR target detector for SAR images based on kernel density estimation and mean square error distance

A new constant false alarm rate （CFAR） target detector for synthetic aperture radar （SAR） images is developed. For each pixel under test, both the local probability density function （PDF） of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error （MSE） distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm （PFA）, an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.

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.

Generalized Approach to Signal Processing in CollaborativeWireless Sensor Systems for Target Detection

Collaboration in wireless sensor systems must be fault-tolerant due to the harsh environmental conditions at which such systems can be deployed. This paper focuses on finding the signal processing algorithms for collaborative target detection based on the generalized approach to signal processing （GASP） in the presence of noise. The signal processing algorithms are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor systems. Two types of generalized signal processing algorithms, namely, value fusion and decision fusion constructed according to GASP in the presence of noise, are identified first. When comparing their performance and communication overhead, the decision fusion algorithm is found to become superior to the value fusion algorithm as the ratio of faulty sensors to fault free sensors increases. The use of GASP under designing the value and decision fusion algorithms in wireless sensor systems allows us to obtain the same performance, but at low values of signal energy, as well as under employment of the universally adopted signal processing algorithms widely used in practice.

基于WGAN-GP-CNN的海面小目标检测

针对传统基于统计理论的海面小目标检测方法在复杂海面环境中性能不高的问题,该文提出了一种改进的检测方法。首先通过分析海杂波和目标回波的特征,将检测问题转化为特征空间的分类任务。鉴于海面小目标样本数量有限,存在样本不平衡的问题,该文引入了一种基于梯度惩罚的沃瑟斯坦生成对抗网络(Wasserstein Generative Adversarial Network with Gradient Penalty,WGAN-GP)来增强目标数据,从而在数量上平衡目标样本与海杂波样本。同时,对原始WGAN-GP网络的损失函数进行了改进,引入相位损失以确保生成数据能够反映真实数据的相位信息。基于这些数据,进一步提取了生成目标和海杂波的高维特征,并将其送入卷积神经网络(Convolutional Neural Network,CNN)进行训练。为了应对高维特征空间中虚警概率难以控制的问题,对CNN算法进行了改进,通过设置Softmax分类器的阈值,实现了虚警概率可控。最后,借助公开的IPIX雷达数据集进行实验验证,所提的WGAN-GP-CNN检测器在积累时间为1.024 s,虚警概率为0.001时,平均检测概率达到0.8683,具有良好的检测效果。

基于SVM算法的虚假航迹识别

针对云雨杂波和主被动干扰导致多雷达传感器产生虚假目标航迹的问题,利用支持向量机(SVM)算法的自主学习能力,通过构建基于数据驱动的判别模型进行虚假航迹识别。针对航迹起始得到的目标潜在航迹,利用人工智能数据驱动、自学习的特点,设计了SVM算法。通过对已标记真假的目标航迹样本进行离线学习,形成虚假航迹识别的SVM分类器,实现了基于数据驱动的判别模型代替先验知识规则约束的固定模型,并在工程应用中,利用SVM分类器在线识别虚假航迹,完成实时剔除。通过实测雷达数据实验验证,该算法的目标虚假航迹准确率高达95%以上,完全满足实际的工程应用需求。相比基于阈值或规则进行硬性判断的传统虚假航迹识别方法,所提出的算法不仅提高了准确率,还具有较高的实时性,能够适应复杂多变的杂波环境,在实际应用中具有更强的适应性和实用性。因此,提出的基于SVM算法的虚假航迹识别方法对于密集杂波场景下的虚假航迹剔除问题具有显著的实际应用价值。

基于KM算法的转发式干扰辨识与抑制方法

转发式干扰是对截获的雷达信号进行调制后发射出去的干扰信号,它在空时频多域与目标回波存在强相关性。当雷达接收端进行信号处理时,此类干扰能够获得匹配滤波增益,形成若干个虚假目标,同时实现欺骗和压制2种干扰效果。联合频率捷变技术,利用聚类思想在脉压域实现干扰辨识与抑制。仿真实验表明,所提方法能够有效抑制干扰,且当干扰功率较大时,依然具有良好的抗干扰效果。

DRFM干扰在OFDM新体制雷达中的研究

新体制雷达以正交频分复用(OFDM)数字调制方式作为雷达信号源,具有高频谱效率、高分辨能力等优点。本文首先介绍了新体制雷达系统的基本组成部分,主要包括OFDM信号源、发射模块、环境模块、接收模块和信号处理模块。其次介绍了数字射频存储器(DRFM)干扰的主要组成部分以及工作原理,其中主要包括假目标干扰。最后将DRFM干扰与新体制雷达结合,在加干扰和不加干扰两种情况下对仿真目标进行测距和测速,结果验证了新体制雷达测量目标的准确性以及DRFM干扰的可行性。对新体制雷达及DRFM干扰研究具有参考意义。

一种慢时间维的密集假目标干扰主瓣匿影算法

受干扰机对雷达脉冲重复频率、波束扫描周期测量精度影响,密集假目标干扰在回波中的慢时间分布与目标不同。针对该差异,提出一种慢时间维的密集假目标干扰主瓣匿影(Main-Lobe Blanking,MLB)算法。该算法与副瓣匿影(Side-Lobe Blanking,SLB)具有一定相似性,区别在于SLB是利用辅助通道回波,对主通道回波沿快时间进行选通,而所提MLB算法是利用主通道内的多个脉冲重复周期回波,对各距离单元回波沿慢时间进行选通。仿真与实测数据表明,所提方法可有效抑制主瓣或副瓣进入的密集假目标干扰。

间歇采样转发干扰技术研究述评

间歇采样转发干扰技术是一种巧妙的相干多假目标干扰技术,针对不同雷达体制、不同雷达波形等的新型间歇采样转发干扰技术已成为当前雷达对抗领域的研究热点之一。为进一步厘清间歇采样转发干扰技术特性及其拓展应用前景,本文从间歇采样转发干扰技术提出的背景及基本原理出发,详细梳理间歇采样转发干扰的技术发展历程,并对诸多新型间歇采样转发干扰技术进行分类总结,最后介绍间歇采样在雷达探测等其他方面的拓展应用,旨在为专家学者研究提供一定的参考。

基于运动模式集精细差异特征估计的真假弹道目标联合跟踪与辨识方法

针对对抗条件下弹道目标和有源多假目标跟踪及辨识难的问题,基于稳健交互多模型(Robust Interacting Multiple Model,RIMM)策略,提出真假弹道目标的联合跟踪与辨识方法。该方法基于推导的真假目标运动模式集以及模式间的精细差异特征设计交互多模型(Interacting Multiple Model,IMM)策略,以扩展卡尔曼滤波(Extended Kalman Filter,EKF)为子滤波器,并引入概率调整因子与时变因子,实时更新概率转移矩阵,有效放大运动模式集的精细差异特征,不仅能实现对真假目标的稳定跟踪,提高跟踪精度,同时也能实时在线辨识真假目标,实现跟踪辨识一体化。仿真结果表明,该方法的跟踪效果比传统单模型EKF算法和经典的IMM+EKF算法更好,能实时跟踪并辨识出真假目标,有利于提高雷达资源调度的效率。

基于等分剔除的毫米波雷达恒虚警检测研究

为解决车载毫米波雷达目标检测过程中在相邻多目标情况下会提高检测门限值,从而导致目标遮蔽现象,引发传统恒虚警(constant false alarm rate, CFAR)检测在多目标环境下产生漏检的问题,提出一种等分剔除恒虚警检测算法(equipartition deletion CFAR,ED-CFAR)。通过将目标两侧的参考单元等分取平均值得到子参考单元,对大于所有参考单元平均值的部分进行处理后排序,剔除部分较大子参考单元,计算得到门限值,与目标的信号幅度进行比较判断目标是否存在。仿真结果表明,在多目标检测的仿真场景下,信噪比为10 dB时,ED-CFAR算法的检测概率比单元平均恒虚警(CA-CFAR)算法提高0.39,比筛选平均恒虚警(CMLD-CFAR)算法提高了0.08,有效解决了传统均值类恒虚警检测在相邻多目标条件下的遮蔽问题。

非均匀杂波背景下的自适应检测器设计

为提高恒虚警检测器在不同检测背景下的鲁棒性和抗干扰能力,提出一种综合型的自适应检测器,其具备多策略选择能力和检测参数动态调节能力。所提检测器引入贝叶斯信息准则,通过计算参考单元的信息估计序列,对当前检测背景类型进行识别。进一步,在非均匀检测背景下,该检测器能够对干扰目标个数或杂波边缘位置进行估计。最终根据识别和估计结果选择对应的检测策略和检测参数。利用蒙特卡罗仿真,对所提检测器性能进行分析,并与经典检测器进行比较。仿真结果表明,该检测器具备检测背景识别能力和背景参数估计能力。同时,该检测器在检测背景参数先验知识不足的情况下,具有较好的抗干扰能力。

基于脉间码型捷变波形的距离-多普勒二维干扰重构算法

密集假目标干扰通过在真实目标周围产生大量假目标,形成欺骗和压制双重干扰效果,严重影响雷达的目标探测能力。为此,该文提出一种基于脉间码型捷变波形的距离-多普勒二维干扰重构算法来抑制密集假目标干扰。该算法基于脉间码型捷变波形的距离选通性,在距离-多普勒域通过交替反演实现干扰和目标重构,并从回波中对消干扰来实现干扰剔除。首先,通过构造不同距离段的接收滤波器组来实现干扰和目标回波的分距离段处理;其次,采用联合失配滤波器组使各脉冲滤波输出的距离旁瓣结构近似相同,减小脉间码型捷变波形经脉冲多普勒处理后沿多普勒维的散布能量;然后,利用干扰和目标回波在不同距离-多普勒平面上的能量分布特性构造滤波矩阵;最后,通过交替反演实现干扰和目标的精准重构,进而实现密集假目标干扰的抑制。仿真结果表明,与传统方法相比,该文所提算法在干扰抑制和运行时间方面展现出优越的性能,显著提升了雷达在强干扰环境中的目标检测能力。

密集假目标干扰峰均比优化方法分析

针对延迟叠加产生密集假目标干扰存在峰均比(Peak-to-Average Power Ratio,PAPR)过高的问题,分析了现有多相序列调制法的适应性。针对其不足,提出了一种利用粒子群算法(Particle Swarm Optimization,PSO)优化干扰波形以降低峰均比的方法。首先基于雷达发射线性调频脉冲信号,建立延迟叠加产生密集假目标干扰的信号模型。然后以最小化干扰波形的峰均比为目标函数,结合干扰调制参数约束条件形成优化问题。随后利用PSO得到干扰调制参数的次优解,最终通过仿真实验验证了所提方法的有效性。

子空间干扰加高斯杂波背景下基于GLRT的斜对称方向检测器设计

文章研究了背景为子空间干扰加高斯杂波的距离扩展目标方向检测问题。杂波是均值为零协方差矩阵未知但具有斜对称特性的高斯杂波,目标与干扰分别通过具备斜对称特性的目标子空间和干扰子空间描述。针对方向检测问题,利用上述斜对称性,根据广义似然比检验(Generalized Likeli-hood Ratio Test,GLRT)准则的一步与两步设计方法,设计了基于GLRT的一步法与两步法的距离扩展目标方向检测器。通过理论推导证明了这2种检测器相对于未知杂波协方差矩阵都具有恒虚警率。对比相同背景下已有检测器,特别是在辅助数据有限的场景下,文章提出的2个检测器表现出了优越的检测性能。

Polarization discrimination between repeater false-target and radar target

High fidelity repeater false-target badly affects a radar system＇s detecting, tracking, and data processing. It is an available approach of confronting false-target for radar that discriminates firstly and then eliminates. Whereas for the technique progress about the repeater false-target jam, it is more and more difficult to discriminate this jam in the time-domain, frequency-domain, or space-domain. The technique using polarization information to discriminate the target and false-target is discussed in this paper, With the difference that false-target signal vector＇s polarization ratio is fixed and target echo signal vector＇s polarization ratio is variational along with radar transmission signal＇s polarization, we transform the discrimination problem to beeline distinguish problem in the 2-dim complex space. The distributing characteristic expression of the false-target discrimination statistic is constructed, with which the discrimination ratio of false-target is analyzed. For the target case, the decomposed model of target scattering matrix and the concept of distinguish quantity are proposed. Then, the discrimination ratio of target can be forecasted according to target distinguish quantity. Thus, the performance of discrimination method has been analyzed integrally. The simulation results demonstrate the method in this paper is effective on the discrimination of target and false-target.

一种岸基船舶雷达多目标跟踪分析系统

岸基船舶雷达主要用于近海船舶监视和管理。海域环境下雷达回波起伏多样复杂,回波的稳定性易受环境影响,直接影响目标的检测及跟踪,强杂波等干扰严重制约了航迹跟踪分析性能。在密集杂波背景下,基于常规数据处理的航迹起始方法易存在虚假航迹多、起始速度慢及目标起始困难等问题。为了降低虚警率、提高起始效率和目标稳定跟踪的性能,设计了一种基于岸基船舶雷达多目标跟踪分析系统。该系统用于近海雷达船舶监视和管理,已经成功应用于海洋渔业局等监管执法部门,在海域杂波环境下,极大地提高目标起始和稳定跟踪效果,具有较高的应用价值。

基于韦布尔分布下改进型恒虚警检测器的检测性能分析

为了提高恒虚警检测器(constant false alarm rate detector,CFAR)在有干扰目标存在的杂波环境下的检测性能,提出了5种基于自适应删除(automatic censored mean level detector,ACMLD)恒虚警算法与均值类算法的改进型,并在韦布尔分布模型下对影响背景杂波功率估计的参考单元长度和干扰目标个数进行检测概率分析。结果表明:基于该改进型检测器可有效提高有干扰目标的检测性能,其中改进型单侧自适应删除(single automatic censored mean level detector,SACMLD)算法适合在一侧参考窗均匀的情况下使用,而自适应删除最小(adaptive censored smallest of detector,ACSO)算法适合两侧参考窗都不均匀的情况下使用。文中提出的基于韦布尔分布下改进型恒虚警检测器可为铁路、航空等多目标环境检测提供参考。

基于改进YOLO-V5算法的烟火检测方法

为减少自然环境中云、水雾、沙尘、灯光、日出、日落等干扰因素对烟雾、火焰目标检测准确性的影响,提出一种基于改进YOLO-V5算法的烟火检测算法。采用现场采集和网络爬取的方法获取烟雾、火焰目标图像和干扰类图像数据集,均衡学习训练样本,提高模型泛化能力;使用加权双向特征金字塔网络(BiFPN)替换原有的特征金字塔网络(FPN)+路径聚合网络(PAN)结构,对目标进行多尺度特征融合,加强模型特征融合能力;同时,运用距离交并比(DIoU)非极大值抑制(NMS)替代原有的NMS,加快检测框损失函数收敛速度,加强模型推理能力。结果表明:改进后的算法准确率为79.2%,召回率为68.6%,平均精度均值(mAP)为74.2%,误报率(FPR)为12.8%;相比于原YOLO-V5算法,改进后的算法准确率、召回率、mAP分别提高1.9%、0.9%、2.7%,检测识别FPR降低3.7%。