Pulse-to-pulse periodic signal sorting features and feature extraction in radar emitter pulse sequences

A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.

NOVEL RADAR SIGNAL SORTING METHOD BASED ON GEOMETRIC COVERING

With the increase of complexity of electromagnetic environment and continuous appearance of advanced system radars,signals received by radar reconnaissance receivers become even more intensive and complex.Therefore,traditional radar sorting methods based on neural network algorithms and support vector machine(SVM) cannot process them effectively.Aiming at solving this problem,a novel radar signal sorting method based on the cloud model theory and the geometric covering algorithm is proposed.By applying the geometric covering algorithm to divide input signals into different covering domains based on their distribution characteristics,the method can overcome a typical problem that it is easy for traditional sorting algorithms to fall into the local extrema due to the use of complex nonlinear equation to describe input signals.The method uses the cloud model to describe the membership degree between signals to be sorted and their covering domains,thus it avoids the disadvantage that traditional sorting methods based on hard clustering cannot deinterleave the signal samples with overlapped parameters. Experimental results show that the presented method can effectively sort advanced system radar signals with overlapped parameters in complex electromagnetic environment.

Multi-Parameter Signal Sorting Algorithm Based on Dynamic Distance Clustering

A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented. The algorithm includes two parts, pulse classification and pulse repetition interval (PRI) analysis. Firstly, we propose the dynamic distance clustering (DDC) for classification. In the clustering algorithm, the multi-dimension features of radar pulse are used for reliable classification. The similarity threshold estimation method in DDC is derived, which contributes to the efficiency of the algorithm. However, DDC has large computation with many signal pulses. Then, in order to sort radar signals in real time, the improved DDC (IDDC) algorithm is proposed. Finally, PRI analysis is adopted to complete the process of sorting. The simulation experiments and hardware implementations show both algorithms are effective.

A feature extraction method for the signal sorting of interleaved radar pulse serial

In this paper, a new feature extraction method for radar pulse sequences is presented based on structure function and empirical mode decomposition. In this method, 2-D feature information was constituted by using radio frequency and time-of-arrival, which analyzed the feature of radar pulse sequences for the very first time by employing structure function and empirical mode decomposition. The experiment shows that the method can efficiently extract the frequency of a period-change radio frequency signal in a complex pulses environment and reveals a new feature for the signal sorting of interleaved radar pulse serial. This paper provides a novel way for extracting the new sorting feature of radar signals.

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio( SNR) adaptive sorting algorithm using the time-frequency( TF)sparsity of frequency-hopping( FH) signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.

基于脉冲间隔分布矩阵的抗抖性改进算法

为解决利用脉冲间隔分布矩阵(PIDM)进行分选无抗抖性的问题,提出了一种改进算法.借鉴改进PRI变换法交叠PRI箱的思想,以PIDM矩阵中PRI箱交叠范围内的幅度之和替代元素幅度,以此进行主周期行、子序列行和门限计算,并对门限的计算方式进行了改进,对过门限的值进行聚类分析,以簇中均值(子序列提取)或最小值(主周期提取)作为真实PRI值.研究结果表明,改进算法在基本保持了原算法分选成功率的基础上,具有一定的抗抖性(5%~10%),并也能适应于多部辐射源的复杂环境.

基于重频熵的自适应方位聚类算法研究

针对传统方位聚类算法由于采用固定门限而难以准确对密集方位目标进行准确分选的难题,提出一种基于重频熵的自适应方位聚类算法。算法首先定义了重频熵数学模型,然后采用一系列不同的方位门限对脉冲进行聚类,并依据重频熵对不同方位门限的脉冲聚类结果进行度量,最后选取最优方位门限对脉冲进行聚类处理。仿真和实际数据验证表明,本算法可处理得到准确的聚类结果,与传统聚类算法相比本算法在密集信号环境下的聚类准确率可提升15%以上。

基于PDW多特征融合的辐射源信号分选方法

针对现有辐射源信号分选技术混叠信号处理困难、分选精度不高的问题,提出了一种针对脉冲信号描述字的多特征融合分选方法,采用分治思想,具备两级分选架构。第一步通过构建时空密度聚类模型,分离出在时频域上混叠的辐射源信号;第二步通过多参数交并比方法,进一步提升算法精度。与传统方法相比,所提方法能够有效分选出时频域混叠脉冲序列,受辐射源脉冲重复间隔变化造成的影响较小,相比传统方法具有更高的准确性。

重频组合脉冲干扰对雷达信号分选的影响分析

为提高雷达信号主动抗分选能力,从削弱分选算法对脉冲重复间隔分选性能的角度出发,分析了直方图法、PRI变换法的算法特征;在一定约束条件下合理设计脉冲干扰信号,并进行仿真验证.仿真结果表明,干扰信号可以改变分选算法对真实雷达重频的统计属性,使侦察系统产生错误或虚假的雷达信号重频,从而实现对高价值目标的电磁防护.

基于改进CFSFDP的雷达信号预分选方法

通过改进CFSFDP聚类方法对雷达信号进行预分选,针对原始CFSFDP方法对截断距离敏感的问题,提出直方图均衡化方法对局部密度进行均衡化,提升聚类结果鲁棒性;针对雷达信号参数交叠严重、密度分布不均衡问题,提出改进的可达距离计算方法及改进的类别分配机制,提升对复杂数据的聚类能力。通过仿真数据集及UCI标准数据集进行实验验证,采用调整兰德系数(ARI)、调整互信息(AMI)和F1-measure值对聚类结果进行评价,结果表明所提方法可有效处理复杂参数分布数据,相较于原始CFSFDP方法及经典聚类方法(DBSCAN,AP,K-means, OPTICS),聚类性能得到了提升。

Study on the role of JAK/STAT signaling pathway during chicken spermatogonial stem cells generation based on RNA-Seq

Spermatogonia! stem cells(SSCs) form the foundation for spermatogenesis and sustain male fertility.To explore the regulatory mechanisms of chicken SSCs generation,we obtained highly purified chicken embryonic stem cells(ESCs),primordial germ cells(PGCs) and SSCs by fluorescence-activated cell sorting(FACS).High-throughput analysis methods(RNA-Seq) were used to sequence the transcriptome level of these cells.Gene ontology and Kyoto encyclopedia of genes and genomes(KEGG) pathway enrichment were used to analyze RNA-Seq results.BMP4 was used to induce chicken ESCs differentiation to SSCs-like cells in vitro.The quantitative real-time(qRT)-PCR was used to detect the expression changes of the key genes.The results showed that 22 relevant critical pathways were found by RNA-Seq,one of them was the Janus kinase/signal transducer and activator of transcription(JAK/STAT) signaling pathway.Total of 103 related genes were detected in this pathway.Protein-protein interactions analysis found that 87 proteins were significantly related to 19 key proteins in this pathway.These 87 proteins were enriched in 21 biological processes and 18 signaling pathways.Moreover,during the differentiation of chicken ESCs to SSCs-like cells induced by BMP4 in vitro,JAK2 and STAT3 were activated.The qRT-PCR results showed that the expression trends of JAK2 and STAT3 were basically the same as in vivo.We concluded that JAK/STAT signaling pathway plays an important role in the process of chicken SSCs generation both in vivo and in vitro;it may achieve its function through multiple biological processes and other related pathways.

用于雷达信号分选的连通k近邻聚类算法

为了能够在密集且复杂多变的信号环境中进行实时有效的信号分选,需要一种具有较低复杂度且能够根据信号环境自动调整参数的聚类方法。在模糊聚类算法的基础上结合k近邻搜索,将λ邻域范围搜索变为λ邻域内k近邻搜索,提出了连通k近邻聚类(connected k-nearest neighbor clustering,CkNNC)算法。相比模糊聚类算法,所提算法时间复杂度降低而空间复杂度稍有增加。为使得该算法能够根据信号环境自动进行参数调整,提出了基于k距离图的阈值参数确定方法。所提算法具有时间复杂度低与阈值参数自动确定的特点,仿真结果表明所提算法与使用Calinski-Harabasz指标确定最佳阈值的低复杂度模糊聚类算法相比,分选效果差距不大、性能相近,而时间复杂度大幅下降。

机载雷达告警接收机发展现状及趋势

随着信息技术的发展和空战模式的改变,机载雷达告警接收机(RWR)成为现代战机不可缺少的电子战系统。为了更好地理解机载RWR,该文从接收机体制角度考虑,将机载RWR的系统架构划分成两个阶段,对每个阶段的特点和组成进行了分析。接着详细阐述了机载RWR的信号处理流程,并且梳理了与信号分选、信号识别和威胁评估相关的技术。最后,从实际运用出发,系统总结了机载RWR在复杂电磁环境中和应对新体制雷达中面临的挑战以及未来的发展需求。

基于复杂电磁环境的射频隐身方法研究

针对复杂战场空间对抗环境下射频传感器的射频隐身问题,开展基于复杂电磁环境的主动射频隐身方法研究。通过自身平台电子侦察设备感知敌方电磁信号分布特征,主动构建对己有利、对敌不利的复杂电磁环境,在敌方威胁侦察接收机的子信道内形成时频交叠信号,影响敌方电子侦察设备的信号检测和分选聚类处理,增加敌方侦察设备的测量误差,不能形成稳定的辐射源,降低信号的被截获概率。通过仿真分析可以看出:将复杂电磁环境当作干扰扰动信号,干信比在6 dB以上时,对侦察设备的频率、脉宽等参数测量和辐射源分选聚类影响较大。基于电磁态势感知结果主动构造的时频交叠的复杂电磁环境,可以根据作战任务和敌我双方电磁环境进行实时管控,实现主动射频隐身的优势。

基于深度分割的端到端雷达信号分选

针对传统的雷达信号分选方法严重依赖预置参数、先验信息和结构不灵活的问题,提出一种基于深度分割的端到端雷达信号分选方法。首先将采集到的脉冲描述字映射成脉冲序列图像,同时保留像素点对脉冲的索引;然后利用训练好的深度分割模型U-Net对脉冲序列图像分割获得像素点分类结果;最后根据像素点索引和像素点分类结果对所有脉冲进行搜索归类,整个过程采用端到端形式。实验表明,该方法能够加强对捷变参数的分选能力,如频率捷变雷达、脉组捷变频等雷达参数,以及对时频域混叠、脉冲丢失严重等未知电磁环境中信号的分选能力。

复杂环境下基于联合特征聚类的多跳频网台分选

为了从混叠信号中去除干扰并分选出各个跳频网台信号,提出一种基于联合特征聚类的多跳频网台分选算法。首先,对混叠信号进行短时傅里叶变换得到时频矩阵,并根据时频矩阵能量分布直方图进行自适应阈值去噪;其次,通过形态学滤波处理,去除扫频干扰;再次,进行连通域标记,计算各段信号的持续时长和平均能量,以此去除定频干扰和短突发干扰并组成各跳频段的联合特征向量;最后,利用MeanShift算法对各段信号的联合特征向量进行聚类分析,完成各个跳频信号的分选。仿真结果表明,相比传统跳频网台分选算法,所提算法对混叠信号具有更高的分选率、更强的抗干扰能力和更广泛的适用性。

基于PRI变换快速雷达信号分选方法

脉冲重复间隔(Pulse Repetition Interval, PRI)变换法对典型雷达信号分选效果良好,但运算量大的缺点使其难以在复杂电磁环境下实现实时信号分选处理,因而制约其广泛应用。基于PRI变换法原理和运算复杂度模型,提出了PRI谱高次谐波抑制和压缩PRI谱分辨率两种方法,对PRI变换法关键步骤进行优化,提升处理速度。仿真了6种典型重频类型雷达信号脉冲数据进行分选效果验证,结果表明所提方法能适应多种重频类型和复杂电磁环境信号分选,处理速度提升达几倍至几十倍,可明显提高PRI变换法的工程应用能力。

基于层次密度聚类和谱间隙的雷达信号分选

雷达信号分选是现代电子战中的重要环节.为了解决传统算法鲁棒性较差的问题,提出一种基于层次密度聚类和谱间隙的雷达信号分选算法.使用载频和脉宽参数进行层次密度聚类,根据重新定义的簇间距得到赋权邻接矩阵,计算赋权邻接矩阵的拉普拉斯谱间隙,通过k-means聚类的超参数k对信号进行分选.仿真实验结果表明:该文算法的平均分选准确率达0.9960、平均召回率达0.9560;相对于密度聚类(density-based spatial clustering of applications with noise,简称DBSCAN)和Meanshift算法,该文算法对杂乱脉冲、漏脉冲及超参数的干扰均有最强的鲁棒性.

一种基于PRI谱的自适应重频检测方法

脉冲重复间隔(Pulse Repetition Interval,PRI)变换方法能够适应典型重频(Pulse Repetition Frequency,PRF)类型雷达信号分选,但采用观察时间原则和谐波抑制原则生成检测门限分选过程中,容易出现多信号分选漏批情况。基于滑窗滤波技术原理,提出PRI谱滑窗滤波重频检测方法,实现多信号自适应分选,提高PRI变换方法对复杂电磁环境的适应能力。仿真了6种典型重频类型雷达脉冲数据进行分选效果验证,结果表明所提方法具备典型类型重频雷达信号分选能力,多信号及复杂信号环境适应能力强,信号分选及脉冲提取准确率高。

微液滴高通量筛选方法的研究与应用进展

在单细胞层面对生物功能进行高通量的分析和分选是对关键基因、元件、途径与细胞工厂进行优化的重要技术。基于微液滴的筛选方法因其低成本、超高通量等优势,已被广泛应用于生物、医药、食品和工业等各个领域。本文针对目前主流的荧光激活的液滴分选、吸光度激活的液滴分选,以及无标记液滴分选等微液滴筛选设备的进展进行综述,主要包括基于质谱、拉曼、核磁共振、电化学、图像识别等。并总结了近年来微液滴筛选设备在酶进化、微生物育种等领域应用成功的案例。此外还对不同的微液滴筛选设备的优势与面临的挑战进行了讨论,未来各种新的荧光探针的开发以及质谱等非标记检测方法的进一步发展,将是微液滴筛选设备的主要发展方向,在蛋白质工程、抗体工程、细胞分选及临床研究等方面具有重要的应用潜力。