Frame Detection Based on Cyclic Autocorrelation and Constant False Alarm Rate in Burst Communication Systems

Frame detection is important in burst communication systems for its contribu- tions in frame synchronization. It locates the information bits in the received data stream at receivers. To realize frame detection in the presence of additive white Gaussian noise （AWGN） and frequency offset, a constant false alarm rate （CFAR） detector is proposed through exploitation of cyclic autocorrelation feature implied in the preamble. The frame detection can be achieved prior to bit timing recovery. The threshold setting is independent of the signal level and noise level by utilizing CFAR method. Mathematical expressions is derived in AWGN channel by considering the probability of false alarm and probability of detection, separately. Given the probability of false alarm, the mathematical relationship between the frame detection performance and EJNo of received signals is established. Ex- perimental results are also presented in accor- dance with analysis.

Constant False Alarm Rate Acquisition Algorithm for Compass B1C Signal

In order to improve the sensitivity of the Compass B1C signal acquisition for the receiver,the principle of constant false alarm rate(CFAR)is applied for the B1C pilot channel acquisition to realize the dynamic adjustment of the threshold of acquisition against the carrier to noise ratio.The non-coherent data/pilot combined acquisition algorithm for B1C signal is analyzed to make full use of the power of the B1C signal under the condition of low carrier to noise ratio.On this basis,to improve the acquisition sensitivity of the receiver,the principle of constant false alarm probability is applied for the non-coherent data/pilot combined acquisition algorithm.Theoretical analysis and simulations show that the non-coherent data/pilot combined acquisition algorithm with CFAR improves the B1C signal acquisition sensitivity of the receiver significantly,and achieves a better Receiver Operating Characteristic compared with the traditional acquisition algorithms.

Performance of Order-statistics Constant-false-alarm-rate Detector with Noncoherent Integration and Its Application to OTH Radar

Noncoherent integration is often ed for approving performance in detection of radar signal. Order-statistics constant false alarm rate (OS-CFAR) detector has some advantages in clutter and multiple target situations. AnOS-CFAN detector with noncoherent integration after Square law envelope detector is presented and an analysis of detection performance for the chi-Square family of Swerling fluctuating targets is given. Its application to the high frequency(HF) ground wave over-the-horizon (OTH) radar is discussed as well.

A Double-threshold Constant False Alarm Rate Detector And Its Performance Analysis

he cell averaging and the order statistics are two typical algorithms for constant false alarm rate detector in radar system. They have different advantages in stationary noise background and fluctuation clutter environment respectively. This paper presents a doublethreshold constant false alarm rate detector constructed on the basis of synthesizing the advantages of the two algorithms above and avioding their disadvantages. The performance of the detector is analyzed, and the simulation result is given.

基于ML估计的高动态GNSS信号快速捕获检测方法

针对高动态环境下GNSS因频域带宽增加导致捕获难度增大的问题,分析了接收端数字中频采样信号的传输特性及复基带信号经FFT模块处理后的相关峰的检测,提出了基于极大似然(ML)估计的高动态GNSS信号快速捕获检测方法。首先,根据随机信号的统计理论建立二元假设检验条件,构建了奈曼-皮尔逊准则下的GNSS信号捕获判决门限模型;其次,通过判决量的统计特性对等效高斯白噪声方差进行ML估计,根据其估计值计算捕获判决门限,其中通过虚警率的量化放大处理,解决了判决量样本值的增加带来的估计偏差问题;最后,对不同高动态条件下北斗B3I信号进行了捕获检测仿真实验。结果表明采用ML估计方法确定捕获判决门限从而提高高动态GNSS信号捕获的检测方法对高动态适应范围较宽,其频移捕获精度与SINS信息辅助捕获相当,比序贯检测算法提高约28%以上,相同条件下具有更快的平均捕获检测速度。

基于模式物理量参数的云南雷暴大风概率预报技术研究

利用2019—2021年云南125个国家站大风数据和云南省地闪资料,统计云南雷暴大风个例,并挑选对雷暴大风有重要意义的物理量参数,结合NCEP再分析资料确定云南雷暴大风个例的物理量阈值,再基于ECMWF数值模式预报产品及确定的阈值,采用二分法进行云南雷暴大风概率预报。结果表明:在云南三次雷暴大风过程预报检验中,8月4日云南中部及东北部雷暴大风均命中,但在云南西部及西南部出现大范围的虚警,导致此次过程虚警率较高和临界成功指数较低;8月5日云南中部、东北及西北部雷暴大风预报正确,且云南西部及南部虚警范围小,虚警率较8月4日明显降低;7月7日云南自东北向西南出现大范围的雷暴大风天气过程,雷暴大风预报落区与实况基本吻合,呈现命中率高、虚警率较低的特征;三次过程命中率、临界成功指数、虚警率平均为0.873、0.203、0.789。

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。

A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized nature.Intrusion detection is one of the key methodologies utilized to ensure the security of the network.Conventional intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system model.In this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot IoT.In this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant features.The proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO algorithm.This results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness estimation.As a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

基于威胁机制-双重深度Q网络的多功能雷达认知干扰决策

针对传统深度Q网络(deep Q network,DQN)在雷达认知干扰决策中容易产生经验遗忘,从而重复执行错误决策的问题,本文提出了一种基于威胁机制双重深度Q网络(threat warning mechanism-double DQN,TW-DDQN)的认知干扰决策方法,该机制包含威胁网络和经验回放2种机制。为了验证算法的有效性,在考虑多功能雷达(multifunctional radar,MFR)工作状态与干扰样式之间的关联性的前提下,搭建了基于认知电子战的仿真环境,分析了雷达与干扰机之间的对抗博弈过程,并且在使用TW-DDQN进行训练的过程中,讨论了威胁半径与威胁步长参数的不同对训练过程的影响。仿真实验结果表明,干扰机通过自主学习成功与雷达进行了长时间的博弈,有80%的概率成功突防,训练效果明显优于传统DQN和优先经验回放DDQN(prioritized experience replay-DDQN,PER-DDQN)。

自动光学检测技术在引线键合中的应用

微电子产品内部结构复杂,引线数量多且分布无规律,给人工检验带来了巨大挑战。自动光学检测具有效率高且漏检率低的特点,可实现产品引线键合正确性的自动化检验。对引线键合的自动光学检测原理进行深入分析,结合实际生产,着重介绍键合点编号技术。实现了引线键合的自动化检验,确保引线键合的正确性,并且极大降低了误报率。

基于多核PC的软件雷达信号积累和恒虚警处理研究

积累的本质是提高信噪比,以利于信号检测;恒虚警率处理的本质是在保证一定检测概率的基础上,用一个自适应门限代替固定门限,以保持虚警率的恒定,同时减小后端处理的压力,二者都是雷达信号处理系统的重要组成部分。在软件化雷达思想的指导下,针对雷达视频信号的积累和恒虚警率处理的实时性问题进行了研究。首先给出了积累和单元平均类恒虚警率处理的仿真模型,然后对积累和单元平均类恒虚警率处理的运算复杂度进行了详细的分析,接着采用集成性能原件(IPP)算法库和常规计算方法对积累和单元平均类恒虚警处理的运算时间进行仿真研究,最后通过实际软件实现,表明了在一定条件下,软件实现积累和恒虚警率可以满足实时性要求。

一种机载组网雷达协同目标检测算法

多机协同的机载雷达组网联合目标探测可有效提高复杂电磁干扰环境下对隐身弱目标的探测能力。文中针对机载雷达组网探测时空间配准误差大、协同探测难以实现的难题,提出了一种基于轨迹空间配准的协同目标检测算法,通过雷达间少量距离-多普勒域数据及低检测门限下目标轨迹域数据的交互,采用极大似然估计广义似然比检测器对目标进行联合恒虚警检测(CFAR),并通过轨迹域空间配准与CFAR的迭代计算,实现配准精度和目标联合检测性能的双提升。数值仿真实验的结果表明,在四部雷达组网工作时,在相参积累后信噪比9 dB、虚警概率10-4的典型场景下,经过迭代处理,空间配准精度可达到一个距离-多普勒分辨单元;对目标的检测概率由单部雷达的28.5%提高到四部雷达协同下的83.67%。

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。

考虑衰减补偿的频域反射单元平均恒虚警率电缆缺陷识别方法

针对目前频域反射(FDR)法通过人为主观经验判断缺陷,存在误判率高的问题,提出了一种基于单元平均恒虚警率(CA-CFAR)的电缆缺陷识别方法。首先对FDR测试信号在电缆中存在传播衰减,导致缺陷及末端在定位谱中归一化幅值过小而测不准的问题,先使用实测数据求出衰减因子与测试频率的线性关系,再使用在频域解卷的衰减补偿方法,有效削弱了信号在电缆传播过程中衰减过大带来的负面影响;其次通过恒虚警率(CFAR)的动态阈值来提取电缆中缺陷的位置及归一化幅值信息,其中动态阈值根据测试信号幅值的变化动态地选取缺陷判断的阈值,避免了人为主观经验判别容易误判的问题;最后分别在30 m、105 m和500 m实验室电缆以及1500 m在运电缆上对该方法进行测试,测试结果表明采用该方法可以准确地识别电缆全部缺陷,证实了该方法的有效性。

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。

Switching variability index based multiple strategy CFAR detector

A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.

2017—2021年西藏高原SWC-WARMS模式降水预报产品的检验与评估

选用2017—2021年西藏高原39个观测站24 h降水资料,对SWC-WARMS模式降水预报产品按照数值预报业务检验评估指标进行检验与评估。结果表明:西藏高原降水主要集中在暖季(降雨时段),以小雨为主。SWC-WARMS模式24 h累积降水预报年平均准确率、命中率和成功率分别为78%、83%和60%,预报效果较好,但整体的空报率和偏差均较大。西藏高原暖季的小雨及以上TS平均值为0.63,中雨及以上TS平均值较小,其空报率、漏报率随海拔高度落差增大而增大;冷季(降雪时段)降水日数较少,TS小于暖季、空报率高,但个别区域的SWC-WARMS模式大量级降雪预报TS超过0.30。

基于双重控制策略的故障检测方法

针对复杂工业过程中变量故障信息统计不完全和故障检测性能不佳的问题,提出一种基于双重控制策略的故障检测方法。对输入数据进行标准化处理并获得数据的偏差变量来揭示数据的故障信息,实现第一重控制;对偏差变量进行处理生成新的辅助监控统计量,实现第二重控制;针对辅助监控统计量阈值难以确定的现象,基于一种反馈调节的参数自适应策略设置阈值。使用提出的方法对田纳西伊斯曼过程(TE过程)进行故障检测,且与改进欧氏距离控制(IEDC)和传统主成分分析(PCA)的故障检测方法进行比较验证。结果表明:与IEDC和PCA方法相比,所提方法能监控TE过程中变量更多的故障信息,在多组类型的故障检测中具有更高的故障检测率与较低的误报率,可有效应用于复杂的工业过程。

储罐自动化仪表计量误差在线识别方法研究

针对站场油气储罐计量仪表的检测误差,以现场366座储罐正常计量数据为基础,设置0.5%~2.5%的误差率,构建数据驱动方法数据集,综合考虑误报率和窗口大小(25~125),针对7类算法(WIN-G、WIN-M、WIN-L、RuLSIF、KL-CPD、VAE和LSTM-VAE)进行综合性能评价,用现场检测的误差数据和正常数据进行算法验证,结果表明:在窗口大小100的条件下,LSTM-VAE算法性能最佳,正确报警率高于0.95。