一种改进的认知无线电循环功率谱特征检测算法

通过对认知无线电频谱检测技术的研究,结合通信信号普遍存在的循环平稳特性,本文首先较为详细地阐述了循环功率谱的基本概念,随后提出了可应用于认知无线电频谱检测的基于二阶循环平稳特性的循环功率谱特征检测算法,并依据认知无线电的实际需求对检测算法进行了修正.在此基础上,以现代通信系统较为常用的QPSK调制信号进行了检测算法的仿真,并根据仿真结果对算法的有效检测性能进行了详细的分析,实际应用也验证了检测算法的可行性.     

基于能量检测的频谱感知方法研究

认知无线电是一种用于提高无线通信频谱利用率的新的智能技术,检测频谱空穴是否存在是实现认知无线电的前提和关键技术之一.首先简述认知无线电的背景和概念,针对认知无线电的频谱感知功能,介绍了基于能量检测的频谱检测方法,并在Matlab环境下进行了仿真实验,比较在相同的虚警概率情况下的检测概率与信噪比的关系.仿真实验结果表明,在相同的虚警概率时,当信噪比大的时候,检测概率越大.     

基于能量和循环谱的两步频谱感知

频谱感知是认知无线电的重要技术之一,它通过实时感知电磁环境以判断频谱空穴的存在。根据能量检测的检测时间短,以及循环平稳特征检测在低信噪比情况下的检测准确性高的优点,提出了基于能量和循环谱的两步检测算法。通过实验仿真表明,与循环平稳特征检测相比,两步检测的检测时间大幅度缩短,同时检测准确性也得到了提高。     

认知无线电中一种基于循环谱的能量检测算法

能量检测方法存在检测精度不高以及对噪声功率的不确定性敏感等问题,在认知无线电中应用时极易造成极大地虚警或漏检.为了提高认知用户接收主用户信号的检测精度,本文在详细分析循环谱理论的基础上提出了一种基于循环谱的接收信号功率检测算法,并以MPSK信号为例给出了该算法详细的理论分析.计算机模拟仿真结果表明:无论是在高信噪比还是在低信噪比条件下,基于循环谱的接收信号功率检测算法的检测精度相比传统的能量检测方法均有较大的性能增益.     

基于循环谱能量的自适应频谱检测算法

根据信号循环平稳谱的特征,研究在低信噪比环境下的频谱检测问题,提出一种基于循环谱能量的自适应判决门限频谱检测算法。该算法融合能量检测与循环平稳特征检测的机理,以信号的循环谱能量为检测统计量,加权合并虚警率与检测率,准确估计循环谱特征值,构建了具有噪声自适应能力的频谱检测判决门限。仿真结果表明,该算法可以在低信噪比环境下有效地完成频谱检测,克服了噪声波动对频谱检测性能的影响,对不同调制主信号的感知具有稳健性。与最大—最小特征值算法和盲检测算法相比,该算法分别改善了信噪比4dB和8dB。     

基于认知无线电的煤矿井下频谱感知技术的研究

对认知无线电的频谱感知技术进行分析,同时结合煤矿巷道环境特点提出煤矿巷道认知无线电感知模型.提出的单节点采用能量检测和循环平稳特征检测联合算法,既弥补了能量检测在低信噪比下检测概率低的缺点,又改善了循环平稳特征检测需要较长时间的问题.     

瑞利信道中自适应双门限频谱感知算法研究

频谱感知是认知无线电中的一项关键技术,能量检测、匹配滤波器检测和周期循环频谱检测是认知无线电中频谱感知技术中传统、经典的算法.基于能量检测的频谱感知算法因实现简单且不需要先验信息而被广泛应用,但低信噪比的情况下检测概率会降低,用户的检测性能不足,从而导致认知用户和主用户之间的冲突碰撞次数增多.针对以上存在的问题,在基于自适应频谱感知的基础上,利用噪声功率估计,提出了一种瑞利信道下的自适应双门限频谱感知算法.仿真结果表明,在低信噪比噪声不确定的情况下,这种算法能够大大提高检测概率,有效降低了认知用户和主用户之间的冲突碰撞次数,从而提高频谱的利用率.     

基于信噪比墙的协作能量检测算法

认知无线电技术可有效地检测到授权频段的频谱空洞,从而提高频谱效率.能量检测由于不需要授权用户的先验信息而被广泛应用.然而由于接收的噪声存在不确定性,使得在信噪比低于某一闸值时,无论观测时间多长,都无法保证检测结果满足要求的检测性能,这一闸值被称作“信噪比墙”.本文通过信噪比墙这一现象进行分析,同时由于协作感知算法在确定噪声下在提高检测性能方便表现出的优势,提出一种基于信噪比墙的协作能量检测算法,通过仿真结果分析,表明本文算法在检测性能和节能上较已有的协作算法具有优势.     

基于二阶循环累积量的多载波信号频谱检测

针对认知无线电系统中多载波信号OFDM/OQAM进行循环谱特征检测预处理要求严格的问题,提出一种以二阶循环累积量的循环频率为特征,结合循环平稳性检测的频谱感知改进算法.算法无需各种参数估计以及同步等预处理过程,理论推导及仿真结果证明,该算法能够实现低信噪比下主用户信号的有效感知,且相比于能量检测法能达到更好的检测性能.     

基于综合检测的多调制格式信号频谱感知

频谱感知是认知无线电领域的关键技术之一.分析了频谱感知发射机探测部分的两大主要手段:能量检测和静态循环平稳检测的理论依据和实现机理,并将其作为一个有机整体应用于不同调制格式的发射信号检测中,根据系统有效性和可靠性指标(虚警概率、复检概率、检测概率)探索并优化相关参量(能量检测双门限、静态循环功率谱检测门限),最后仿真分析得到检测概率对接收端信噪比的变化趋势,得到不同调制格式下的兼顾可靠性和有效性的检测效果.     

Multisensor towed array detection system for UXO detection

The multisensor towed array detection system (MTADS) was designed to be an efficient, sensitive tool for the detection and characterization of buried unexploded ordnance. It comprises arrays of total-field magnetometers and time-domain electromagnetic induction (EMI) sensors, associated navigation and data acquisition hardware, and a custom data analysis system. The MTADS has conducted eleven demonstrations and surveys. The system has shown the ability to detect ordnance at its likely self-penetration depths with a probability of detection of 0.95 or better The model-derived positions and depths of the detected ordnance items are generally well within the physical size of the targets, making remediation much quicker and less costly than with standard techniques. Data sets corresponding to many of the MTADS surveys are available to others in the field     

TD-CDMA系统中的联合检测技术--联合检测算法

多用户检测（MUD）技术作为CDMA系统的关键技术之一，具有优良的抗多址干扰（MAI）和抗远近效应性能。在介绍传统的多用户检测算法的基础上，引入了线性联合检测器，深入分析和比较了TD-CDMA系统中的联合检测算法，并根据TD-CDMA系统上行链路传播模型，对TD-CDMA上行链路的联合检测算法进行仿真。最后，根据仿真结果，比较了三种线性联合检测算法的性能。     

Robust noise detection for speech detection and enhancement

A simple and robust method of reliably detecting stationary noise periods in a mobile telephony environment is presented. Such noise detection techniques are useful for updating parameters in adaptive voice activity detectors and for speech enhancement techniques, such as spectral subtraction, which require accurate noise models     

3D saliency detection based on background detection

Unlike 2D saliency detection, 3D saliency detection can consider the effects of depth and binocular parallax. In this paper, we propose a 3D saliency detection approach based on background detection via depth information. With the aid of the synergism between a color image and the corresponding depth map, our approach can detect the distant background and surfaces with gradual changes in depth. We then use the detected background to predict the potential characteristics of the background regions that are occluded by foreground objects through polynomial fitting; this step imitates the human imagination/envisioning process. Finally, a saliency map is obtained based on the contrast between the foreground objects and the potential background. We compare our approach with 14 state-of-the-art saliency detection methods on three publicly available databases. The proposed model demonstrates good performance and succeeds in detecting and removing backgrounds and surfaces of gradually varying depth on all tested databases.     

被动探测中的信号检测技术研究

被动探测中,低信噪比下检测信号的方法备受关注。本文讨论基于总和的检测方法。采用该方法就能将信号和噪声在时域中区分开来,并能较精确地确定信号的起止时刻。该方法具有实时性好、检测概率高的特点。仿真结果表明这种方法切实可行。     

Nonparametric detection

This paper considers some of the simpler nonparametric detection schemes and compares their asymptotic relative efficiencies to those of detectors which are optimal in the Neyman-Pearson sense. In the one-input case, the nonparametric sign and Wilcoxon detectors are compared to the linear detector which is optimal for the detection of a dc signal of unknown amplitude in Gaussian noise. For two-input systems the nonparametric polarity coincidence correlator is compared to the system which is optimal for the detection of a common random Gaussian component in two-input Gaussian noises. The nonparametric detectors are shown to offer advantages in simplicity of implementation and in insensitivity to changes in input statistics while performing moderately well compared to the parametric detectors. More impressive results can be obtained with more complicated detectors utilizing nonlinear rank statistics.     

Image forgery detection algorithm based on U-shaped detection network

Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.     

激光主动侦测系统探测能力分析

激光主动侦测系统可实现远距离猫眼目标的成像探测与识别,探测能力分析是系统设计的前提和基础.为了评估激光主动侦测系统对猫眼目标的探测能力,以激光回波功率为基础,建立了系统信噪比的数学物理模型.分析了影响探测能力的因素,数值模拟了信噪比与接收光学镜头直径、发射激光峰值功率、激光束散角以及等效反射面离焦量的定量关系.结果表明,在一定作用距离范围内,散粒噪声、背景噪声与暗电流噪声、热噪声相差至少2个数量级,系统噪声主要来源于散粒噪声及背景噪声;提高系统探测能力最为有效的方法是选用激光束散角小的光源.这一结果可用于指导激光主动侦测系统的设计.     

Sequential detection versus conventional detection: A comparative study

M-ary sequential detection algorithms are discussed in terms of the advantage measure A, the number of possible signals M, and the signal-to-noise ratio SNR. The advantage measure A is defined as the logarithm of the ratio of the error probability of the optimum conventional detector to that of the optimum sequential detector with the same values of M and SNR. It increases as the SNR increases but decreases when M increases. For M → ∞, the advantage measure A diminishes to zero, so that application of the sequential approach is useless for large values of M. Thus, the sequential approach is most useful for binary detection with high signal-to-noise ratios. These results are demonstrated for detection of equiprobable orthogonal signals received in white Gaussian noise.     

表面裂纹深度检测的非接触光声检测技术研究

提出了一种基于激光超声宽带透射系数和的表征参数,实现了表面裂纹深度的定量检测。通过有限元仿真与实验测量方法,从时域和频域分析了激光产生的瑞利波与表面裂纹的相互作用机制,采用宽带透射系数和作为表征裂纹深度的物理参数,并建立了其与表面缺陷深度之间的拟合方程。结果表明,脉冲激光产生的瑞利波对金属表面裂纹深度变化十分敏感,所提出的计算方法可用于精确判断表面裂纹的深度。本文研究成果可为工件表面裂纹的非接触检测提供潜在应用。