认知无线电网络中利用FEC和DE的多节点频谱感知算法

针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知;然后,研究了信道噪声对频谱感知性能的影响;最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。     

一种深空背景空间小目标条痕检测算法

针对深空慢速运动目标(目标在焦面的运动速度小于1 pixel/frame)提出了一种最大似然条痕检测算法,能够在较低信噪比情况下实现有效的慢速目标检测。算法将目标脉冲形状信息引入信号模型中,是最大值投影算法的改进形式。建立基于高斯噪声分布的图像信号模型,在此基础上推导了最大似然条痕检测算法模型;分析该算法的实时性及其理论探测性能;采用蒙特卡罗仿真方法比较最大似然条痕检测算法与最大值投影检测算法的检测性能。仿真结果表明,输入信噪比为3.5时,最大似然条痕检测算法的探测概率为95%,其相同探测概率条件下所需信噪比比最大值投影算法降低了2.5(即最大值投影算法要达到95%的检测概率,所需信噪比为6)。算法实时性分析表明,最大似然条痕检测算法的实时处理能力为31.25 Mb/s。     

强杂波背景红外弱小目标检测算法

针对强杂波背景远距离红外弱小信号目标的特点,提出了一种基于自适应滤波的红外弱小信号检测方法。算法首先对图像进行消噪声处理,其次运用自适应滤波方式消除背景增强目标信号,最后进行基于点源目标(试验采集)成像信号特性的判决法则删除虚假目标,算法有效解决了光电探测设备高检测概率与低虚警率的矛盾。实验结果表明:该方法能够在单帧图像上有效提取出小区域信噪比为4的弱小信号目标,检测概率不低于0.75,虚警率不高于1次/100帧。     

低信噪比下激波信号奇点检测方法

针对低信噪比下的激波信号检测问题,利用激波信号奇点在小波变换中的路径传递特征及信号奇点小波系数与噪声小波系数明显幅度差异,提出了一种基于信号奇点的连续小波变换检测算法。仿真实验结果表明,在恒定虚警概率下,该方法检测性能相较于能量检测器能提升约3 dB;实际数据实验结果说明,该方法在-3 dB信噪比下检测性能明显优于能量检测器和传统小波多尺度积检测器。      

基于回溯筛选的稀疏重构时延估计算法

针对无线定位中时延估计在小样本(单快拍)、低信噪比条件下需要大量独立分布测量数据问题,提出了一种基于回溯筛选的稀疏重构时延估计算法,实现了单快拍、低信噪比条件下接收信号的精确时延估计.该算法首先建立接收信号的稀疏表示模型,然后基于该模型建立正交观测矩阵,最后在重构算法中引入回溯筛选思想,利用时延与观测矩阵之间的一一对应关系得到时延的无偏估计.对该模型下时延估计的克拉美罗界进行了推导.仿真分析表明,所提方法在单快拍、低信噪比条件下精度远高于求根多重信号分类算法,相比于正交匹配追踪算法,在较小的复杂度代价下性能得到了较大提升.     

变分模态分量降噪后重构的水声弱信号检测

针对能量检测法在低信噪比下对非合作水声探测信号的检测性能显著下降的问题,提出了一种组合变分模态分解和小波变换降噪重构的信号检测方法。以信号分解出的各个本征模态函数的近似熵与互相关系数比值作为分量分类参数,将所得分量分为信号分量、含噪信号分量与噪声分量,然后利用第二代小波变换对含噪信号分量降噪后与信号分量组成重构信号,最后对重构信号进行检测。数值仿真结果表明该方法可以在无先验信息的情况下对CW和LFM信号自适应降噪,信噪比0 dB以下时CW信号重构后信噪比提升约12 dB,宽带LFM信号信噪比提升约8～9 dB,有效提升了低虚警概率下信号的检测概率。湖试结果表明,虚警概率为0.1时检测概率可提升至0.9以上,验证了该方法的有效性。     

基于Gm-APD的成像激光雷达目标探测特性

针对采用盖革模式雪崩光电二极管（Gm-APD）作为探测器的成像激光雷达,介绍了其测距原理及3D成像原理,并对如何提高其探测性能的方法进行了分析。以分析Gm-APD触发信号的统计特性为基础,对出现在距离门内不同位置目标的探测概率和虚警概率进行了研究与仿真,结果表明,目标处在距离门最前面时,探测概率受噪声水平影响最小,虚警概率受信号强度影响最大;目标靠近距离门中间位置时,探测概率随噪声水平增大下降缓慢,虚警概率随信号强度增大下降缓慢;目标处在距离门末尾时探测概率受噪声水平影响最大,而虚警概率几乎与回波信号强度无关。     

接收机工作特性曲线在红外成像系统检测性能评估中的应用

提出了一种红外弱小目标检测算法性能评估的方法。该方法通过假设的数学模型给出了评价指标的理论推导过程,得到了虚警概率与检测门限,检测概率与信噪比之间的关系,绘出了接收机工作特性（ROC）曲线。同时,给出了在目标与背景分布函数未知的情况下,利用红外图像序列绘出ROC曲线来评估算法的检测性能。利用该方法得到了四种典型的弱小目标检测算法的ROC曲线,比较了四种算法的检测性能。     

基于谱图分解的宽带通信信号智能检测算法

对于宽带通信信号检测问题,针对目前基于深度学习的信号检测算法不适应于处理大带宽和大时宽的宽带信号以及对信号时频参数估计存在的固有偏差问题,提出基于谱图分解的宽带通信信号智能检测算法,完成对大带宽接收信号中窄带信号的高效准确检测。首先将由宽带信号转化而来的灰度时频谱图通过谱图分解得到适合于目标检测网络输入大小的子谱图,然后使用改进的无锚框YOLOx目标检测算法对子谱图中的窄带信号进行检测,最后将子谱图的信号检测结果融合得到窄带信号的时频参数等检测结果。经过实验测试得出,该算法能够适应复杂的噪声环境,与其他深度学习算法和传统算法相比,具有较高的信号检测概率,较低的虚警概率,较小的信号参数估计平均误差,其检测精度更高,鲁棒性、实用性、通用性更强。     

一种基于特征值修正的盖尔圆变换信息论判源方法

本文在盖尔圆-信息论(GAIC)方法的基础上,提出了一种基于盖尔圆半径修正的目标数目判断方法。将特征值和盖尔圆半径结合作为修正的特征值,并将修正后的特征值应用于GAIC方法。这种盖尔圆半径修正的GAIC(MGAIC)方法检测性能稳定,克服了AIC在高信噪比下不是目标数目的一致估计的缺点且在低信噪比时检测性能优于GAIC,尤其在目标角度间隔较小或多目标强度不等时MGAIC表现出明显的优势,适合于多目标对抗的复杂环境。仿真实验和实际数据处理结果验证了所提算法的有效性。     

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.     

TCVQ-SVM algorithm for narrowband spectrum sensing

Spectrum sensing is viewed as the basic and crucial technology for cognitive radio. To improve the accuracy of spectrum sensing in low signal to noise ratio (SNR), this paper presents an efficient TCVQ-SVM method based on machine learning for narrowband spectrum sensing. Firstly, trace of covariance matrix and variance of quadratic covariance matrix (TCVQ) is extracted as feature vectors and combined as training samples of spectrum sensing. Then, the classification model can be achieved by training samples based on support vector machine (SVM), which can avoid setting threshold and adjusting classification hyperplane by its self-learning ability. Lastly, the result of spectrum sensing can be obtained. By utilizing trace and variance as input features of SVM, the algorithm can make full use of the eigenvalue difference and structure characteristic of the received signal, and at the same time, achieve good performance in low SNR. Theoretical analysis reveals that the proposed method has low computational complexity. Simulation results and experiments on the hardware platform illustrate that the proposed algorithm is effective and robust.     

Quadratic covariance matrix-based cooperative spectrum sensing method by using an evolutionary algorithm

Cognitive radio (CR) is a practical technology to solve the current low utilization of spectrum resources, and spectrum sensing is the most critical technique in a CR network. In this paper, a genetic simulated annealing algorithm based on quadratic covariance matrix and information geometry is proposed for cooperative spectrum sensing (CSS) to enhance the performance in the low signal-noise ratio (SNR). Firstly, the quadratic covariance matrix of cooperative secondary users (SUs) is used as the characteristic matrix to perform feature extraction. Secondly, based on the information geometry, the characteristic matrix is mapped on the statistical manifold to avoid information loss. Furthermore, the genetic simulated annealing algorithm is used to obtain a classifier on the statistical manifold, and the mutation process is improved by a new mutation operator to accelerate the convergence speed of the whole algorithm. Finally, the classifier is employed to implement spectrum sensing. In the simulation analysis, the proposed method has better spectrum sensing performance than the popular various methods under low SNR and faster convergence speed.     

一种基于信号功率的水声通信线性调频同步信号自适应阈值检测算法

由于线性调频信号具有一定的抗多普勒效应能力,在水下突发通信中作为同步序列得到了广泛的应用。在对线性调频信号进行同步的过程中发现,当采用固定判决门限检测方法时,往往会造成检测概率的减小或者虚警概率的增大,针对这一问题,本文提出了一种根据信号功率,并在滑动窗口中进行比较判决,自动调整相关峰检测门限值的自适应算法。通过仿真和湖试验证,该方法性能良好,具有一定的实用价值。     

A cost-effective approach for spectrum sensing using beamforming

Spectrum sensing (SS) is one of the principal challenges on which the mobile communication is based on. Identifying the available frequency bands, also called white spaces, is the main issue. A novel blind approach for SS in the narrowband context is proposed in order to improve the signal detection. Considering a channel with its angle of arrival (AoA), we use beamforming technique to exploit the maximum and minimum angular energy. Both theoretical developments of the threshold and performance analysis are developed. To validate our contribution, the analytical results of the performance developed in this paper are compared with those from simulation. A comparison of state-of-the-art SS method using the eigenvalue decomposition is provided which brings an interesting trade-off between complexity and performance. Finally, simulation results considering the probability of misdetection under very low signal-to-noise ratio (SNR) are presented.     

二进制信号的混沌压缩测量与重构

二进制信号的压缩感知问题对应超奈奎斯特信号系统中未编码的二进制符号的检测问题, 具有重要的研究意义. 已有的二进制信号压缩测量采用高斯随机矩阵, 信号重构采用经典的l₁最小化方法. 本文利用混沌映射构造基于Cat序列的循环测量矩阵, 并提出一种针对二进制信号的全新的重构算法——平滑函数逼近法. 文章构造的混沌循环测量矩阵兼具确定性和随机性的优点, 能够抵御低信令效率和低信噪比的影响, 取得更好的压缩测量效果. 文章提出的平滑函数逼近法利用非凸函数代替原问题不连续的目标函数, 将组合优化问题转化为具有等式约束的优化问题进行求解. 利用稀疏贝叶斯学习算法进一步修正误差, 得到更准确的重构信号. 在信道含有加性高斯白噪声的条件下对二进制信号进行了压缩测量与重构的数值仿真, 仿真结果表明:基于Cat 序列的循环测量矩阵的压缩测量效果明显优于传统的高斯随机矩阵; 平滑函数逼近法对二进制信号的重构性能明显优于经典的l₁最小化方法.     

Simple and effective method to improve the signal-to-noise ratio of compressive imaging

This Letter presents a simple and effective method to improve the signal-to-noise ratio(SNR) of compressing imaging. The main principles of the proposed method are the correlation of the image signals and the randomness of the noise. Multiple low SNR images are reconstructed firstly by the compressed sensing reconstruction algorithm, and then two-dimensional time delay integration technology is adopted to improve the SNR. Results show that the proposed method can improve the SNR performance efficiently and it is easy to apply the a lgorithm to the real project.     

混沌信号的压缩感知去噪

对非线性时间序列进行噪声抑制是从中提取有效信息的前提. 混沌信号的去噪算法不仅要使滤波后的信号具有较高的信噪比, 也要具有较好的不确定性. 从压缩感知的角度出发,提出了一种新的噪声抑制方法. 该方法包括估计噪声方差, 以及依据动态的稀疏度将观测值往确定的过完备字典上投影. 仿真实验表明, 该方法比常用的小波阈值法和局部曲线拟合法具有更高的输出信噪比, 而原始信号的混沌特性也能得到较大程度的恢复.     

基于渐进添边的准循环压缩感知时延估计算法

针对时延估计问题中压缩感知类算法现有测量矩阵需要大量数据存储量的问题,提出了一种基于渐进添边的准循环压缩感知时延估计算法,实现了稀疏测量矩阵条件下接收信号时延的准确估计.该算法首先建立压缩感知与最大似然译码之间的理论桥梁,然后推导基于低密度奇偶校验码的测量矩阵的设计准则,引入渐进添边的思想构造具有准循环结构的稀疏测量矩阵,最后利用正交匹配追踪算法正确估计出时延.对本文算法的计算复杂度与测量矩阵的数据存储量进行理论分析.仿真结果表明,所提算法在测量矩阵维数相同的条件下正确重构概率高于高斯随机矩阵和随机奇偶校验测量矩阵,相比于随机奇偶校验矩阵,在数据存储量相等的条件下,以较少的计算复杂度代价得到了重构概率的较大提高.     

Acceleration target detection based on LFM radar

In radar systems, the echo signal caused by an accelerated target can be similarly considered as linear frequency modulation (LFM) signal. In high signal-to-noise ratio (SNR), discrete polynomial-phase transform (DPT) algorithm can be used to detect the echo signal, as it has low computation complexity and high real-time performance. However, in low SNR, the DPT algorithm has a large mean square error of the rate of frequency modulation and a low detection probability. In order to detect LFM signal in low SNR, this paper proposes a detection method, segment discrete polynomial-phase transform (SDPT), which means, at first, dividing the whole echo pulses into several segments with same duration in time domain, and then, using coherent accumulation method of DFT to segments, at last, processing this signal with DPT in intra-segment. In the case of a large number of segments, the SDPT can improve the output SNR. In addition, in a certain SNR, to the target signal with big sampling interval, large acceleration and less segments, this paper proposes an algorithm to detect the LFM signal generated from the combination of an improved DPT (IDPT) and fractional Fourier transform (FRFT). The output SNR of this algorithm is connected with the length of time delay. In the simulation, when the length of the time delay is 0.2 N, the output SNR is 2.5 dB more than that which results from directly using DPT. Finally, the detection performance and algorithm complexity of the proposed algorithm were analyzed, and the simulated and measured data verify the effectiveness of the algorithm.