不同时频分析方法综合检测信号

本文讨论了不同时频分析方法的性能,线性时频分析方法稳健,但精度有限;非线性时频分析方法如:威格纳分布精度高,但是不稳健,对噪声敏感,多信号情况下存在交叉项的问题。本文将线性和非线性的时频分析方法结果按照与的规则进行综合,得到了稳健的高分辨的时频分布结果。     

Frequency Domain DTV Pilot Detection Based on the Bussgang Theorem for Cognitive Radio

In this paper, a signal detection scheme for cognitive radio (CR) based on the Bussgang theorem is proposed. The proposed scheme calculates the statistical difference between Gaussian noise and the primary user signal by applying the Bussgang theorem to the received signal. Therefore, the proposed scheme overcomes the noise uncertainty and gives scalable complexity according to the zero‐memory nonlinear function for a mobile device. We also present the theoretical analysis on the detection threshold and the detection performance in the additive white Gaussian noise channel. The proposed detection scheme is evaluated by computer simulations based on the IEEE 802.22 standard for the wireless regional area network. Our results show that the proposed scheme is robust to the noise uncertainty and works well in a very low signal‐to‐noise ratio.     

脉冲噪声环境中基于clipping方法的鲁棒LPFT及其重排算法

为了更好地处理脉冲噪声环境中的时变信号,本文提出了基于clipping方法的鲁棒局部多项式傅里叶变换(LPFT)及其重排算法。首先利用clipping方法对信号中掺杂的脉冲噪声进行抑制,得到较好的信号时频分布表示,然后将重排算法与该鲁棒LPFT相结合,以提高信号的时频聚集性。通过实验仿真可以看出,与基于中值滤波器的鲁棒LPFT相比,基于clipping方法的鲁棒LPFT同样能对被脉冲噪声干扰的信号给出较好的时频表示,而且其瞬时频率估计的最小均方误差(MSE)较低,计算量较小。并且,本文在基于clipping方法的鲁棒LPFT对掺杂脉冲噪声的信号进行处理的基础上,利用重排算法与其结合,有效增强了信号的时频聚集性。因此基于clipping方法的鲁棒LPFT及其重排算法是一种高效的处理脉冲噪声干扰信号及提高信号时频聚集性的方法。     

多径传播条件下宽带线性调频信号波达方向估计方法

针对信号出现多径传播情况时现有宽带信号波达方向（direction of arrival, DOA）估计方法性能下降的问题,提出了一种多径传播条件下宽带线性调频（chirp）信号波达方向估计方法,该方法将导向有效投影（steered effective projection, STEP）技术与宽带线性调频信号的时频特性相结合,对具有不同时频特性的信号分量进行分离,逐个处理,并以时频分布矩阵代替传统的协方差矩阵,从而构造有效噪声子空间,实现时域角度估计。本方法无需进行信号聚焦操作,因此理论上不受聚焦误差的影响。仿真结果验证了所提方法的有效性。      

An iterative particle filter signal detector for MIMO fast fading channels

For flat fast fading Multiple-Input Multiple-Output （MIMO） channels, this paper presents a sampling based channel estimation and an iterative Particle Filter （PF） signal detection scheme. The channel estimation is comprised of two parts： the adaptive iterative update on the channel distribution mean and a regular update on the ＂adaptability＂ via pilot. In the detection procedure, the PF is employed to produce the optimal decision given the known received signal and the sequence of the channel samples, where an asymptotic optimal importance density is constructed, and in terms of the asymptotic update order, the Parallel Importance Update （PIU） and the Serial Importance Update （SIU） scheme are performed respectively. The simulation results show that for the given fading channel, if an appropriate pilot mode is selected, the proposed scheme is more robust than the conventional Kalman filter based superimposed detection scheme.     

A Robust Acquisition Scheme for FH Signal in Adverse Environments

Both partial-band jamming and multi- tone jamming have severe effect on the acquisition of frequency-hopping (FH) signal in adverse environments. In this paper, an anti-jamming FH signal acquisition scheme based on cognitive correlation process is proposed to boost the robustness of acquisition. The main idea of this scheme is to utilize a priori knowledge of FH speed and FH pattern to distinguish jamming signal from received signal. Furthermore, theoretic analysis on detection probability and false probability is given to demonstrate the robust performance of the FH signal acquisition method compared with conventional acquisition scheme without any prior information on FH speed and pattern in adverse environments.     

采用自适应功率回归门限设定的HF雷达舰船探测

高频（HF）雷达能够探测和跟踪极远距离上的目标。但是，包含外部噪声、各种不同杂波和干扰的信号环境将大大限制探测性能和系统能力。文中研究了一种新方法来解决复杂HF雷达信号环境中的目标探测问题。该方法采用传统的恒虚警率（CFAR）检测方法，但其门限设置是基于沿距离和多普勒单元功率谱值的回归分析。就中，CFAR检测试验规则与局部峰值测量方法相结合。文中所提探测方法已经真实HF雷达数据试验，得到了很有前景的结果。     

改进的基于分形盒维数的共信道多信号存在性检测算法

针对时频重叠的共信道多信号存在性检测问题,提出了一种改进的基于分形盒维数的算法。算法用瞬时幅度的盒维数作为检测统计量,通过理论推导得出噪声瞬时幅度的盒维数为一定值1.415,共信道多信号瞬时幅度的盒维数近似等于1,并以此得出了检测的理论门限。如果接收信号瞬时幅度的盒维数小于设定的检测门限则说明有信号,否则没有信号。共信道多信号为MASK、MPSK、MQAM和MFSK任意类型混合时,仿真结果表明在加性高斯噪声背景下算法准确有效,信噪比大于-2dB时检测概率达到100%且虚警概率极低。另外,算法对信号调制类型、调制参数、信号源个数具有很好的鲁棒性,计算简单复杂度低、可实时处理。相比于已有研究,本算法门限值的设定更加精确,检测性能有大幅提升。      

时频域重叠多信号智能检测方法研究

针对现有基于深度学习理论的信号智能检测方法大多只能对单信号或时频域不重叠的信号进行检测,本文提出了一种基于掩膜区域卷积神经网络(Mask R-CNN)与Criminisi算法的时频重叠多信号智能检测新方法.首先将一维时域信号通过时频变换得到二维时频图像.然后针对时频图中多信号重叠部分像素位置信息缺失这一问题,提出了利用...     

基于时频图深度学习的雷达动目标检测与分类

雷达动目标检测技术一直是雷达信号处理领域中的关键技术,而传统的雷达动目标检测技术仅适用于匀速运动目标,检测性能有限。针对该问题提出一种基于卷积神经网络(CNN)时频图处理的雷达动目标检测方法,通过从雷达动目标回波中提取多普勒频移信息,然后利用短时傅里叶变换转换为时频图,输入卷积神经网络,进行深度特征学习,进而实现检测和分类的目的。仿真数据验证表明,所提方法能够有效检测和区分匀速、匀变速运动以及微动目标,稳健性高,与传统动目标检测方法相比具有显著优势。     

结合时变滤波和时频掩码的语音增强方法

本文提出了一种结合鲁棒时变滤波和时频掩码的语音增强方法。首先在带噪语音的时频域中,结合图像处理方法估计出初始瞬时频率信息。然后基于该瞬时频率信息,利用鲁棒时变滤波算法构建降噪后的语音信号。最后根据重构语音的时频特征预测时频掩码。该掩码在带噪语音的时频域中能够有效地保留语音成分且抑制噪声成分,从而达到语音增强的目的。实验结果表明,在几种常见背景噪声环境下,所提语音增强算法在抑制背景噪声干扰、提升语音整体质量方面表现良好,尤其是在低信噪比环境下具有明显的优势。      

Locally robust array detectors for random signals

Detection of random signals in arrays of receivers is considered when the exact distribution of the additive noise is not known. Locally robust correlator-type test statistics are obtained for weak-signal detection, for multivariate array noise densities in classes described by three different extensions of the univariate Tukey-Huber contaminated nominal density model. The robust correlator nonlinearities are shown to he of the form previously derived for known signal detection. In addition to robust correlator-type statistics, a quadratic-limiter-sum detector is investigated for robust performance in contaminated Ganssian noise.     

Low-cost sensorless control of brushless DC motors using afrequency-independent phase shifter

This paper describes a sensorless control scheme for brushless DC motors (BLDCMs) using a phase shifter (FlPS) which can shift the zero-crossing point of the input signal with a specified amount of phase. The detection performance of the proposed FIPS is independent of the frequency of the input signal and quite robust with respect to the measurement noise. It is shown that the proposed sensorless control scheme using the FIPS is more effective in the respects of noise-robustness and cost than the previously known schemes. Moreover, a flux-weakening control scheme can be easily incorporated into the proposed sensorless control scheme. The generality and practicality of the proposed sensorless control scheme is demonstrated through performance analysis and experiments under various operating conditions     

Blind Adaptive Multiuser Detection for the MC‐CDMA Systems Using Orthogonalized Subspace Tracking

In this paper, we study the performance of subspace‐based multiuser detection techniques for multicarrier code‐division multiple access (MC‐CDMA) systems. We propose an improvement in the PASTd algorithm by cascading it with the classical Gram‐Schmidt procedure to orthonormalize the eigenvectors after their sequential extraction. The tracking of signal subspace using this algorithm, which we call OPASTd, has a faster convergence as the eigenvectors are orthonormalized at each discrete time sample. This improved PASTd algorithm is then used to implement the subspace blind adaptive multiuser detection for MC‐CDMA. We also show that, for multiuser detection, the complexity of the proposed scheme is lower than that of many other orthogonalization schemes found in the literature. Extensive simulation results are presented and discussed to demonstrate the performance of the proposed scheme.     

基于分数阶Fourier变换的机载SAR运动目标检测

该文首先建立了机载合成孔径雷达(SAR)对运动目标的回波信号模型,阐述了其本质为线性调频信号的特点。根据这种特点,提出了一种基于分数阶Fourier变换的机载SAR运动目标检测新方法,有效地消除了线性调频信号的时频耦合特性对信号检测的影响。与双线性时频分布类算法相比,分数阶Fourier变换是线性变换,在多运动目标存在的情况下,不会受到交叉项的影响。仿真结果验证了算法的有效性。     

Time-frequency localised subspace tracking for narrowbandinterference suppression of DS/CDMA systems

A new narrowband interference suppression scheme based on time-frequency localised subspace tracking is presented. The received nonstationary signal is time-frequency-localised to extract stationary signal segments, on which reduced-order subspace tracking is applied to achieve interference suppression. Simulation reveals that this new scheme performs as well as the subspace decomposition scheme with a dramatically reduced computational complexity     

基于改进的半监督主动学习的雷达信号识别

为解决在雷达信号分类识别过程中训练样本较少的问题,本文提出了联合主动学习和半监督学习,并对其伪标记样本进行迭代验证改进的分类算法。针对复杂的电磁环境下雷达信号识别率低的问题,本文将径向高斯核时频分析应用于雷达信号,并对时频分布进行奇异值分解,提取出奇异向量作为雷达信号识别的特征参数。针对传统的半监督主动学习算法的不足,利用改进的半监督主动学习算法构建分类器,该算法通过对伪标记样本进行迭代验证来提高伪标记信息的准确性,从而改善了最终的分类性能,实现了在可获取的有标签样本数量较少的条件下对雷达信号的高概率识别。仿真结果表明,本文提出的特征识别方法可以获得较高的识别率。      

一种小波域新颖鲁棒语音扩谱水印

文章提出一种基于小波变换的新颖的鲁棒语音扩谱水印算法。算法对原始语音进行离散小波变换．利用扩谱水印技术将水印隐藏到小波域。根据检测与估计理论，运用新的检测统计量进行相关检测。同时，通过引入抗异步攻击的机制和倒谱滤波，增强了算法的鲁棒性。实验结果表明，该算法对噪声、中值滤波、低通滤波、异步攻击等有较强的鲁棒性。     

雷达动目标短时稀疏分数阶傅里叶变换域检测方法

复杂背景下的动目标检测技术是雷达目标探测的关键技术和难点之一,亟需发展和研究高时频分辨率、大数据量高效以及适用于多分量信号分析的方法和手段.该文结合经典时频分析技术和高分辨稀疏域信号处理的优势,提出短时稀疏分数阶傅里叶变换（ST-SFRFT）并用于雷达动目标检测和参数估计,实现时变信号高分辨时频表示的同时,改善SCR,提高复杂环境下雷达动目标检测的性能.实测对海雷达数据验证表明,所提方法在抗杂波以及参数估计精度等方面较经典时频动目标检测方法有明显优势.     

太赫兹频段下基于EMD的人体生命特征检测

非接触式的心跳呼吸信号检测对重症患者心跳呼吸的远程监控、自然灾害中受害者的搜寻等都有重要的应用。本文针对人体心跳呼吸信号相对于复杂环境属于微弱信号的情况,提出了一种太赫兹频段下基于经验模态分解(EMD)的人体生命特征检测方法。首先建立太赫兹雷达人体目标回波模型,对回波信号进行经验模态分解。然后进行时频分析,得到心跳呼吸微多普勒信息,提取其频谱质心曲线。再做第二次时频分析,实现心跳呼吸频率的提取与分离。在高斯杂波环境中进行了仿真实验,检测结果表明,基于EMD的检测方法与直接检测方法相比,取得了更好的检测效果,具有较强抗噪能力,适合于微弱信号处理。