基于三阶累积量对角切片的直接自适应谱线增强算法

依据随机平方相位耦合信号三阶累积量不为零的特点，提出了从高斯噪声环境中恢复平方相位耦合信号的基于三阶累积量对角切片的直接自适应谱线增强算法。该算法恢复平方相位耦合信号、抑制高斯噪声的性能优于传统的自适应谱线增强算法。仿真结果证实了这种算法的有效性。     

基于三阶循环累积量的二维谐波信号的参数估计

本文从循环平稳的观点出发来研究乘性和加性噪声中的二维谐波参数估计问题.利用循环累积量的性质得到了二维三阶循环累积量单一记录估计的统计性质.在此基础上,提出了基于三阶循环累积量特殊切片的二维谐波分量个数和频率对的估计方法,并得到了估计的强相容性质和强收敛速度.仿真试验对算法作了说明.     

基于三阶累积量的红外弱小运动目标检测新方法

为检测强杂波背景中的红外弱小运动目标,提出了一种基于三阶累积量的检测新方法.该方法利用图像中目标经过时像素点灰度值有起伏变化这一特点,将其看作是一种非高斯弱瞬态信号,通过构造三阶累积量对其进行检测.此三阶累积量估计中的去均值处理同时实现了背景杂波的抑制,较好地改善了信杂比(SCR),提高了单帧检测性能.实验表明,该方法能够有效地抑制强杂波背景,在SCR>1时,能可靠检测目标.     

基于Donoho模型和高阶统计理论的小波消噪算法研究及其应用

本文结合经典Donoho小波消噪模型,提出了基于高阶统计理论的小波消噪算法.算法在小波分解各尺度引入了高阶统计理论中的四阶累积量方法,从信号和噪声的统计特性出发进行信噪分离.仿真结果验证了本文算法的性能.算法能满足无线电监测系统中对信号进行后续处理的需求.     

基于切片谱的脑电特征分析

脑电信号包含与人脑的生理结构、状态等相关的大量信息。由于脑电信号很容易受到其他噪声的污染,并且其本身又具有很强的随机性,为了更好地提取脑电信号中的有用信息,运用三阶累积量切片谱分析法对临床实测脑电数据进行分析。仿真结果表明,该方法能有效抑制随机信号中的加性高斯噪声,并且能揭示不同状态下癫痫脑电信号中的非线性耦合现象,这表明该方法将为研究脑电信号提供一个新的途径。     

关联噪声和周期信号驱动的非对称双稳系统的稳态分析

运用Liouville equation和诺维科夫原理,解出了关联噪声和周期信号共同驱动的非对称双稳系统的近似福克-普朗克方程,并求解了其稳态概率密度函数.在此基础上,分析了乘性噪声强度 、加性噪声强度 、噪声间关联系数 ,周期信号振幅 、频率 以及系统非对称参数 等对稳态概率密度分布曲线的影响.结果表明:(1)噪声强度及其关联、周期信号振幅、系统非对称参数的改变均能引起稳态概率密度分布曲线单峰结构和双峰结构之间的转换,即能够诱导非平衡相变产生;(2)周期信号频率改变时,没有非平衡相变发生;(3)当系统非对称参数为零时,稳态概率密度分布曲线具有关于 的对称结构;当系统非对称参数不等于零时,其对称结构被破坏.     

基于三阶累积量的多径时延估计

研究多径传输条件下的时延估计问题。利用三阶累积量的一维切片作为高阶统计量,结合相关算法原理,提出一种新的时延估计算法。为提高时延估计精度,对相关数据进行了加权处理。该算法可有效抑制空间相关高斯噪声或对称分布噪声,得到非高斯信号准确的时延估计。算法具有计算量小,易于实现的优点。仿真结果表明了该算法的有效性。     

关联噪声和周期信号驱动非对称双稳系统的稳态分析

运用Liouville方程和诺维科夫原理,解出了关联噪声和周期信号共同驱动的非对称双稳系统的近似福克-普朗克方程,并求解了其稳态概率密度函数。在此基础上,分析了乘性噪声强度D、加性噪声强度Q、噪声间关联系数λ,周期信号振幅A、频率Ω以及系统非对称参数r等对稳态概率密度分布曲线的影响。结果表明:1)噪声强度及其关联、周期信号振幅、系统非对称参数的改变均能引起稳态概率密度分布曲线单峰结构和双峰结构之间的转换,即能够诱导非平衡相变产生;2)周期信号频率改变时,没有非平衡相变发生;3)当系统非对称参数为零时,稳态概率密度分布曲线具有关于x=0的对称结构;当系统非对称参数不等于零时,其对称结构被破坏。     

基于纹理特征的背景噪声提取的应用研究

 无线电频谱监测中,背景噪声的检测滤波和提取是影响频谱监测的最关键因素之一.文中提出了一种基于纹理特征的背景噪声提取方法,该方法采用平滑滤波阈值限定法实现背景噪声纹理特征的提取,从而实现背景噪声的提取,达到信噪分离的目的.通过大量的试验和工程应用证明,该方法对背景噪声提取的有效性不受硬件设备、频段背景噪声分布和信号带宽等影响,能够很好地提取背景噪声,实现信噪分离.     

基于高阶累积量的目标机动检测新方法

机动检测是多模型目标跟踪中的一个关键问题.在卡尔曼滤波中,当目标机动被噪声淹没时,传统的机动检测算法将失效,多分辨方法虽然能够有效地抑制噪声,可靠检测机动,但由于计算复杂导致严重的检测延迟,从而限制了它的应用.本文提出一种基于三阶累积量的机动检测新算法,它有效地克服了上述二者的缺陷.由于高阶累积量能够抑制高斯噪声,因此在三阶累积量域易于检测机动.同时通过采用逐点更新法,可实时进行机动检测.仿真结果表明,该算法优于传统算法和多分辨方法,特别是在低信噪比的情况下.     

Distributed multichannel speech enhancement with minimum mean-square error short-time spectral amplitude, log-spectral amplitude, and spectral phase estimation

In this paper, the authors present optimal multichannel frequency domain estimators for minimum mean-square error (MMSE) short-time spectral amplitude (STSA), log-spectral amplitude (LSA), and spectral phase estimation in a widely distributed microphone configuration. The estimators utilize Rayleigh and Gaussian statistical models for the speech prior and noise likelihood with a diffuse noise field for the surrounding environment. Based on the Signal-to-Noise Ratio (SNR) and Segmental Signal-to-Noise Ratio (SSNR) along with the Log-Likelihood Ratio (LLR) and Perceptual Evaluation of Speech Quality (PESQ) as objective metrics, the multichannel LSA estimator decreases background noise and speech distortion and increases speech quality compared to the baseline single channel STSA and LSA estimators, where the optimal multichannel spectral phase estimator serves as a significant quantity to the improvements, and demonstrates robustness due to time alignment and attenuation factor estimation. Overall, the optimal distributed microphone spectral estimators show strong results in noisy environments with application to many consumer, industrial, and military products.     

无线传感器网络中基于小波变换的分布式压缩感知技术

Wireless Sensor Networks (WSN ) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink. In this paper, we present a Distributed Wavelet Basis Generation (DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN. And on this basis, a Wavelet Transform-based Distributed Compressed Sensing (WTDCS ) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation. Finally, we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio, the channel Signal-to-Noise Ratio (SNR), the observation noise power and the correlation decay parameter by simulation. The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy, as compared to the conventional distributed wavelet transform algorithm.     

Probe: Noise-and-rotation resistance of Hopfield Neural Network in imaged traffic sign recall

This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.     

The method of separating harmonic signals from multiplicative and additive noises

This paper focuses on the extraction of a harmonic signal from multiplicative and additive noises. A method is proposed in two stages: (1) to square the original discrete time series, which in- cludes both signals and noises, and form a new time series. By this means, the multiplicative noise is converted to additive noise; and (2) to filter out the noise by using existing noise removal schemes. With a large amount of simulation, experimental results demonstrated the efficiency and effective- ness of this newly developed method in terms of Signal-to-Noise Ratio (SNR) and other criteria. From the experiment, it is also found that: the two kinds of noises affect the SNR differently. In gen- eral, the SNR is not influenced by multiplicative Gaussian noise regardless of its variance. However, if both kinds of noise exist, the SNR decreases with the incensement of the Variance of Additive Noise to Multiplicative Noise Ratio (VAMNR). This analysis is also supported by simulation work.     

基于噪声子空间奇异值的信号分量数目估计

针对现有信号分量数目估计方法在低信噪比时估计性能差的问题,该文提出了基于噪声子空间奇异值恒虚警检测方法估计信号分量数目。通过对Hankel矩阵奇异值与噪声能量之间关系的研究,利用高斯白噪声包络平方服从指数分布的特点,在虚警率一定的条件下,获得噪声子空间奇异值检测门限。仿真结果显示该方法在低信噪比时有效。     

QAM signals recognition based on Amplitude distribution

In this paper, a Quadrature Amplitude Modulation (QAM) signal recognition algorithm is proposed based on amplitude distribution of the signal. The algorithm uses envelop amplitude dis-tribution information extracted by wavelet analysis to do modulation classification. It provides ro-bustness for symbol rate determination. Simulation shows that it is more effective and convenient than the recognition algorithm of likelihood function at moderate Signal-to-Noise Ratio (SNR).     

噪声对室内听觉距离感知的影响研究

听觉距离感知是人耳的基本功能之一,研究其机理对于人工智能等领域具有重要意义。实际环境中的听觉距离感知除了受声场要素的影响外,还受到噪声的干扰,以室内环境为研究对象,利用主观听觉实验研究背景噪声、声源信号的信噪比对听觉距离感知规律的影响。     

噪声相关性偏态分布的最佳模型

提出了用对数正态分布描述噪声相关性偏态分布的最佳模型。首先通过理论分析和实验发现,在(0,1]区间β分布、Γ分布和对数正态分布可用于描述噪声相关性的偏态分布;然后分别利用这3种分布的概率密度函数(PDF)模拟实际的噪声相关性偏态分布曲线,曲线的形态与实际的噪声相关性偏态分布形态的相似程度的比较表明,采用对数正态分布描述噪声相关性偏态分布的效果最佳;最后利用最大熵方法证明了模型的最佳性。实验结果表明,本文提出的模型与另外两种模型相比,可使最小错误率降低70%以上。     

分布式全相参雷达相参性能分析

该文针对多脉冲发射协同结构的分布式全相参雷达,首先建立了全相参模式下的信号模型,然后推导了输出信噪比增益(oSNRg)的解析式,并基于相干参数估计的克拉美-罗界(CRB),利用多项式拟合,获得了oSNRg上界的数值解。结果表明:增加发射天线数或脉冲数能够提高oSNRg;而增加接收天线数能否提高oSNRg取决于输入信噪比大小,输入信噪比较大时则oSNRg随之提高,较小时反而随之下降。最后的仿真实验验证了研究结论的正确性。