音频无人机定位的时延估计模拟分析

针对无人机非平稳音频信号时差定位中,广义互相关时延估计算法抗噪性差和时延估计值精度低等问题,文章采用了一种基于广义二次相关时延估计的改进算法。算法对叠加了实际噪声（如风声、雨声、汽车鸣笛声等）的无人机音频信号进行频谱细化的广义二次相关,有效抑制了噪声干扰,融合相关峰精确插值算法,提高了互相关函数的分辨率,使得时延峰值更加明显。仿真实验结果表明,改进的广义二次相关方法在不同信噪比时,比广义互相关和广义二次相关算法的时延估计精度更高,稳定性更好。改进的广义二次相关算法对无人机定位中的时延估计具有更好的性能优势,具有较强的实际应用性。     

基于主动Lamb波和小波变换的二维结构损伤定位研究

基于主动监测技术，将结构健康和损伤状态下的Lamb波激励响应信号进行对比，两者的差信号实际上是激励信号通过损伤折射后被接收到的类似声发射源产生的一种信号。该信号与激励响应信号比较将有时间延迟，通过Gabor小波变换确定时间延迟，从而获取损伤位置信息。实验证实这种位置信息是可靠的。     

融合经验模态分解与时频分析的单通道振动信号分离研究

通过单通道振动信号分离研究探讨机械振动源信号提取问题。采用集合经验模态分解方法将单通道信号构造成多通道信号,再通过主分量分析方法得到多通道构造信号的特征值分布情况并以此进行源信号数目估计,进而利用基于时频分析的盲源分离技术获取振动源信号。实验表明,该方法能有效实现单通道振动信号分离,具有较强实际应用价值。     

矢量水听器阵时频MUSIC算法研究

时频MUSIC算法利用信号的时频分布构造空间时频分布矩阵,并用该矩阵代替传统的相关矩阵进行DOA估计,可以有效抑制噪声和干扰,提高算法的稳健性。时频子空间算法突破了传统子空间算法中阵元数对估计信号个数的限制,时频点包含了信号的时频空三维信息,通过时频点的选择可直接确定信号的频率从而确定阵列流型矩阵。对于宽带信号,在进行方位估计时避免了频域搜索,减少了运算量。将时频MUSIC算法应用于二维矢量水听器垂直线阵中,充分利用矢量水听器的标、矢量信息和信号的时、频信息进行宽带信号的二维波达方位估计。仿真研究验证了算法的有效性。     

New ISAR imaging algorithm based on modified Wigner?Ville distribution

Inverse synthetic aperture radar (ISAR) imaging of air, space or ship targets with complex motion has attracted the attention of many researchers in the past decade. Complex motion of targets induce cross-range scatterer-variant quadratic phase terms, which will degrade the cross-range resolution and affect focusing quality. A new algorithm is proposed for the ISAR imaging of complex moving targets. First, conventional range alignment, phase compensation and range compression are performed over the raw phase history data such that each range bin can be modelled as the sum of several linear frequency modulation or chirp signals. Secondly, a modified- Wigner?Ville distribution (referred to as M-WVD) approach is proposed, which is based on a scale transform in the time-frequency distribution plane and can effectively suppress the troublesome cross-term interference associated with WVD via coherent integration. Finally, the azimuth ISAR image can be obtained via a simple maximisation projection from the two-dimensional accumulated plot to the azimuth dimension. Compared with existing WVDbased ISAR imaging algorithms, the proposed method has the following features: better cross-term interference reduction achieved at no resolution loss, computationally more efficient with no expensive two-dimensional parameter search, and higher signal processing gain because of coherent integration during the whole imaging time. Bothnumerical and experimental results are provided to demonstrate the performance of the proposed method.     

改进的二阶统计量盲分离算法

利用二阶统计量(不同时延相关矩阵)的非平稳性和信号时序结构特征,能简单估计出线性瞬时混合的盲源信号。但随时延[τ]增大,仅利用某一个时延协方差均衡化,忽略了信号的时间变化特性,很难保证算法的性能。通过分析矩阵的平均特征,提出一种改进的基于二阶统计量盲源分离算法,对一组均衡化的时延相关函数进行等时延分段,并对等间隔段的两个时延矩阵分别求取均值,采用类似联合近似对角化,估计出最优化的酉矩阵,最终得到信源的稳健估计。性能指标分析和仿真实验证明,算法很好地解决原算法的不足,并成功分离出信号。     

自适应共振解调法及其在滚动轴承故障诊断中的应用

与AR模型、小波变换等故障诊断方法相比较,工程人员更多的是采用共振解调法对滚动轴承故障进行诊断,但诊断成功与否很大程度上依赖于滤波器中心频率及其带宽的选择。这里提出的诊断滚动轴承故障的自适应共振解调法避免了带通滤波器难以选择的困难。其核心思想是:不采用滤波的方式而是通过先对时间信号进行时频变换,然后从时频能量谱中自动提取时间能量信号的方式来达到将由于冲击引起的共振高频信号和高能量的低频信号分离。此外,给出了一个统一的框架从时频能量谱中自动提取类似于时间边缘的时间能量信号,即Lp范数准则。数值实验结果表明,自适应共振解调法能有效地诊断滚动轴承的外圈故障、内圈故障、滚动体故障,而且比传统的共振解调法的性能更优。     

A spline-based algorithm for continuous time-delay estimation using sampled data

Time delay estimation (TDE) lies at the heart of signal processing algorithms in a broad range of application areas, including communications, coherent imaging, speech processing, and acoustics. In medical ultrasound for example, TDE is used in blood flow estimation, tissue motion measurement, tissue elasticity estimation, phase aberration correction, and a number of other algorithms. Because of its central significance, TDE accuracy, precision, and computational cost are of critical importance. Furthermore, because TDE is typically performed on sampled signals-and delay estimates are usually desired over a continuous domain-time delay estimator performance should be considered in conjunction with associated interpolation. In this paper we present a new time-delay estimator that directly determines continuous time-delay estimates from sampled data. The technique forms a spline-based, piecewise continuous representation of the reference signal then solves for the minimum of the sum squared error between the reference and the delayed signals to determine their relative time delay. Computer simulation results clearly show that the proposed algorithm significantly outperforms other algorithms in terms of jitter and bias over a broad range of conditions. We also describe a modified version of the algorithm that includes companding with only a minor increase in computational cost.     

低信噪比环境下语音识别的鲁棒性方法研究

针对噪声环境下语音识别率急剧下降的问题，提出了一种基于语音时频域稀疏性原理的改进最小方差无畸变响应波束形成与改进维纳滤波结合的算法。该算法首先利用麦克风阵列语音信号的空间信息，通过基于时频掩蔽的改进最小方差无畸变响应波束形成器，增强目标声源方向的语音信号，抑制其他方向噪声的干扰，然后再使用改进的维纳滤波器去除残留噪声并提高语音可懂度，对增强后的语音信号提取梅尔频率倒谱系数作为特征参数，使用隐马尔可夫模型搭建语音识别系统。实验结果表明，该方法能够有效提高低信噪比环境下的语音识别率，具有较好的鲁棒性。     

广义解调时频分析方法在调制信号处理中的应用

介绍了一种新的信号处理方法-基于广义解调的时频分析方法,并将这种方法应用于调制信号的处理。广义解调时频分析方法采用广义解调将时频分布是曲线的信号变换为时频分布是平行于时间坐标轴的直线的信号,然后采用最大重叠离散小波包变换（Maximal overlap discrete wavelet packet transform,简称MODWPT）对广义解调后的信号进行分解,得到若干个瞬时频率和瞬时幅值都具有物理意义的单分量信号,再对各个单分量信号进行逆广义解调,进一步求出瞬时频率和瞬时幅值,从而得到原始信号完整的时频分布。采用广义解调时频分析方法对调幅-调频信号进行了分析,结果表明该方法能有效地提取调幅-调频信号的调制信息。     

基于GST-ECNN的运动想象脑电信号识别方法

在对脑电信号的解码研究中,存在着现有时频分析方法对高频信号处理能力有限,多通道信号信息冗余,常用卷积神经网络分类器ReLU激活函数受学习速率的影响较大,对不同层采用相同的正则化很难获得满意结果等问题。为此,提出了一种基于广义S变换特征提取和增强卷积神经网络分类相结合的方法,同时提出一种结合Relief算法和向前选择搜索策略的包裹式方法进行通道选择。结果表明,提出的方法利用较少的信号通道,具有更强的特征提取和分类的能力,在第Ⅳ届BCI的数据集I上取得最高98.44±1.5%的分类准确率,高于其他现有算法。该方法良好的分类性能不仅减少了计算消耗,也有效提高了分类准确率,对脑电信号特征提取和分类具有一定的参考意义。     

A time-efficient and accurate strain estimation concept for ultrasonic elastography using iterative phase zero estimation

In ultrasonic elastography, the exact estimation of temporal displacements between two signals is the key to estimating strain. An algorithm was previously proposed that estimates these displacements using phase differences of the corresponding base-band signals. A major advantage of these algorithms compared with correlation techniques is the computational efficiency. In this paper, an extension of the algorithm is presented that iteratively takes into account the time shifts of the signals to overcome the problems of aliasing and accuracy in the estimation of the phase shift. Thus, it can be proven that the algorithm is equivalent to the search of the maximum of the correlation function. Furthermore, a robust logarithmic compression is proposed that only compresses the envelope of the signal. This compression does not introduce systematic errors and significantly reduces decorrelation noise. The resulting algorithm is a computationally simple and very fast alternative to conventional correlation techniques, and the accuracy of strain images is improved.     

基于高阶累积量的高精度时延估计算法

针对海洋探测中由于接收信号信噪比低并存在各种噪声干扰导致时延估计精度低的问题,提出一种基于二次相关和高阶累积量的具有多种噪声抑制能力的高精度时延估计新方法——SC-HOCS法。该方法首先对两路接收信号进行自相关和互相关处理,抑制部分高斯噪声,然后利用高阶累积量一维切片法对信号进行处理,抑制相关高斯噪声和非高斯色噪声,通过对接收信号的上述处理提高信噪比,最后结合希尔伯特变换对相关峰进行锐化处理,进一步提高时延估计精度。与广义相关法、二次相关法及高阶累积量一维切片法相比,该方法能很好地抑制相关噪声并且能在更低的信噪比下获得较好的时延估计精度,同时该算法计算量较小,可满足对数据实时处理的需求。计算机仿真和水池实验验证了该方法的有效性。该方法为海洋探测中低信噪比信号的高精度时延估计提供一种新的技术途径。     

时延估计方法的分析

对周内外时延估计的最新研究现状及各种算法进行研究,在研究了大量时延估计文献的基础上,分析了广义相关法、广义相位谱法、自适应算法及其改进算法,分析了各种算法之间的关系以及各自的特点。重点论述了现代信号处理算法与时延估计算法相结合的各种时延估计方法及其在非平稳信号中的应用,同时,分析了带宽对时延估计精度的影响及宽带信号的时延估计方法,指出了时延估计算法还需要进一步研究的方向。     

Two bit correlation-an adaptive time delay estimation

Time delay estimation is a very important operation in ultrasound time-domain flow mapping and correction of phase aberration of an array transducer. As the interest increases in the application of one and a half-dimensional (1.5-D) and two-dimensional (2-D) array transducers to improving image quality and three-dimensional (3-D) imaging, the need of simple, fast, and sufficiently accurate algorithms for real-time time delay estimation becomes exceedingly crucial. In this paper, we present an adaptive time-delay estimation algorithm which minimizes the problem of noise sensitivity associated with the one bit correlation while retaining simplicity in implementation. This algorithm converts each sample datum into a two bit representation including the sign of the sample and an adaptively selected threshold. A bit pattern correlation operation is applied to find the time delay between two engaged signals. By using the criterion of misregistration as an indicator, we are able to show that the proposed algorithm is better than one bit correlation in susceptibility to noise level. Analytical results show that the improvement in reducing misregistration of the two bit correlation over its counterpart is consistent over a wide range of noise level. This is achieved by an adaptive adjustment of the threshold to accommodate signal corruption due to noise. The analytical results are corroborated by results from simulating the blood as a random distribution of red blood cells. Finally, we also present a memory-based architecture to implement the two bit correlation algorithm whose computation time does not depend upon the time delay of the signals to be correlated     

非线性调频分量分解的转子油膜涡动信号分析研究

转子轴承系统的振动信号常呈现非线性调频特征且信号分量在频域混叠,传统的频谱分析方法难以处理该类信号。基于参数化解调的非线性调频信号分解方法来分析油膜涡动、油膜振荡特征信号能够有效分解频域混叠的非平稳信号。首先通过优化频谱集中性指标来估计信号瞬时频率参数并用估计到的参数将非线性调频信号解调为平稳信号,最后用带通滤波器提取解调信号。仿真及实验信号通过该方法分析后的结果证明,所用非线性调频分量分解的信号分解方法能够有效提取转子轴承系统的油膜涡动、油膜振荡故障特征,从信号时频图及提取分量的时域图可以清晰看到油膜涡动、油膜振荡的发生发展过程,为早期油膜涡动判定提供依据。     

一种变步长的自适应特征值分解时延估计方法

在房间声场环境下基于传声器阵列的说话人定位中,时延估计算法是其中的关键步骤。与其它常用时延估计方法相比,自适应特征值分解（Adaptive Eigenvalue Decomposition,AED）时延估计算法因其优越的抗混响性能受到越来越多的关注。但在受噪声和混响干扰的语音条件下,传统的自适应特征值分解算法收敛速度较慢,对初值敏感。通过引进动量因式,提出了一种变步长的特征值分解算法,通过理论分析和仿真实验,证实了新算法的收敛性能要优于传统的特征值分解算法,节省收敛时间,使算法的整体性能有所提高。     

Fast acquisition implementation for high sensitivity global positioning systems receivers based on joint and reduced space search

In Global Positioning Systems (GPS), unique pseudorandom codes allow different satellites to share available frequency spectra without interference. In receivers, the locally generated codes (replicas) are correlated with received signals to extract a particular satellite?s signal and wipe-off its pseudorandom code. The signals received are generally misaligned with replicas. As a result, receivers search for signals using different codes, code phases, and additional residual sinusoidal demodulations at several possible frequencies. This search is called acquisition and it is computationally challenging for receivers operating under weak signal conditions. Software GPS receivers have been developed over the past several years and continue to be an area of extensive research. Faster, less complex, and more efficient algorithms are required for software GPS receivers. We present a fast algorithm for accelerating acquisition. Several frequency domain approaches proposed by authors earlier are combined to reduce computational complexity. These are (i) joint code, code delay and frequency search; (ii) limited code-phase search; and (iii) a shifting replica approach. Significant gains in arithmetic complexity are achieved. A novel method for restoring correlation degradation introduced by limited code phase search is also presented in this paper. Overall performance of the acquisition is illustrated on different platforms.     

用慢特征分析算法实现水声信号盲分离

在常规的水声信号盲处理研究中,通常都是用独立成分分析算法分离线性混合信号,而对于较复杂的非线性混合信号,独立成分分析算法无能为力。针对这种情况,提出将慢特征分析(Slow Feature Analysis,SFA)算法应用于水声信号非线性盲源分离领域。一般而言,对源信号做非线性混合变换后输出混合信号较源信号变化较快,而采用SFA算法可以从复杂的非线性混合信号中提取出变化缓慢的信号,通过仿真实验,分别对简单信号和复杂水声信号的非线性混合信号进行分离,通过将源信号与分离信号对比,发现SFA算法输出信号与源信号高度相似,验证了SFA算法在非线性盲源分离领域应用的有效性和可行性。     

一种新的估计多项式相位信号瞬时频率的参数化时频分析方法

通过多项式非线性核函数取代线性调频小波变换中的线性核函数,提出一种新的参数化时频分析方法：非线性调频小波变换。对瞬时频率是时间任意连续函数的信号而言,选择合适的多项式核特征参数,非线性调频小波变换的时频分布有良好的时频聚集性。应用非线性调频小波变换分析任意阶次多项式相位信号。由于非线性调频小波变换的性能取决于多项式核特征参数,本文还给出非线性调频小波变换的核特征参数估计算法,进一步可实现多项式相位信号的瞬时频率和参量估计。仿真信号验证算法的有效性。