Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions

In this paper, we propose techniques of surface electromyographic (EMG) signal detection and processing for the assessment of muscle fiber conduction velocity (CV) during dynamic contractions involving fast movements. The main objectives of the study are: 1) to present multielectrode EMG detection systems specifically designed for dynamic conditions (in particular, for CV estimation); 2) to propose a novel multichannel CV estimation method for application to short EMG signal bursts; and 3) to validate on experimental signals different choices of the processing parameters. Linear adhesive arrays of electrodes are presented for multichannel surface EMG detection during movement. A new multichannel CV estimation algorithm is proposed. The algorithm provides maximum likelihood estimation of CV from a set of surface EMG signals with a window limiting the time interval in which the mean square error (mse) between aligned signals is minimized. The minimization of the windowed mse function is performed in the frequency domain, without limitation in time resolution and with an iterative computationally efficient procedure. The method proposed is applied to signals detected from the vastus laterialis and vastus medialis muscles during cycling at 60 cycles/min. Ten subjects were investigated during a 4-min cycling task. The method provided reliable assessment of muscle fatigue for these subjects during dynamic contractions.     

The dual-bootstrap iterative closest point algorithm with application to retinal image registration

Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% image overlap.     

PCMA系统自干扰频率估计新算法

成对载波多址（PCMA）是一种新兴的频率重用技术,通过对自干扰信号的重构和抑制能够有效提高系统容量,其关键技术在于自干扰信号的参数估计。针对PCMA系统特点提出了一种无需单独训练的自干扰信号频率估计新方法,利用本地信号与对方信号的弱相关性,基于最小均方误差拟合准则拟合出相关函数的估计曲线,从而获得频偏估计值。仿真结果表明该算法能够得到比较小的估计误差和较为理想的误码率性能。     

中频通信信号信噪比的快速盲估计

信噪比是通信信号处理中的一个重要参数,许多算法都以它作为先验信息来获取最佳性能。该论文通过确定中频通信信号的频谱范围来计算信号能量,进而根据定义估计信噪比,由此提出了一种中频通信信号的信噪比快速盲估计方法。对BPSK,QPSK,16QAM等常用线性调制通信信号的仿真表明：该方法性能稳定,在数据样点长度为4096且信噪比变化范围为-5 dB到25 dB时,估计值的偏差和均方根误差基本都小于0.5 dB。与基于特征值分解的方法相比,在得到精确估计结果的同时具有计算复杂度小、适应动态范围广等优点。     

一种基于相位补偿的匹配变换算法

本文提出一种基于相位补偿的匹配变换算法,通过构造匹配函数使信号能量在变换域内达到聚集,从而有效地检测信号并估计出相应参数.本文以线性调频信号为例作了具体分析,并和其它方法进行比较,说明该方法具有更好的稳健性,较低的信号检测旁瓣以及良好的信号分辨能力.针对具有多个参数的复杂信号的检测及估计问题,本文将匹配变换和遗传算法相结合,有效估计出信号参数同时提高了运算速度.仿真结果验证了该算法的有效性.     

AWGN信道中非恒包络信号SNR估计算法

本文提出一种利用信号自相关矩阵的奇异值分解(SVD)对加性高斯白噪声(AWGN)信道下非恒包络信号信噪比的盲估计算法,并作了详细理论分析.通过对该算法进行计算仿真,结果表明:该算法性能稳定,对MPSK、MQAM信号在实际信噪比为1~20dB时,估计误差小于0.5dB.     

Fast frequency estimation and tracking using Lagrange interpolation

A computationally efficient and accurate frequency estimation and tracking algorithm is proposed, based on the adaptive frequency estimator (AFE) of Etter and Hush. A Lagrange interpolator (a fractional delay filter) is used to estimate the gradient of the performance surface of the adaptation, which enables highly accurate estimation. The performance of the new algorithm is demonstrated in the context of tracking a chirp signal in noise     

The multi-user detection under fading channel

This paper proposes a method of blind multi-user detection algorithm based on signal sub-space estimation under the fading channels in the present of impulse noise. This algorithm adapts recursive least square (RLS) filter that can estimate the coefficients using only the signature waveform. In addition, to strengthen the ability of resisting the impulse noise, a new suppressive factor is induced, which can suppress the amplitude of the impulse, and improve the ability of convergence speed. Simulation results show that new RLS algorithm is more robust against consecutive impulse noise and have better convergence ability than conventional RLS. In addition, Compared to the least mean square (LMS) detector, the new robust RLS sub-space based method has better multi-address-inference (MAI) suppressing performance, especially, when channel degrades.     

Doppler Spread Estimation for Broadband Wireless OFDM Systems Over Rician Fading Channels

In this paper, we present a new Doppler spread estimation algorithm for broadband wireless orthogonal frequency division multiplexing (OFDM) systems with fast time-varying and frequency-selective Rayleigh or Rician fading channels. The new algorithm is developed by analyzing the statistical properties of the power of the received OFDM signal in the time domain, thus it is not affected by the influence of frequency-domain inter-carrier interference (ICI) introduced by channel variation within one OFDM symbol. The operation of the algorithm doesn’t require the knowledge of fading channel coefficients, transmitted data, or signal-to-noise ratio (SNR) at the receiver. It is robust against additive noise, and can provide accurate Doppler spread estimation with SNR as low as 0 dB. Moreover, unlike existing algorithms, the proposed algorithm takes into account the inter-tap correlation of the discrete-time channel representation, as is the case in practical systems. Simulation results demonstrate that this new algorithm can accurately estimate a wide range of Doppler spread with low estimation latency and high computational efficiency.     

Frequency-domain blind deconvolution based on mutual information rate

In this paper, a new blind single-input single-output (SISO) deconvolution method based on the minimization of the mutual information rate of the deconvolved output is proposed. The method works in the frequency domain and requires estimation of the signal probability density function. Thus, the algorithm uses higher order statistics (except for Gaussian source) and allows non-minimum-phase filter estimation. In practice, the criterion contains a regularization term for limiting noise amplification as in Wiener filtering. The score function estimation, which represents a key point of the algorithm, is detailed, and the most robust estimate is selected. Finally, experiments point to the relevance of the proposed algorithm: 1) any filter, minimum phase or not, can be estimated and 2) on actual data (underwater explosions, seismovolcanic phenomena), this deconvolution algorithm provides good results with a better tradeoff between deconvolution quality and noise amplification than existing methods.     

A new correntropy based TDE method under <Emphasis Type="Italic">α</Emphasis>-stable distribution noise environment

This paper presents a robust time delay estimation algorithm for the α-stable noise based on correntropy. Many time delay estimation algorithms derived for impulsive stable noise are based on the theory of Fractional Lower Order Statistics (FLOS). Unlike previously introduced FLOS-type algorithms, the new algorithm is proposed to estimate the time delay by maximizing the generalized correlation function of two observed signals needing neither prior information nor estimation of the numerical value of the stable noise’s characteristic exponent. An interval for kernel selection is found for a wide range of characteristic exponent values of α-stable distribution. Simulations show the proposed algorithm offers superior performance over the existing covariation time delay estimation, least mean p-norm time delay estimation and achieves slightly improved performance than fractional lower order covariance time delay estimation at lower signal to noise ratio when the noise is highly impulsive     

基于信源数目估计的超定盲信号分离

针对盲信号分离中信道噪声大、信号分离效果差等问题,在传统主分量分析和特征值分解方法的基础上,提出了一种基于信源数目估计的超定盲信号分离方法。首先,采用主分量分析和最大似然估计方法分别对混合矩阵和噪声协方差进行估计,用于对信道噪声的估计与去除;然后,采用交叉验证法对源信号维数进行估计,实现盲信号分离。为了验证提出算法的分离效果,对轻拖尾与轻拖尾混合信号以及重拖尾与轻拖尾信号混合情况进行仿真实验验证,结果表明该算法具有良好的分离效果。     

改进K-means算法对AltBOC信号组合码序列的盲估计

针对恒包络交替二进制偏移载波(Alternate Binary Offset Carrier, AltBOC)调制信号组合码序列难以估计的问题，提出了利用改进K-means算法进行信号组合码盲估计方法。该方法首先通过引入互调分量以及重建副载波的方式构建AltBOC信号模型，然后在接收端将AltBOC信号分段成单倍组合码周期窗长的不重叠观测数据矩阵，并利用相似性原理从观测数据中选择最优的样本作为K-means聚类中的初始均值向量，最后通过K-means算法迭代优化数据样本与其聚类均值向量的平方误差，完成对AltBOC信号组合码序列的盲估计。计算机仿真结果表明，利用该算法在信噪比-15dB下能够较为精确地估计AltBOC信号组合码序列。      

正弦波信号频率估计快速高精度递推算法的研究

该文提出了一种正弦波频率估计的频偏校正算法,结合M-Rife算法精度高和频偏校正算法运算量小的特点,研究了一种快速高精度正弦波信号频率估计的递推算法。先对一个较短的截短信号序列用M-Rife算法进行频率初始估计,以此作初始值用频偏校正算法对一个更长的截短信号序列进行估计得到更精确的估计频率,并依此类推,在最后一步递推时,用M-Rife算法得到最终的估计频率。在信号序列较长时,该算法的运算量小于做一次FFT。仿真结果表明,该算法性能稳定,估计方差接近克拉美-罗限(Cramer-Rao Lower Bound,CRLB),与M-Rife算法相仿。该算法便于实时地实现高精度频率估计。     

采用动态窗口宽度和二次平均算法的SSOA定位方法

信号的中值估计是到达信号强度(SSOA)定位的核心,其误差主要来源于信道的快衰落和阴影(慢)衰落。信号平均是获得信号中值估计的重要手段,该文提出了二次平均和动态窗口宽度算法以提高中值估计精度,从而达到进一步提高定位精度的目的。计算机仿真结果表明,在衰落信道中,二次平均算法的定位精度比传统方法约高30％。采用动态窗口宽度后,可保证在移动台低速运动时仍具有较高的定位精度。     

Improved maximum frequency estimation with application to instantaneous mean frequency estimation of surface electromyography

The purpose of this study was to improve the maximum-frequency estimation. Three methods to estimate the maximum frequency of a bandlimited signal with additive white noise were compared. Two existing methods, the threshold-crossing method (TCM) and the hybrid method, were modified for time-frequency representations. A novel approach, the running-block threshold method (RBTM), was introduced. Based on calculation of detection probability (sensitivity) the RBTM improved the maximum-frequency estimate as compared with the TCM. The maximum-frequency estimation methods were also used to determine the integration interval for instantaneous mean-frequency (IMNF) estimation from synthesized surface electromyography containing white noise. Results showed that the IMNF estimate was improved by using any of the three methods and that the RBTM gave the best IMNF estimate.     

子空间算法在OFDM信道时延估计中的应用

到达时延(TOA)和到达时延差(TDOA)定位算法是无线定位中应用最广泛的2种方法,TOA测量值的准确获取将影响定位的精确度。正交频分复用(OFDM)是实现无线多媒体通信的关键技术,利用OFDM信号的导频子载波所携带的信息,参考均匀线列阵的DOA估计过程,提出将MUSIC子空间法应用到OFDM信道的时延估计上。给出了OFDM符号内导频子载波分布的相关条件,对提出算法进行仿真实验,可以看出,导频子载波个数的增加和导频子载波间隔的扩大都可以提高估计精确度。     

一种基于稀疏分解的窄带信号频率估计算法

针对窄带多分量信号频率估计问题,该文提出一种基于稀疏分解的频率估计算法,能够同时对多个窄带信号的频率进行估计。首先利用传统方法进行频率预估计,然后根据频率预估计的结果建立冗余字典,对信号进行稀疏表示,最后通过匹配追踪算法得到精确的频率估计。该算法极大地减小了字典的长度和稀疏分解的运算量,而且在迭代过程中利用了全局信息更新残差向量,估计结果更为精确,在低信噪比情况下性能也较为稳健。仿真结果验证该算法的有效性和正确性。     

一种改进的跳频信号参数估计方法

针对在跳频信号跳变时刻和跳变频率估计方面实时性和估计精度无法同时兼顾的问题,提出了一种基于短时傅立叶变换(STFT)和多重信号分类(MUSIC)算法的跳频信号参数估计方法。在建立跳频信号数学模型的基础上,利用STFT选取较大时间窗对整个信号在时域进行粗搜索,生成时频谱图,提取时频脊线从而获得跳变时刻,然后选取较小时间窗在已知跳变时间段利用STFT进行跳变时刻的细估计,并利用MUSIC算法进行频率的精确估计。该方法利用STFT的二次估计,减少了MUSIC搜索范围,从而降低了时间开销。仿真表明该算法的跳变时刻频率估计精度高,实时性能满足参数测量需求。     

加权多窗口字典的非线性调频信号参数估计方法

非线性调频(NLFM)信号经脉冲压缩后具有较低的旁瓣电平,广泛应用于脉冲压缩雷达中,实现对NLFM 信号的精确参数估计,对雷达对抗侦察具有重要意义。针对非线性调频信号的准确参数估计问题,文中通过构建加权多窗口字典并结合稀疏重构算法提出了一种凸优化基追踪信号重构方法,实现了对NLFM 信号的精确重构。通过筛选的Gabor 原子获得了信号的瞬时频率,利用反正切无模糊相位重构算法获得了信号的相位信息。经过仿真验证:该方法可以实现NLFM信号的精确重构。