Latency change estimation for evoked potentials via frequencyselective adaptive phase spectrum analyzer

This paper addresses the problem of detecting and estimating latency changes in evoked potentials (EP's). EP's have been widely used to quantify neurological system properties. Transient and time-varying changes in latency may indicate impending neurological injury. Traditional time averaging and correlation methods for EP latency estimation are inefficient under low signal-to-noise ratio (SNR) and/or strong periodic interference conditions. This paper proposes an adaptive phase spectral time delay estimation method to detect and estimate the time-varying latency changes when both the SNR and the signal-to-interference ratio (SIR) are low. A theoretical analysis and computer simulation demonstrate that the proposed method can track the time-varying latency changes effectively and accurately when both the SNR and the SIR are as low as -5 dB. The method is also suitable for real time detection and estimation of the latency changes.     

一种基于非线性变换的EP潜伏期变化自适应检测方法

该文依据分数低阶矩理论和诱发电位（EP）信号及噪声的低阶α稳定分布特性，提出了一种自适应检测EP潜伏期变化的新方法。这种方法基于sigmoid函数对误差信号en(k)进行连续的非线性变换，即抑制了EP信号中的低阶α稳定分布噪声，又有效保留了信号成分，在高斯和低阶α稳定分布噪声条件下具有很好的韧性，且无须动态估计信号噪声的α参数。利用这种方法动态检测EP潜伏期的变化，比以往的DLMS，DLMP和SDA等算法具有较高的估计精度和较快的收敛速度，是一种具有较高韧性的性能优良的EP潜伏期变化动态检测方法。     

基于改进粒子滤波的PSK信号时延和码元联合估计算法

提出了一种基于自适应重采样的粒子滤波算法用于对PSK信号的时间延迟进行估计,可以消除由于状态噪声方差设置过小而产生不准确的后验概率分布和设置过大引起的估计误差增大的问题.同时,考虑已有算法无法实现较小时间延迟准确估计的问题,提出了一种码元正向与反向检测相结合的算法,可实现一个码元周期内任意时间延迟的准确估计.另外,对载频偏差进行精确估计并补偿.仿真结果表明这种新方法与原算法相比能够实现更精确的时间延迟估计与更低码元检测误码率     

Joint time-delay estimation and adaptive recursive least squaresfiltering

A general estimation model is defined in which two observations are available: a noisy and a noise-filtered and delayed version of the transmitted signal. The delay and the filter must be estimated. The joint estimator is composed of an adaptive delay element operating in conjunction with an adaptive transversal filter. The delay is updated using a form of derivative, with respect to the delay, of the sum of squared errors. The adaptive delay is limited to integer values and is defined as the lag. The lag value is computed and updated so that the optimum least-squares solution is attained. The joint algorithm is obtained by combining the lag update relations with a version of the fast transversal filter RLS algorithm. Simulations show that both stationary and time-varying delays are effectively tracked and that the adaptive filter properly estimates the reference filter impulse response     

基于辅助源和相关熵的ADS-B信息时延估计新算法

提出了一种基于辅助源和相关熵的ADS-B信息时延估计新算法。首先,对报文信息时延产生机制进行了研究,提出新的时延模型;其次,以辅助源信号所包含的多普勒频移特征为研究对象,在信号相似性比较时引入相关熵提出时延估计算法;然后,分别研究了扫描步长与高斯核长对本时延估计精度的影响;最后,通过仿真实验验证了在不同噪声条件下本算法的时延估计性能。与已有方法相比,本算法不需要更改报文格式,在脉冲噪声条件下具有更高的时延估计精度,故具有更好的实际应用价值。     

An adaptive pre-distorter for the compensation of HPA nonlinearity

A high power amplifier (HPA) is used for the amplification of transmitting communication signals. However, it produces distortions by creating AM/AM and AM/PM modulations in the transmitting signal, Accordingly, this nonlinearity results in bandwidth expansion and nonlinear distortion in the in-band signal. This paper proposes an algorithm for the operation of a pre-distorter, which is composed of a look-up table (LUT), that can compensate for the distortion produced by an HPA. For the fast initialization of the LUT, an estimation algorithm is also proposed for the HPA characteristics. Furthermore, an adaptive algorithm based on the minimization of the mean square error is proposed to compensate for the time-varying property of an HPA. The performance of the proposed algorithm is analyzed by applying the algorithm to an 8-level vestigial sideband (VSB) modulation to be used in the ATSC terrestrial digital TV system     

Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials

Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the improved performance of our new algorithm.     

Adaptive Filterng of Evoked Potentials

We present an adaptive filtering (AF) technique for rapid processing of evoked potentials (EP). The AF algorithm minimizes the mean-square error (MSE) between successive ensembles. We demonstrate theoretically that the filter output converges to the least square estimate of the underlying signal. Computer simulations with known signal and added noise show that AF produces lower MSE than ensemble averaging. We also compare results of AF to those obtained by ensemble averaging for some EP recorded from animals and humans. For very noisy EP recordings, we propose techniques that combine AF and ensemble averaging. The AF technique shows promise for requiring fewer ensembles than averaging to attain adequate signal quality.     

采用合成方法的多路信号自适应时延联合估计

本文关注的是多路信号之间时延差异的联合估计问题。不同于传统的自适应时延估计算法,本文以合成信号作为自适应时延估计的参考信号,给出了基于信号合成的联合自适应时延估计算法。同时本文推导和仿真了该算法时延估计的均值、学习曲线及方差特性。性能分析和仿真结果均显示,本文提出的基于合成的多路信号自适应时延估计为渐进无偏的时延估计。在不明显增加计算量的条件下,当算法收敛时,联合时延估计算法的方差显著低于传统的两路信号之间自适应时延估计算法方差。      

Nondata-aided error vector magnitude based adaptive modulation over rapidly time-varying channels

A novel nondata-aided error vector magnitude based adaptive modulation(NDA-EVM-AM) was proposed to solve the problem of lower spectral efficiency over rapidly time-varying wireless channels.Namely,NDA-EVM was considered as a metric to reflect the rapid change of time-varying channels.The unified model to calculate different modulation order of NDA-EVM was analytically derived,with which the relationship between NDA-EVM and bit error rate (BER) for each modulation order was presented.Thereafter,the mechanism to adaptively select the modulation orders of multilevel quadrature amplitude modulation (MQAM) signals was designed to guarantee the predefined BER.Taking the two rapidly time-varying channels proposed for high-speed railway scenarios as examples,numerical results are conducted to verify the effectiveness of the proposed algorithm.It shows that NDA-EVM estimation has the lest root mean square error than data-aided error vector magnitude (DA-EVM) estimation and signal to noise ratio estimation.The proposed algorithm has better accuracy in aspects of channel quality estimation and modulation orders adjustment,Compared with conventional data-aided error vector magnitude based-adaptive modulation (DA-EVM-AM),the accuracy improves by 7.9%,spectral efficiency improves by 0.53 bit·s^?1·Hz^?1,and compared with signal to noise ratio based-adaptive modulation (SNR-AM),the accuracy improves by 15.7%,spectral efficiency improves by 0.82 bit·s^?1·Hz^?1.     

Stochastic analysis of the LMS algorithm with a saturationnonlinearity following the adaptive filter output

This paper presents a statistical analysis of the least mean square (LMS) algorithm with a zero-memory scaled error function nonlinearity following the adaptive filter output. This structure models saturation effects in active noise and active vibration control systems when the acoustic transducers are driven by large amplitude signals. The problem is first defined as a nonlinear signal estimation problem and the mean-square error (MSE) performance surface is studied. Analytical expressions are obtained for the optimum weight vector and the minimum achievable MSE as functions of the saturation. These results are useful for adaptive algorithm design and evaluation. The LMS algorithm behavior with saturation is analyzed for Gaussian inputs and slow adaptation. Deterministic nonlinear recursions are obtained for the time-varying mean weight and MSE behavior. Simplified results are derived for white inputs and small step sizes. Monte Carlo simulations display excellent agreement with the theoretical predictions, even for relatively large step sizes. The new analytical results accurately predict the effect of saturation on the LMS adaptive filter behavior     

An enhanced technique for online discrete cosine transform-based time-varying delay estimation

Time-delay estimation is developed in the transform domain where discrete cosine transform (DCT) coefficients of time-varying delay signals are estimated. The DCT is very efficient in compacting the signal energy in a small number of coefficients. Hence, the estimation of time-varying delay is obtained through the calculation of a small number of the DCT coefficients. An online maximum likelihood estimator of time-varying delay is developed. The coefficients of the transformed delay signal are obtained by maximizing the likelihood function. When dealing with noisy signals, it is observed that increasing the number of estimated DCT coefficients beyond a certain level does not produce better estimates of the delay signal. This happens because the extra DCT coefficients accommodate the noise present in the received signals.     

Wideband Blind Source Separation Algorithm Based on Beamforming

As the problem of array mixing model of wideband signals cannot be solved by conventional blind source separation algorithms, an improved algorithm based on beamforming is proposed in this paper. First, the received signals are transformed into time–frequency domain, and the delays of source signals are estimated. Then, the received signals are compensated with the estimated delay in frequency domain. Finally, the desired signal is acquired by using Frost wideband beamforming algorithm. Due to adopting the new methods of single source point extraction and delay estimation, the complexity of the proposed algorithm is reduced. Pre-steering delay is used in frequency domain to eliminate the compensation error when the delay is not an integer multiple of the sampling interval, which improves the separation performance significantly. The simulation results show that the proposed algorithm can adequately solve the problem of delay mismatch and achieve wideband blind source separation effectively. The existing algorithms are mostly fail for frequency hopping signals when there are numerous overlapping time–frequency points. In this case, the proposed algorithm still has good separation performance.

     

时频干涉仪到达角估计性能分析

传统干涉仪测向是对单个脉冲信号测向的，对于多信号没有分辨能力，对于线性调频等时变频率信号也不能直接应用。本文提出了一种时频干涉仪算法以实现对宽带线性调频信号的到达角（DOA）估计；同时该算法可实现多信号分辨；讨论了通道误差对算法性能的影响；分析表明，通道增益不一致不会造成DOA估计错误，而通道时延的不一致将造成DOA估计错误；给出了通道时延误差校正算法，通过校正可实现DOA的正确估计；计算机仿真结果证实了分析的正确性。     

一种基于分数低阶协方差的自适应 EP潜伏期变化检测方法

由于诱发电位(EP)信号中含有分数低阶α稳定分布噪声,致使传统的基于二阶统计量的EP潜伏期变化检测方法性能显著退化.本文提出了一种基于分数低阶协方差的自适应EP潜伏期变化检测方法(AFLC),通过对带噪信号的非线性处理,把其转变为二阶矩过程,从而保证了算法在α∈(0,2]范围内的可靠收敛,并获得了EP潜伏期变化估计的较高韧性和精度.给出了性能分析,计算机仿真和实验数据分析的结果.     

On-line estimation of signal and noise parameters with application to adaptive Kalman filtering

This paper presents two algorithms for on-line estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and colored measurement noise have been assumed. The proposed adaptive Kalman filtering algorithms have been tested for various spectra of the pure signal and noise, and for various signal-to-noise ratios. The effect of the length of an adaptation step and a sampling frequency on the mean square errors of the pure signal estimation has also been examined. Although the test have been performed for stationary signals, the algorithms presented can also be used successfully for time-varying sensor signals when the signal-to-noise ratios vary very slowly in comparison with the length of the adaptation step.The results are helpful for designers who synthesize optimal linear digital filters for sensor signals with first-order spectra and colored measurement noise. The estimation error curves presented enable designers to determine the noise reduction attainable for particular applications of the adaptive Kalman filtering algorithms.     

基于时延估计算法的透明转发器的实现方法

讨论了转发器实现收发不间断的方法,提出了在自适应噪声相消的系统上,将简化的分数阶傅里叶变换理论应用于时延估计,进而将干扰信号重构抵消。推导了该算法,并提出基于该算法实现收发同时进行的转发器系统,即透明转发器。给出本系统模型框图,该透明转发器采用最小均方(LMS)算法建立自适应系统控制结构,能够通过自适应滤波器将自发干扰信号减除,并将不相关的背景噪声抵消。最后利用 MATLAB 软件仿真了基于该算法的透明转发器在具体信号上的运用,实验结果表明该方法实现了不间断转发功能,并且系统结构简单、易实现。     

宽带DOA估计的频域时延补偿算法

针对宽带波达方向（Direction of Arrival,DOA）估计中导向矢量的频率不一致问题,提出一种新算法.首先对搜索方向信号进行时延补偿,使其与法线方向信号具有相同的阵列时延特征,同时将其它方向信号当作噪声处理,然后计算子空间的正交性并将其作为该搜索方向上空间谱值,最后给出快速算法以降低运算量.由于滤波器的群时延值不能任意改变,采用频域方法实现时延补偿.新算法不需要预估信号源数目和DOA初值,且仿真结果表明：在信号源互不相关和功率谱密度分布平坦的前提下,新算法分辨率更高、估计误差更小.     

分数低阶α稳定分布下DLMP算法的收敛特性分析

DLMP算法是一种在高斯和分数低阶α稳定分布噪声环境下均具有良好韧性的EP信号潜伏期变化检测算法.本文基于分数低阶统计量的原理,根据确定性平均方法,结合文中给出并证明的两个引理,对DLMP算法的收敛性能进行了理论分析和证明.结果表明,若EP潜伏期变化为EP信号采样间隔的整数倍,则DLMP算法对这种变化的估计是无偏估计.若整数倍的条件不满足,则DLMP算法的估计偏差不大于半个采样间隔.     

基于一步特征值分解的T-ESPRIT算法

在分析了时延估计算法的特点和性能之后,提出了基于一步特征值分解的T-ESPRIT(OT-ESPRIT)算法,此算法只需一步特征值分解就可以求解无线网络环境中多径窄带信号的时延,与T-ESPRIT算法相比,该算法具有较好的顽健性和较小的估计误差。仿真结果证明了该算法的有效性。