A new method of nonstationary time series analysis based oninhomogeneous AR equation

We propose a new model for nonstationary time series analysis. The model is of a noise-contaminated signal of an AR system excited by a sequence of an input signal represented in terms of orthogonal functions. We also propose an algorithm that enables us to estimate parameters of the AR part and the input signal simultaneously. The models are finally evaluated by testing the recovery of an output signal. Examples of data analysis of the synthetic time series are shown for the ease in which the input signal is represented by a sequence of wavelets     

A weighted-principal component regression method for the identification of physiologic systems

We introduce a system identification method based on weighted-principal component regression (WPCR). This approach aims to identify the dynamics in a linear time-invariant (LTI) model which may represent a resting physiologic system. It tackles the time-domain system identification problem by considering, asymptotically, frequency information inherent in the given data. By including in the model only dominant frequency components of the input signal(s), this method enables construction of candidate models that are specific to the data and facilitates a reduction in parameter estimation error when the signals are colored (as are most physiologic signals). Additionally, this method allows incorporation of preknowledge about the system through a weighting scheme. We present the method in the context of single-input and multi-input single-output systems operating in open-loop and closed-loop. In each scenario, we compare the WPCR method with conventional approaches and approaches that also build data-specific candidate models. Through both simulated and experimental data, we show that the WPCR method enables more accurate identification of the system impulse response function than the other methods when the input signal(s) is colored.     

Signal and noise estimation from seismic reflection data using spectral coherence methods

Spectral coherence analysis is unrivaled as a quantitative tool over a range of practical problems in seismic interpretation, data processing, quality assessment for data acquisition, and research. Its great virtue is its ability to supply the detailed error information necessary for a thorough interpretation of results. Ordinary coherence analysis is employed in line intersection analysis and the design of filters to cross-equalize differently acquired seismic sections in a given area; both ordinary and partial coherence methods are indispensable in matching synthetic seismograms and seismic data; and multiple coherences are used to estimate the coherent signal and incoherent noise content of seismic sections and gathers. The precise meaning of the signal and noise estimates output by coherent analysis has to be related to the particular technique employed and the type of data input to it. The principles and procedures for analyzing seismic data with these methods are reviewed and illustrated with practical examples.     

An empirical Bayes estimator for in-scale adaptive filtering

A scale-adaptive filtering scheme is developed for underspread channels based on a model of the linear time-varying channel operator as a process in scale. Recursions serve the purpose of adding detail to the filter estimate until a suitable measure of fidelity and complexity is met. Resolution of the channel impulse response associated with its coherence time is naturally modeled over the observation time via a Gaussian mixture assignment on wavelet coefficients. Maximum likelihood, approximate maximum a posteriori (MAP) and posterior mean estimators, as well as associated variances, are derived. Doppler spread estimation associated with the coherence time of the filter is synonymous with model order selection and a MAP estimate is presented and compared with Laplace's approximation and the popular AIC. The algorithm is implemented with conjugate-gradient iterations at each scale, and as the coherence time is recursively decreased, the lower scale estimate serves as a starting point for successive reduced-coherence time estimates. The algorithm is applied to a set of simulated sparse multipath Doppler spread channels, demonstrating the superior MSE performance of the posterior mean filter estimator and the superiority of the MAP Doppler spread stopping rule.     

Identification and deconvolution of multichannel linearnon-Gaussian processes using higher order statistics and inverse filtercriteria

This paper is concerned with the problem of estimation and deconvolution of the matrix impulse response function of a multiple-input multiple-output (MIMO) system given only the measurements of the vector output of the system. The system is assumed to be driven by a temporally i.i.d. and spatially independent non-Gaussian vector sequence (which is not observed). An iterative, inverse filter criteria-based approach is developed using the third-order or the fourth-order normalized cumulants of the inverse filtered data at zero lag. Stationary points of the proposed cost functions are investigated. The approach is input iterative, i.e., the input sequences are extracted and removed one by one. The matrix impulse response is then obtained by cross correlating the extracted inputs with the observed outputs. Identifiability conditions are analyzed. The strong consistency of the proposed approach is also briefly discussed. Computer simulation examples are presented to illustrate the proposed approaches     

Scattering function estimation

The estimation of the scattering function of a random, zero-mean, homogeneous, time-variant, linear filter is considered. The sum of the random filter output and independent noise is the input to an estimator. The estimator structure is equivalent to a bank of linear filters followed by squared-envelope detectors; the envelope detector outputs are the input to a final linear filter. The estimator output is shown to be an unconstrained linear operation on the ambiguity function of the estimator input. Except for a bias term due to the additive noise, the mean of the estimator output is an unconstrained linear operation on the scattering function of the random filter. The integral variance of the output is found for a Gaussian channel. The mean and variance clearly indicate the tradeoff between resolution and variance reduction obtained by varying the estimator structure. For any well-behaved channel it is shown that an effectively unbiased estimate of the scattering function can be obtained if the input signal has both sufficient energy and enough time and frequency spread to resolve the random filter; the random filter is not required to be underspread. The variance of an estimate can be further reduced by increasing the time or frequency spread of the transmitted signal.     

Least square identification of alias components of linear periodically time-varying systems and optimal training signal design

A least-squares (LS) method for identifying alias components of discrete linear periodically time-varying (LPTV) systems is proposed.The authors apply a periodic input signal to a finite impulse response (FIR)--LPTV system and measure the noise-contaminated output.The output of this LPTV system has the same period as the input when the period of the input signal is amultiple of the period of the LPTV system.The authors show that the input and the output can be related by using the discrete Fourier transform. In the frequency domain, an LS method can be used to identify the alias components. A lower bound on the mean square error (MSE) of the estimated alias components is given for FIR--LPTV systems.The optimal training signal achieving this lower MSE bound is designed subsequently. The algorithm is extended to the identification of infinite impulse response (IIR)--LPTV systems as well. Simulation results show the accuracy of the estimation and the efficiency of the optimal training signal design.     

Receiver design for multicarrier transmission systems in presence of Tx/Rx imperfections

The aim of this article is to design a simple receiver, which can jointly estimate the frequency selective channel impulse response, frequency independent transmit/receive IQ imbalance, and carrier frequency offset with minimal training and implementation complexity. The estimation of carrier frequency offset is performed using 2 scalable solutions. To estimate the frequency selective channel impulse response and frequency independent transmit/receive IQ imbalance, we proposed 2 different estimation techniques. The first technique is an iterative approach stemming from a doubly linear model of the transmission system in the presence of transmit/receive IQ imbalance and frequency selective channel impulse response, while the second approach is a least squares solution. Both these schemes provide a good performance/complexity trade‐off. Although the iterative estimates of channel impulse response are not optimal, they do provide a near ideal bit error performance. The proposed scheme blends seamlessly with Institute of Electrical and Electronics Engineers 802.11 standard but can be adapted to work with any orthogonal frequency‐division multiplexing standard.     

Response Characterization of Electroactive Polymers as Mechanical Sensors

The characterization of the dynamic response (including transfer function identification) of trilayer polypyrrole (PPy) type conducting polymer sensors is presented. The sensor was built like a cantilever beam with the free end stimulated through a mechanical lever system, which provided displacement inputs. The voltage generated and current passing between the two outer PPy layers as a result of the input was measured to model the output/input behavior of the sensors based on their experimental current/displacement and voltage/displacement frequency responses. We specifically targeted the low-frequency behavior of the sensor as it is a relatively slow system. Experimental transfer function models were generated and verified experimentally for sensors with different dimensions. The models can be used to understand the dynamic behavior and sensing ability of the polymers as mechanical sensors. The effect of the active sensor length on the voltage and current outputs has demonstrated that the shorter is the sensor length, the higher are the voltage output and the current passed for the same mechanical input. Also, their current and voltage responses under an impulse displacement stimulus were experimentally measured to show their dynamic sensing response and to estimate the current and voltage sensing bandwidths. Further, an energy balance method has been proposed to estimate the sensor output. Based on the novel experimental and analytical results, the contribution of this study is the first comprehensive investigation into the response analysis and characterization of the PPy-type conducting polymers as mechanical sensors, to the best of authors' knowledge.     

自积分式脉冲磁场传感器的补偿研究

自积分式磁场传感器用于脉冲场测量时，普遍存在低频失真问题，本文提出一种数字滤波的方案对传感器的低频特性进行补偿。滤波器采用无限冲激响应滤波器的形式，并以系统辨识的方法直接在时域进行设计，应用结果表明，该滤波器能有效地修复失真波形，使传感器的响应特性得到显著改善。     

Space-time channel estimation and performance analysis for wireless MIMO-OFDM systems with spatial correlation

This paper treats channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with correlation at the receive antenna array. A two-step channel estimation algorithm is proposed. Firstly, the iterative quadrature maximum likelihood based time delay and spatial signature estimation is presented by utilizing special training signals with a cyclic structure. The receive spatial correlation matrix of the vector valued channel impulse response is formulated as a function of the spatial signature, the time delay, and the pulse shaping filter. The joint spatio-temporal (JST) filtering based minimum mean squared error channel estimator is derived by virtue of the spatial correlation. In addition, the effect of channel estimation errors on the bit error probability performance of the space-time block coded OFDM system over correlated MIMO channels is derived. The Cramer-Rao lower bound on the time delay estimate is provided for a benchmark of the performance comparison. The performance of proposed algorithms is illustrated based on analysis and computer simulations. The JST channel estimator achieves significant gains in the mean squared error compared to the temporal filtering. It also enables remarkable savings in the pilot symbol power level.     

基于幅-频曲线的系统时域响应特性评价方法

在电磁兼容测量中,通常采用频率响应来描述一个系统的特性。而系统的频响特性一般仅提供了响应幅度的频域分布,很少给出相位信息。在连续波测量时这种幅－频信息是足够的,但在利用频域测量结果评价一个系统的脉冲响应特性时,还必须知道系统的相位信息。本文假设系统为最小相位系统,采用Ｈｉｌｂｅｒｔ变换根据系统响应的频谱幅度估计相位信息,进而将系统的频域信息转换到时域,用于评价系统的时域响应特性。根据一组磁场探头的     

Time‐varying channel estimation for MIMO/OFDM systems using superimposed training and basis expansion models

An approach of superimposed training (ST)‐aided time‐varying (TV) channel estimation for multiple‐input multiple‐output orthogonal frequency division multiplexing systems is presented. By modeling the TV channel with the truncated discrete basis expansion model, a two‐step approach is adopted to estimate the TV channel. In addition, the mean square error (MSE) of the proposed channel estimation is analyzed, and its closed‐form expression is derived, which is a function of the data‐to‐ST power ratio. Using the developed channel MSE, we case the problem of ST power‐allocation by minimizing the lower bound on the average channel capacity. To enhance the performance of channel estimation, a low‐complexity decision feedback mechanism is introduced to iteratively mitigate the unknown data interference. Numerical results verify the performances of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Blind identification of MISO-FIR channels

In this paper, we address the problem of determining the order of MISO channels by means of a series of hypothesis tests based on scalar statistics. Using estimated 4th-order output cumulants, we exploit the sensitiveness of a Chi-square test statistic to the non-Gaussianity of a stochastic process. This property enables us to detect the order of a linear finite impulse response (FIR) channel. Our approach leads to a new channel order detection method and we provide a performance analysis along with a criterion to establish a decision threshold, according to a desired level of tolerance to false alarm. Afterwards, we introduce the concept of MISO channel nested detectors based on a deflation-type procedure using the 4th-order output cumulants. The nested detector is combined with an estimation algorithm to select the order and estimate the parameters associated with different transmitters composing the MISO channel. By treating successively shorter and shorter channels, it is also possible to determine the number of sources.     

Blind MMSE Equalization of FIR/IIR Channels Using Oversampling and Multichannel Linear Prediction

A linear‐prediction‐based blind equalization algorithm for single‐input single‐output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second‐order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single‐input multiple‐output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum‐mean‐square‐error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.     

Volterra series modeling of power conversion systems

The nonlinear control-to-output response of pulse width modulated (PWM) conversion systems is modeled via the Volterra functional series. A brief overview of the series is presented. It is seen that the Volterra series is a power series with memory. Each term in the series represents a convolution integral. The nonlinear response of the system, for any input, can thus be determined from a knowledge of the multidimensional Volterra kernels or impulse responses. The determination of the Volterra kernels in the transform domain is performed on a simplified state-space model of the converter. The dominant component of various harmonic and intermodulation distortion frequency products in the output spectrum is derived and is expressed in terms of these kernels. Experimental results are presented confirming the modeling procedure     

Adaptive Sampling Rate Correction for Acoustic Echo Control in Voice-Over-IP

Hands-free terminals for speech communication employ adaptive filters to reduce echoes resulting from the acoustic coupling between loudspeaker and microphone. When using a personal computer with commercial audio hardware for teleconferencing, a sampling frequency offset between the loudspeaker output D/A converter and the microphone input A/D converter often occurs. In this case, state-of-the-art echo cancellation algorithms fail to track the correct room impulse response. In this paper, we present a novel least mean square (LMS-type) adaptive algorithm to estimate the frequency offset and resynchronize the signals using arbitrary sampling rate conversion. In conjunction with a normalized LMS-type adaptive filter for room impulse response tracking, the proposed system widely removes the deteriorating effects of a frequency offset up to several Hz and restores the functionality of echo cancellation.     

An efficient approach to ARMA modeling of biological systems withmultiple inputs and delays

Presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization,which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. The authors present results of applying the algorithm to simulated data and experimental biological data. In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds     

一种收敛于全局无偏最优解的非线性自回归（NAR）预测

本文研究了基于Ｖｏｌｔｅｒｒａ级数展开的非线性自回归预测模型的参数估计问题，从线性自适应ＩＩＲ波滤输出误差算法的观点，研究ＮＡＲ模型的参数估计问题，利用平均收敛条件，提出一可收敛于全局无偏最优解的新算法，解决了一般的预测误差算法的有偏解问题。     

Characterization of the Arterial System in the Time Domain

The impulse response function and the input impedance of the systemic arterial tree emphasize different aspects of this system. The impulse response function is calculated via inverse Fourier transformation of the input impedance. The effects of truncation of the impedance are reduced by subjecting the data to a Dolph-Chebyshev filter. The impulse response functions of a windkessel model, a uniform tube model, and of the arterial system of the dog, are given. The impulse response functions of the windkessel model and of the arterial system of the control dog show a sharp initial peak followed by an exponential decay (equal decay time as that of the diastolic pressure tracing). The height of the decay extrapolated to time zero is related to total arterial compliance. Total arterial compliance calculated in this way agrees with the value calculated from the ratio of the time constant of the diastolic pressure decay and peripheral resistance. The presence of peaks in the impulse response function indicates a distinct reflection site as shown in the uniform tube model and found in the dog with balloon occlusion of the descending aorta. The measurement of the time intervals between these peaks and the start of excitation together with the pulse wave velocity enable us to calculate the distance between the location of the reflecting site and the heart.