High-resolution biosensor spectral peak shift estimation

In this paper, we present a maximum likelihood (ML) approach to high-resolution estimation of the shifts of a spectral signal. This spectral signal arises in application of optically based resonant biosensors, where high resolution in the estimation of signal shift is synonymous with high sensitivity to biological interactions. For the particular sensor of interest, the underlying signal is nonuniformly sampled and exhibits Poisson amplitude statistics. Shift estimation accuracies orders of magnitude finer than the sample spacing are sought. The new ML-based formulation leads to a solution approach different from typical resonance shift estimation methods based on polynomial fitting and peak (or ) estimation and tracking.     

一种快速的谱分析迭代法

为克服现有正弦波频率估计算法运算量大或估计精度不高的弊端,提出了一种快速的频率估计算法,该算法利用正弦波频谱对称性原理和频移技术,使信号频率位于峰值点附近再进行估计。仿真结果表明:文中算法稳定性不随被估计频率分布而产生波动,在低信噪比条件下不存在发散现象,以它为初始值进行一次谱分析迭代,其均方根误差在整个频段都接近克拉美-罗限。     

离散傅里叶变换及变参考阵元特征法在相干信号估计中的应用

本文讨论理想条件下均匀线阵(ULA)对相干入射信号的高分辨测向问题。在分析经典多重信号分类法(MUSIC)对相干信号测向失效原因的基础上提出新算法,它利用1）离散傅里叶变换(DFT)估计入射信号数目;2）变参考阵元重构入射信号功率矩阵估计入射信号方位角。通过与经典MUSIC算法比较验证了该算法对相干入射信号估计的可行性,并经过进一步分析得出如下结论:1）文中介绍的算法在不减少阵列有效口径前提下能够对高度相干信号进行高分辨测向;2）在低信噪比条件下能够精确估计入射信号方位角;3）随着阵列中阵元数目的增加,阵列分辨率逐渐提高。      

单音干扰下OFDM系统中频域迭代干扰消除算法

针对正交频分复用（OFDM,Orthogonal Frequency Division Multiplexing）系统受单音干扰问题,提出了一种有效的频域迭代干扰消除算法。所提算法首先在频域对干扰进行准确估计和重构,然后进行干扰消除。为实现更准确的干扰频率粗估计,提出了新的结合补零内插的频谱波峰搜索方法,有效地避免了干扰位于两子载波之间时频谱泄露导致的谱峰错判。干扰频率精确估计综合采用频率转化、低通滤波、加权相位平均算法迭代实现,估计误差在每一次迭代中减小。仿真结果表明:对于-20dB以上的干信比,所提算法能保证干扰参数的精确估计,使系统达到没有干扰时的误码率性能。      

Spectral Caracterization via Fusing Modified Prony Method with High Resolution Nonparametric Spectral Estimators

In this paper, we present a new approach to high-resolution spectral characterization of the unknown number of spectral line components embedded in colored noise. The addressed method resolves the spectral analysis problem via intelligent fusing the two spectrum estimation paradigms: (i) the parametric line spectral estimation that employs the modified regularized Prony (MORP) method for multi-harmonic signal characterization and (ii) nonparametric spectral estimation. Two nonparametric high-resolution spectral estimation methods are proposed to be fused with the MORP: the minimum variance (MV) and maximum entropy (ME) techniques. Via aggregation of the developed model-based MORP and model-free MV/ME techniques into the fused MORP-MV/MORP-ME resulting method a substantial improvement of the spectral characterization performances is gained when those are applied to characterization/analysis of the composed distributed scenes that contain noised closely spaced spectral lines to be localized with high resolution and accuracy. The simulation results are presented to illustrate the performance enhancement gained with the proposed fused MORP-MV/MORP-ME method.     

改进的非相干信号子空间宽带测向算法

研究了非相干信号子空间高分辨率宽带测向算法(ISM),提出了基于最大功率进行频点选择的改进的ISM算法,即将接收的时域信号经傅里叶变换后,选取代表最高功率的子带的频点来构造频谱密度矩阵,然后将该矩阵应用于窄带测向算法进行测向,提高了测向精度,减小了运算量。计算机仿真表明,新算法比原算法在分辨概率达到100%时的信噪比降低了9.5 dB,并且方差也明显比原算法小。     

Estimation of frequencies and damping factors by two-dimensionalESPRIT type methods

In this paper, we consider two-dimensional (2-D) signals modeled by the sum of damped cisoids. We propose two high-resolution approaches to estimate their frequencies and damping factors. Both high-resolution methods are based on the shift-invariance structure of the signal subspace related to each dimension. The first one estimates the frequency components in both dimensions as in the matrix enhancement and matrix pencil (MEMP) method before pairing them with a new algorithm. The second one consists of the direct estimation of the signal frequency pairs without an additional step to pair the frequencies related to each dimension. We show how these methods can estimate the scattering points of radar images     

Minimum variance filters and mixed spectrum estimation

This paper presents a spectral density estimator based on a normalized minimum variance (MV) estimator as the one proposed by Lagunas. With an equivalent frequency resolution, this new estimator preserves the amplitude estimation lost in Lagunas one. This proposition comes from a theoretical study of MV filters that highlights this amplitude lost. Two signal types are taken into account: periodic deterministic signals (narrow-band spectral structures) and stationary random signals (broad-band spectral structures). Without selecting a smoothing window, the proposed estimator is an alternative to Fourier-based estimator and, without modeling the signal, it is a concurrent to high-resolution estimators.     

基于神经网络的多窗口频谱估计方法研究

干扰温度估计是认知无线电技术中的关键问题之一.多窗口频谱估计结合奇异值分解技术提供了一种估算射频环境中干扰温度功率谱的有效途径.提出基于神经网络的多窗口频谱估计结合奇异值分解的算法,与传统算法相比,该算法提高了干扰温度估计值的可靠性和准确度,降低了计算复杂度,同时能够适应复杂电磁环境下对干扰温度的实时性估值的要求.     

Active high resolution time delay estimation for large BT signals

Two different high-resolution time-delay estimation (HRTDE) methods, a temporal method and a frequency method, specially adapted to large bandwidth duration (BT) product time-resolvent signals, are described. The performance gain of these methods is shown to be about four times better in comparison with the classical time-delay resolution methods. The frequency HRTDE method is applied to real data obtained from an ocean acoustic experiment. Although classic methods cannot distinguish close signal components, the method presented yields estimates of the delay differences and the attenuation associated with each propagation path     

Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG

This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal.     

An adaptive filtering approach to spectral estimation and SARimaging

We present an adaptive FIR filtering approach, which is referred to as the amplitude and phase estimation of a sinusoid (APES), for complex spectral estimation. We compare the APES algorithm with other FIR filtering approaches including the Welch (1967) and Capon (1969) methods. We also describe how to apply the FIR filtering approaches to target range signature estimation and synthetic aperture radar (SAR) imaging. We show via both numerical and experimental examples that the adaptive FIR filtering approaches such as Capon and APES can yield more accurate spectral estimates with much lower sidelobes and narrower spectral peaks than the FFT method, which is also a special case of the FIR filtering approach. We show that although the APES algorithm yields somewhat wider spectral peaks than the Capon method, the former gives more accurate overall spectral estimates and SAR images than the latter and the FFT method     

Frequency Offset Estimation Algorithm of High-Order M-APSK Modulation Signal Based on DFT

How to accurately compensate the Doppler shift is the main challenge for broadband satellite terminals. In this paper, a frequency offset estimation algorithm based on frequency domain window function iterative peaking search is proposed with high-order M-APSK signal models, considering both algorithm complexity and estimation accuracy. The variance of the new algorithm is derived mathematically, and performance curve compared with Cramer-Rao Lower Bound (CRLB) is also simulated under various SNR (signal-to-noise ratio). The effectiveness of new method is verified by simulations.

     

A new perturbation analysis for signal enumeration in rotational invariance techniques

The ESPRIT algorithm is a subspace-based high-resolution method used in source localization and spectral analysis, which provides very accurate estimates of the signal parameters. However, the underlying theory assumes a known model order, which is usually not the case in many applications. In particular, it is well known that underevaluating the model order biases the estimation. In this paper, we analyze the perturbation induced by an erroneous model order, and we present an error bound for the estimated parameters. Based on this theoretical framework, we propose a new method for selecting an appropriate modeling order, which consists in minimizing the error bound. This approach is applied to both synthetic and musical signals, and its performance is compared to that of existing methods, such as the information theoretic criteria.     

Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis

A new method for characterization of atrial arrhythmias is presented which is based on the time-frequency distribution of an atrial electrocardiographic signal. A set of parameters are derived which describe fundamental frequency, amplitude, shape, and signal-to-noise ratio. The method uses frequency-shifting of an adaptively updated spectral profile, representing the shape of the atrial waveforms, in order to match each new spectrum of the distribution. The method tracks how well the spectral profile fits each spectrum as well as if a valid atrial signal is present. The results are based on the analysis of a learning database with signals from 40 subjects, of which 24 have atrial arrhythmias, and an evaluation database with 211 patients diagnosed with atrial fibrillation. It is shown that the method robustly estimates fibrillation frequency and amplitude and produces spectral profiles with narrower peaks and more discernible harmonics when compared to the conventional power spectrum. The results suggest that a rather strong correlation exist between atrial fibrillation frequency and f wave shape. The developed set of parameters may be used as a basis for automated classification of different atrial rhythms.     

Robust parameter estimation for mixture model

In pattern recognition, when the ratio of the number of training samples to the dimensionality is small, parameter estimates become highly variable, causing the deterioration of classification performance. This problem has become more prevalent in remote sensing with the emergence of a new generation of sensors with as many as several hundred spectral bands. While the new sensor technology provides higher spectral and spatial resolution, enabling a greater number of spectrally separable classes to be identified, the needed labeled samples for designing the classifier remain difficult and expensive to acquire. Better parameter estimates can be obtained by exploiting a large number of unlabeled samples in addition to training samples, using the expectation maximization algorithm under the mixture model. However, the estimation method is sensitive to the presence of statistical outliers. In remote sensing data, miscellaneous classes with few samples are often difficult to identify and may constitute statistical outliers. Therefore, the authors propose to use a robust parameter-estimation method for the mixture model. The proposed method assigns full weight to training samples, but automatically gives reduced weight to unlabeled samples. Experimental results show that the robust method prevents performance deterioration due to statistical outliers in the data as compared to the estimates obtained from the direct EM approach     

The Marple algorithm for the autoregressive spectral estimates of the SMMW fourier transform spectroscopy data

The Marple algorthm for the autoregressive spectral estimates has been applied to the SMMW Fourier transform spectrum analysis. The experimental results have shown that this method yields AR spectra with three times higher resolution than the FFT method does. The improvements obtained from the Marple algorithm over the maximum entropy algorithm include higher resolution, less bias in the spectral peak frequency estimation and absence of observed spectral line splitting. The effects of the structure of the spectral lines and the noise on the resolution are discussed.     

基于神经网络的光纤温度和应变快速解调方法

为了提高基于布里渊散射的分布式光纤传感系统实时性,在分析经典基于洛伦兹和伪Voigt模型拟合法优缺点的基础上,将多层前馈神经网络方法用于布里渊频移的估算.确定了神经网络的结构、输入及输出量、激活函数和训练算法,采用不同信噪比(5dB～40dB)和布里渊频移(10.62GHz～10.82GHz)的布里渊谱训练该网络,训练...     

Simultaneous Schur decomposition of several nonsymmetric matricesto achieve automatic pairing in multidimensional harmonic retrievalproblems

This paper presents a new Jacobi-type method to calculate a simultaneous Schur decomposition (SSD) of several real-valued, nonsymmetric matrices by minimizing an appropriate cost function. Thereby, the SSD reveals the “average eigenstructure” of these nonsymmetric matrices. This enables an R-dimensional extension of Unitary ESPRIT to estimate several undamped R-dimensional modes or frequencies along with their correct pairing in multidimensional harmonic retrieval problems. Unitary ESPRIT is an ESPRIT-type high-resolution frequency estimation technique that is formulated in terms of real-valued computations throughout. For each of the R dimensions, the corresponding frequency estimates are obtained from the real eigenvalues of a real-valued matrix. The SSD jointly estimates the eigenvalues of all R matrices and, thereby, achieves automatic pairing of the estimated R-dimensional modes via a closed-form procedure that neither requires any search nor any other heuristic pairing strategy. Moreover, we describe how R-dimensional harmonic retrieval problems (with R⩾3) occur in array signal processing and model-based object recognition applications