EM-HMM多频率线跟踪算法

基于隐马尔可夫模型的多频率线跟踪算法 ,能在很低的SNR环境下工作 ,但量化误差较大 ,和计算量大 .本文提出另一种选择量测向量和计算量测概率的方法 ,创造条件减小量化误差 .又经简单论证 ,将EM算法和HMM用于多频率线跟踪 ,严格地 (而不是启发式地 )得到EM HMM算法 ,可以极大地减少计算量 .本文又提出获得初始估计以启动EM HMM算法的二种方法 .仿真计算表明 ,所提的算法是有效的     

Myoelectric signal classification for phoneme-based speech recognition

Traditional acoustic speech recognition accuracies have been shown to deteriorate in highly noisy environments. A secondary information source is exploited using surface myoelectric signals (MES) collected from facial articulatory muscles during speech. Words are classified at the phoneme level using a hidden Markov model (HMM) classifier. Acoustic and MES data was collected while the words "zero" through "nine" were spoken. An acoustic expert classified the 18 formative phonemes in low noise levels [signal-to-noise ratio (SNR) of 17.5 dB] with an accuracy of 99%, but deteriorated to approximately 38% under simulations with SNR approaching 0 dB. A fused acoustic-myoelectric multiexpert system, without knowledge of SNR, improved on acoustic classification results at all noise levels. A multiexpert system, incorporating SNR information, obtained accuracies of 99% at low noise levels while maintaining accuracies above 94% during low SNR (0 dB) simulations. Results improve on previous full word MES speech recognition accuracies by almost 10%.     

基于频率信噪比加权的麦克风阵列声源定位算法

为了提高噪声和混响环境下麦克风阵列的声源定位算法性能,提出了一种基于频率信噪比加权的可控响应功率定位算法。该算法首先根据每帧阵列信号的频域协方差矩阵估计每个频率的信噪比;然后通过激活函数将频率信噪比映射为加权值,并修正传统的相位变换可控响应功率计算公式;最后利用修正公式计算每个候选位置的可控响应功率值,通过搜索可控响应功率的最大值实现声源定位。该算法根据实时估计的频率信噪比自适应地调整各频率分量对可控响应功率的贡献。仿真结果表明,与传统的相位变换可控响应功率算法、维纳预滤波波束形成算法相比,在噪声和混响的复杂声学环境下,本文算法的定位正确率更高,均方根误差更小,对噪声的鲁棒性更强。      

Two algorithms for adaptive retrieval of slowly time-varyingmultiple cisoids in noise

Two algorithms for tracking parameters of slowly varying multiple complex sine waves (cisoids) in noise (the multiple frequency tracker and the adaptive notch filter) are described. For high signal-to-noise ratio (SNR), the properties of the algorithms (i.e., stability, noise rejection, and tracking speed) are studied analytically using a linear filter approximation technique. The tradeoff between noise rejection and tracking error for both algorithms is shown to be similar. Different choices of the design variables are discussed, namely (i) minimal mean-square estimation error for random walk modeled frequency variations and (ii) minimal stationary estimation variance subject to a given tracking delay     

LMS algorithms for tracking slow Markov chains with applications to hidden Markov estimation and adaptive multiuser detection

This paper analyzes the tracking properties of the least mean squares (LMS) algorithm when the underlying parameter evolves according to a finite-state Markov chain with infrequent jumps. First, using perturbed Liapunov function methods, mean-square error estimates are obtained for the tracking error. Then using recent results on two-time-scale Markov chains, mean ordinary differential equation and diffusion approximation results are obtained. It is shown that a sequence of the centered tracking errors converges to an ordinary differential equation. Moreover, a suitably scaled sequence of the tracking errors converges weakly to a diffusion process. It is also shown that iterate averaging of the tracking algorithm results in optimal asymptotic convergence rate in an appropriate sense. Two application examples, analysis of the performance of an adaptive multiuser detection algorithm in a direct-sequence code-division multiple-access (DS/CDMA) system, and tracking analysis of the state of a hidden Markov model (HMM) with infrequent jumps, are presented.     

Estimation of time varying signal parameters using an improved Adaline learning algorithm

This paper presents an improved adaptive linear combiner (Adaline) structure for fast estimation of time varying power signal parameters corrupted by noise. Unlike the conventional Adaline approach, the new algorithm minimizes an objective function based on weighted square of the error and uses a modified recursive Gauss Newton (MRGN) method. The Hessian matrix, obtained by minimizing the objective function, was simplified using certain approximations. A weight adjustment procedure for the Adaline is defined in a decoupled manner for direct current (DC), fundamental, harmonic components and system frequency. The new improved Adaline, thus produces a faster convergence and tracking accuracy for the time varying distorted power system signals. To test the effectiveness of the algorithm, several time varying power network signals were simulated with abrupt change in system frequency, harmonics, decaying dc components with low signal to noise ratio (SNR), and the changing parameters were estimated. The performance of proposed Adaline structure is compared with the standard Adaline structure in terms of accuracy.     

Effects of CEE and Feedback Error on Performance for AF-ORS with PSA-CE Schemes Over Quasi-Static Rayleigh Fading Channels

In this paper, we propose the analytical approach for amplify-and-forward (AF) opportunistic relaying schemes (ORS). When operation of AF-ORS consists of relay selection and data transmission phases based on pilot symbol assisted-channel estimation (PSA-CE) methods over quasi-static Rayleigh fading channels, we show that the relay selection phase can be implemented by pilots symbols transmission for source-relay and relay-destination. Moreover, the feedback method for the selected relay index is proposed to have a simple fashion. Then, we investigate the effects of both a channel estimation error and an estimated noise variance, which are obtained by PSA-CE methods, on the received signal-to-noise ratio (SNR). The average SNR loss is also derived in terms with the number of pilots in PSA-CE methods. Moreover, the average symbol error rate, the outage probability, and the normalized channel capacity of the ORS are derived in approximated closed-form expressions for an arbitrary link SNR when the channel state information in the source-relay-destination link is estimated based on transmitted pilots symbols. As the number of pilot symbols, the derived analytical approach is verified, and by comparing it with simulation results, the accuracy is demonstrated. In addition, it is verified that the effect of the feedback error can be neglected for PAS-CE methods over quasi-static fading channels.     

Adaptive nonlinear filters for narrow-band interference suppressionin spread-spectrum CDMA systems

This paper presents a novel nonlinear filter and parameter estimator for narrow band interference suppression in code division multiple access spread-spectrum systems. As in the article by Rusch and Poor (1994), the received sampled signal is modeled as the sum of the spread-spectrum signal (modeled as a finite state independently identically distributed (i.i.d.) process-here we generalize to a finite state Markov chain), narrow-band interference (modeled as a Gaussian autoregressive process), and observation noise (modeled as a zero-mean white Gaussian process). The proposed algorithm combines a recursive hidden Markov model (HMM) estimator, Kalman filter (KF), and the recursive expectation maximization algorithm. The nonlinear filtering techniques for narrow-band interference suppression presented in Rusch and Poor and our proposed HMM-KF algorithm have the same computational cost. Detailed simulation studies show that the HMM-KF algorithm outperforms the filtering techniques in Rusch and Poor. In particular, significant improvements in the bit error rate and signal-to-noise ratio (SNR) enhancement are obtained in low to medium SNR. Furthermore, in simulation studies we investigate the effect on the performance of the HMM-KF and the approximate conditional mean (ACM) filter in the paper by Rusch and Poor, when the observation noise variance is increased. As expected, the performance of the HMM-KF and ACM algorithms worsen with increasing observation noise and number of users. However, HMM-KF significantly outperforms ACM in medium to high observation noise     

Performance analyses of OFDM AF relaying system in the presence of phase noise with APS and IPS

This article investigates the significant performances of orthogonal frequency division multiplexing (OFDM)-based dual-hop system in the presence of phase noise (PN). A scenario with Rayleigh fading statistics on both hops is assumed. Amplification factor for this amplify-and-forward (AF) relay networks system is divided into two conditions, average power scaling (APS) and instantaneous power scaling (IPS). Before deriving signal-to-noise ratios (SNR) under APS and IPS, the Gaussianity of intercarrier interference (ICI) is proved firstly. The accurate closed-form expressions of end-to-end SNR cumulative distribution functions (CDF) and probability density functions (PDF) for both cases are obtained later. With the help of moment generating functions (MGF), we have closed-form asymptotic expressions of bit error rate (BER), which show that the BER of system in the presence of PN cannot exceed a fixed level even when SNR in high regime. Finally, simulations verify accuracy of the results. Conclusion analysis will provide a useful help in future application of the system.     

一种基于矩阵三对角化分解的DOA估计算法

针对DOA（Direction of Arrival）估计在低信噪比的情况下估计性能下降的问题, 根据阵列协方差矩阵共轭对称的特点,采用基于Givens变换的三对角化分解方法对协方 差矩阵进行三对角化,同时利用盖氏(Gerschgorin)圆递推方法准确估计信号子空间的秩, 然后再对三对角 矩阵进行对角化,估计出噪声子空间,利用噪声子空间与导向矢量正交实现波达方向估计, 改善了低信噪比背景下估计的误差性能和稳健性。计算机仿真证明了算法的有效性 。     

基于指数平滑的动态规划检测前跟踪算法

针对传统动态规划检测前跟踪(Dynamic Programming Track-Before-Detect, DP-TBD)算法在低信噪比(Signal to Noise Ratio, SNR)环境下跟踪性能较差以及容易出现团聚效应的问题, 提出一种基于指数平滑法的DP-TBD算法.该算法的创新之处在于:利用指数平滑法预测当前帧的目标状态, 当对当前帧代价函数进行优化时利用预测的目标状态对前一帧搜索窗内的代价函数进行加权.仿真结果表明, 文中所提算法能够有效抑制团聚效应, 且算法的检测性能和跟踪性能都比传统算法有所提高, 并且信噪比越低, 性能提高越明显.因此文中算法相对于传统算法来说更适用于低信噪比环境.     

基于多通道的跳频信号欠采样频率估计

为实现跳频信号频率跟踪估计,本文提出一种基于多通道的跳频信号欠采样频率估计方法。基于快速傅里叶变换（FFT）,提出了一种3谱线方程的频率校正算法,提高了基于中国余数定理的频率估计方法对短序列信号的频率估计精度,与现有的两种基于离散傅里叶变换（DFT）的频率校正算法相比,序列补零数量灵活。给出了一种频率估计检错机制,可以提高算法可靠性。仿真结果表明,本文所提频率估计算法的精度优于现有算法,增加序列补零数量可进一步提高算法的估计精度和信噪比阈值,降低误差平台;检错机制在-23 dB至8 dB信噪比范围内的准确率高于95.5%。      

空间非平稳噪声下的宽带DOA估计算法

投影子空间正交性测试(TOPS)法是利用子空间的正交性实现宽带信号DOA估计,而在空间非平稳噪声环境下子空间的正交性条件不再满足,尤其是在低信噪比或低快拍条件下子空间估计将出现较大误差,TOPS算法性能将急剧下降。针对该问题,提出了一种空间非平稳噪声下宽带DOA估计算法。该算法首先通过构造特殊对角矩阵将噪声从数据协方差矩阵中剔除,从而克服非平稳噪声对DOA估计的影响;然后利用平方TOPS法实现宽带信号DOA估计,消除了传统TOPS算法中的伪峰。该算法适用于空间非平稳噪声背景及低信噪比环境,提高了对角度相近目标的分辨性能;仿真实验表明了该算法的有效性。     

一种基于奇异值分解的改进信噪比盲估计算法

针对空间分解类信噪比(SNR)估计算法中子空间维数估计复杂度较高,低信噪比下估计偏差较大的问题,提出了一种改进的子空间维数估计算法。该算法首先利用样本自相关矩阵的奇异值序列进行后向差分得到梯度序列,对梯度序列每一项与后5项之和的比值进行搜索,最大比值所对应的奇异值序号作为信号子空间维数,最后计算信噪比。合适数据长度下的仿真结果表明：在信噪比-5 dB~20 dB范围内,常规通信信号的信噪比估计平均偏差小于0.5 dB,标准差小于1 dB;该算法提升了低信噪比下的估计性能,运算量较小,无需知道调制方式、载波频率、符号率等先验信息,在低信噪比时对信噪比时变的跟踪估计更为准确,且对复杂高阶调制信号同样适用。     

一种基于序列相关的信噪比估计算法

针对多径信道,提出了一种基于序列相关的信噪比估计算法,利用本地序列与接收信号相关,并采用最小二乘估计法,精确地估计了接收信号幅度和噪声方差,得到了两径信道下信噪比的估计值。仿真结果表明该算法整体估计性能较好,特别适合于低信噪比条件下。在信噪比为-1dB时,与现有的频域和二阶矩四阶矩（M2M4,2-order and 4-order Moments）估计算法相比,该算法的归一化均方误差分别降低了0．09和0．2。     

A robust method for ECG-based estimation of the respiratory frequency during stress testing

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% +/- 0.2%, mean +/- SD (0.002 +/- 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% +/- 4% (0.022 +/- 0.016Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.     

用于正弦波频率估计的修正Kay算法

Kay算法能够估计出采样点较少的正弦波频率,但低信噪比下估计性能不佳.针对此问题,提出了修正Kay算法.首先基于最大似然估计准则,推导了观测信号模值与相位的条件概率密度函数,进而重建了Kay算法的相位差噪声矢量协方差矩阵与权值矩阵.实验结果表明,修正算法能够有效估计正弦波信号频率,与Kay算法相比,抗噪性更强.     

Comparative tracking performance of the LMS and RLS algorithms forchirped narrowband signal recovery

This paper studies the comparative tracking performance of the recursive least squares (RLS) and least mean square (LMS) algorithms for time-varying inputs, specifically for linearly chirped narrowband input signals in additive white Gaussian noise. It is shown that the structural differences in the implementation of the LMS and RLS weight updates produce regions where the LMS performance exceeds that of the RLS and other regions where the converse occurs. These regions are shown to be a function of the signal bandwidth and signal-to-noise ratio (SNR). LMS is shown to place a notch in the signal band of the mean lag filter, thus reducing the lag error and improving the tracking performance. For the chirped signal, it is shown that this produces smaller tracking error for small SNR. For high SNR, there is a region of signal bandwidth for which RLS will provide lower error than LMS, but even for these high SNR inputs, LMS always provides superior performance for very narrowband signals     

A Noise Spectrum Measurement System Using the Fast Fourier Transform

A portable multichannel battery-operated measurement system was developed to measure the rms magnetic-field noise spectrum in the frequency range from 100 Hz to 375 kHz. During each measurement, the entire spectrum is measured simultaneously through the use of time-domain recordings which are later analyzed by Fast Fourier Transform (FFT) processing. Dynamic ranges of 60 dB in a 125-Hz bandwidth are obtained for spectra covering the range from 100 Hz to 100 kHz. The method also allows a three-dimensional display of the way spectrum occupancy changes with time. Some advantages of the system are portability, rms measurement, 100 percent probability of signal intercept, data gathering times measured in seconds or milliseconds, simultaneous coverage of several decades of frequency, no "picket fence" amplitude uncertainty with the associated potential 3-dB error, and increased frequency measurement resolution. Some examples of electromagnetic noise (measured in and above coal mines) are given, primarily to show the capability of the measuring system.     

一种直扩信号伪码周期及序列的盲估计方法

论述了功率谱二次处理理论和信号子空间分解方法在直接序列扩频(DS/SS)信号的伪码周期估计以及伪码序列盲估计中的应用,提出了利用功率谱二次处理结合信号子空间分解的方法以实现对低信噪比DS/SS信号的完全估计.计算机模拟结果表明,该方法在较低的输入信噪比条件下能良好地工作.