共查询到20条相似文献,搜索用时 231 毫秒
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准确的时延估计(Time Delay Estimation,TDE)是基于到达时间差(Time Difference of Arrival,TDOA)的声源定位技术的前提.在众多时延估计算法中,广义互相关(Generalized Cross Correlation,GCC)算法因其较低的运算复杂度和易于实现的特点得到了广泛的应用.针对不同的噪声情况,GCC时延估计算法利用不同的加权函数来抑制噪声干扰.本文在介绍麦克风阵列模型和GCC时延估计算法的基础上,针对GCC算法的弊端提出了一种改进算法,并在多种信噪比条件下,对部分加权函数的GCC时延估计算法进行了MATLAB仿真,通过比较其时延估计性能和声源定位精度,分析了这些加权函数各自的优劣性. 相似文献
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传统的分数时延估计算法对环境噪声和混响噪声比较敏感,在复杂的实际环境中,算法性能会严重下降。为进一步提高时延估计算法性能,提出一种基于广义互相关(Generalized cross correlation,GCC)改进算法的广义互相关 最大似然相位补偿(
GCC Maximum likelihood phase compensation,GCC MLP)分数延时估计算法。该算法改进了GCC频域加权函数,并将线性相位补偿应用于频域互相关谱,获得连续的分数时延估计值,进一步提高了分数时延估计的精确性。仿真结果表明,GCC MLP相位补偿分数时延估计算法增强了对环境噪声和混响噪声的鲁棒性,减小了时延估计误差,算法性能优于曲线拟合、Sinc插值等传统分数时延估计算法。 相似文献
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Yong Bian 《International journal of remote sensing》2013,34(19):6796-6836
In this article, the statistical model of the polarimetric synthetic aperture radar (SAR) single-look complex image is analysed using alpha-stable distribution. It is better to use alpha-stable distribution than Gaussian distribution to represent the statistical characteristics of the polarimetric SAR image. A polarimetric SAR covariance matrix estimation method based on fractional lower-order statistics (FLOS) is proposed. Based on this model, an adaptive polarimetric SAR optimal despeckling method based on FLOS is developed. This algorithm adaptively estimates the characteristic exponents of each channel and uses these estimated alphas to calculate the parameters for the optimal despeckling adaptively. The experiments using polarimetric SAR data demonstrate that the proposed method not only reduces the blurs that occur in the area of impulsive reflectors in the result of the original optimal despeckling method, but also maintains the speckle reduction ability (equivalent number of looks). 相似文献
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《Digital Signal Processing》2006,16(2):149-163
The class of alpha-stable distributions is better for modeling impulsive noise than Gaussian distribution in array signal processing. After briefly introducing the statistical characteristics of stable distribution and the fractional lower order statistics, including the covariation and the fractional order correlation, this paper proposes a new FOC-ESPRIT method of 2-D direction finding based on the fractional order correlation and subspace technique for underwater 2-D source localization using a vector hydrophones array under alpha-stable noise conditions. A vector hydrophone comprises two or three spatially co-located, orthogonally oriented identical velocity hydrophones (each of which measures one Cartesian component of the underwater acoustical particle velocity vector-field) plus an optional pressure hydrophone. Simulation experiments show that the proposed method is robust in a wide range of characteristic exponent values of stable distribution. Its performances are better than those of the conventional second-order statistics based ESPRIT algorithm, furthermore, the fractional order correlation is more suitable than the covariation in practical applications. 相似文献
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信号传输时间(时延)的测量,是雷达、声纳系统的关键技术之一。本文研究了一种基于过零点的时延测量方法,在建立过零点搜索模型的基础上推导得出时延测量的测量结果表达式;定量分析了高斯白噪声条件下过零点时延测量的测量精度,得到测量误差的理论表达式。仿真与实验表明,过零点时延测量精度与信号频率、过零点数目、信噪比密切相关,当信噪比较高时,其测量精度与经典FFT法相当,而优势在于过零点时延测量的算法简单、计算量小,适用于对实时性要求较高的测量场合。 相似文献
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基于时延估计(TDE)的声源定位算法是数字助听器中的核心算法之一,其估计精度会受到噪声和采样频率等因素的影响,导致了定位的不准确性。针对这一问题,结合相关峰精确插值算法(FICP),提出了一种基于二次相关改进的广义互相关时延估计算法。该算法通过二次相关,有效地降低噪声的干扰,再利用FICP,提高相关函数的分辨率。仿真实验表明,无论在低信噪比,还是在高信噪比环境下,改进算法的时延估计性能都有了明显改善。 相似文献
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Interference mitigation is one of the main challenges in wireless communication, especially in ad hoc networks. In such context, the Multiple Access Interference (MAI) is known to be of an impulsive nature. Therefore, the conventional Gaussian assumption is inadequate to model this type of interference. Nevertheless, it can be accurately modeled by stable distributions. In fact, it was shown in literature that the α-stable distribution is a useful tool to model impulsive data. In this paper, we tackle the problem of noise compensation in ad hoc networks. More precisely, this issue is addressed within an Orthogonal Frequency Division Multiplexing (OFDM) transmission link assuming a symmetric α-stable model for the signal distortion due to MAI. Based on Bayesian estimation, the proposed approach estimates the transmitted OFDM symbols in the time domain using the Sequential Monte Carlo (SMC) methods. Unlike existing schemes, we consider the more realistic case where the impulsive noise parameters are assumed to be unknown at the receiver. Consequently, our approach deals also with the difficult task of noise parameters estimation which can be very useful for other purposes such as target tracking in wireless sensor networks or channel estimation. Simulations results, provided in terms of Mean Square Error (MSE) and Bit Error Rate (BER), illustrate the efficiency and the robustness of this scheme. 相似文献
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描述了稳定分布的谱表示,提出了共变谱密度的概念,得到一种基于自共变序列与共变谱的稳定分布白噪声与有色噪声的概念及其判断标准,对传统意义上的白噪声进行了广义化,依据多项式自回归(PAR)系统模型,对基于稳定白噪声输入的系统输出非线性稳定有色噪声建立其非线性PAR模型,提出基于最小P范数的EIRLP算法对非线性PAR系统进行辨识。模拟和分析表明,这种算法是一种在高斯和分数低阶 稳定分布噪声条件下具有良好韧性的非线性系统辨识方法,是对传统的二阶统计量基础上的系统辨识方法的改造与推广。 相似文献
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《IEEE transactions on audio, speech, and language processing》2009,17(2):392-401
The removal of noise and interference from an array of received signals is a most fundamental problem in signal processing research. To date, many well-known solutions based on second-order statistics (SOS) have been proposed. This paper views the signal enhancement problem as one of maximizing the mutual information between the source signal and array output. It is shown that if the signal and noise are Gaussian, the maximum mutual information estimation (MMIE) solution is not unique but consists of an infinite set of solutions which encompass the SOS-based optimal filters. The application of the MMIE principle to Laplacian signals is then examined by considering the important problem of estimating a speech signal from a set of noisy observations. It is revealed that while speech (well modeled by a Laplacian distribution) possesses higher order statistics (HOS), the well-known SOS-based optimal filters maximize the Laplacian mutual information as well; that is, the Laplacian mutual information differs from the Gaussian mutual information by a single term whose dependence on the beamforming weights is negligible. Simulation results verify these findings. 相似文献
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By introducing correntropy as the robust statistics, a novel direction of arrival estimator for α-stable noise is proposed. In this method, the signal subspace is estimated by solving the correntropy based optimization problem under the maximum correntropy criterion. An optimal step size based iterative algorithm is developed and the convergence of it is proved. Comprehensive simulation results demonstrate that the proposed method is superior to several existing algorithms in terms of the probability of resolution and the estimation accuracy, especially in the highly impulsive noise environments. 相似文献
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A single distribution is typically used to model the innovations of an autoregressive (AR) model. However, sparse impulses may exist within the innovations which may cause outliers in the observations. These impulses cannot be modeled by a single distribution and may potentially degrade the estimation. In this study, the innovation of an AR model is modeled by using both a Gaussian noise component and a sparse impulse noise model in order to obtain robust estimation and estimation of the impulses simultaneously. The Gaussian distribution models the normal noise and the sparse impulse noise model models the sparse abnormal innovation impulses. A hierarchal Bayesian model is built for the proposed model. Automatic relevance determination (ARD) priors are set for both the coefficients and the sparse impulses. A Variational Bayesian (VB) learning algorithm is given to estimate the parameters of the model. Experimental results show that the proposed model with the learning algorithm is valid for AR models with outliers caused by sparse innovation impulses, the coefficient estimation accuracy is better than other methods, and the sparse impulses can be estimated simultaneously. 相似文献
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Durrieu J.-L. Richard G. David B. Fevotte C. 《IEEE transactions on audio, speech, and language processing》2010,18(3):564-575
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Biqing Wu 《Automatica》2004,40(2):203-212
This paper presents a multi-channel active noise control algorithm that is designed to reject periodic signals of unknown frequency. It is based on a so-called indirect approach, where the frequency of the disturbance is estimated in real time, and the estimate is used in a disturbance rejection scheme designed for a known frequency. Improvements over an earlier algorithm include an extension to multi-channel systems, a better frequency estimation algorithm, and a thorough experimental evaluation. For disturbance rejection, a so-called inverse G algorithm is proposed and its properties are compared through analysis and experiments to those of a gradient algorithm. A new frequency estimator is also considered that is simple and flexible in design, and is able to use multiple harmonics or multiple signals in order to estimate the fundamental frequency of the noise source. In this manner, the algorithm maintains tracking of the fundamental frequency despite significant changes in signal characteristics. The ability of the indirect approach to reject periodic noise with fixed or time-varying frequency and amplitudes is demonstrated in active noise control experiments. The algorithm may also be useful in other control applications where periodic disturbances of unknown frequency must be rejected. 相似文献
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针对稳定分布环境下非平稳过程分析方法时频滑动平均(TFMA)模型算法的退化,引入分数低阶统计量共变,提出了一种改进的分数低阶时频时频滑动平均(FLO-TFMA)模型算法。推导了FLO-TFMA模型的参数求解过程,给出了基于FLO-TFMA模型的时频谱估计。通过在稳定分布环境下对TFMA模型算法和所提出的FLO-TFMA模型算法的参数估计均方误差(MSE)比较和时频谱估计比较,仿真结果表明,FLO-TFMA模型算法的参数估计精度优于TFMA模型算法,TFMA模型时频谱估计完全失效,而FLO-TFMA模型时频谱算法能较好地进行时频谱估计。 相似文献
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相关背景噪声下的时延估计问题是现阶段的一个研究热点.在时延估计的众多算法中,属于“约束”类算法,ETDE(explicit time delay estimation)算法具有适用性广、可靠性高、计算量小和实时性好等优点.文中首次将ETDE算法扩展到相关背景噪声领域.首先从理论上分析了在相关背景噪声下ETDE算法的估计效果,得出了时延估计的期望表达式.指出在此种情况下,ETDE算法得到的是时延的有偏估计,且偏差量与时延真值、信噪比以及背景噪声间互相关函数值的大小有关.最后通过计算机仿真验证了理论结果. 相似文献
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为了消除润滑油内金属磨粒检测系统(metal debris detection system,MDDS)输出信号中混合的高斯白噪声,提出了一个基于ICA的算法对两路输出信号进行消噪处理.对两路信号添加前缀信号,并按照所述步骤进行两次ICA后得到三路独立源信号,根据ICA前后前缀信号幅值和相位的变化校正ICA分离结果的幅值和相位,完全恢复源信号.对MDDS的输出信号进行仿真以验证算法的去噪效果,实验结果表明,该算法可以有效地消除输出信号中的白噪声. 相似文献