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本文研究了维纳加权广义相关时间延迟估计问题,提出了一种自适应实现维纳加权的时延估计方法。文中讨论了算法结构,分析了其性能,并给出了计算机模拟的结果。理论分析和计算机模拟表明,这种自适应维纳加权时延估计方法,不依赖于输入信号和噪声的统计先验知识,可以有效地消除信号中噪声对时延估计的影响,具有较高的估计精度和较快的收敛速度。 相似文献
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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。 相似文献
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在存在多径信号和空间相关性未知的背景高斯噪声情况下,不考虑多径信号传输的传统时延估计方法的性能会受到影响,甚至恶化。针对此问题,提出了一种基于四阶累积量的约束自适应多径时延估计算法,并对该算法的多径时延估计性能进行了收敛性能分析。该算法能够有效抑制空间相关性未知噪声的影响,在低信噪比的情况下能够直接、准确地进行自适应多径时延估计,克服了传统算法不能直接估计非整数倍采样间隔时延的缺点。计算机仿真试验验证了新算法的有效性。 相似文献
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四阶统计量在时差定位中的应用 总被引:1,自引:0,他引:1
实际时差定位中需要从有高斯噪声的环境中估计信号的时延 ,但基于互相关或互功率谱的时延估计方法对噪声十分敏感 ,本文作了基于四阶统计量的时延估计 ,并给出了几种求解延时的方法 ,可以很好地解决这一问题。 相似文献
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在多站时差定位系统中使用基于LMS自适应滤波的互相关法进行时延估计时,若采用固定步长因子则会在收敛速度和稳态失调之间存在较大矛盾,从而影响时延估计精度。针对这一问题,文中提出了一种基于分段变步长LMS自适应滤波和希尔伯特差值的互相关时延估计优化算法。该方法首先采用分段变步长LMS自适应滤波对信号进行滤波处理,然后将滤波后的信号作互相关运算,最后通过希尔伯特差值法锐化相关函数的峰值,进一步提高时延估计精度。在相同条件下,文中模拟分析了不同算法的时延估计精度。实验结果表明,新的优化算法时延估计精度更高。在不同信噪比下,新方法相较传统时延估计方法精度提高了2.2%以上,具有良好的抗噪声性能。 相似文献
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This paper deals with the identification of a nonlinear SISO system modelled by a second-order Volterra series expansion when both the input and the output are disturbed by additive white Gaussian noises. Two methods are proposed. Firstly, we present an unbiased on-line approach based on the LMS. It includes a bias correction scheme which requires the variance of the input additive noise. Secondly, we suggest solving the identification problem as an errors-in-variables issue, by means of the so-called Frisch scheme. Although its computational cost is high, this approach has the advantage of estimating the Volterra kernels and the variances of both the additive noises and the input signal, even if the signal-to-noise ratios at the input and the output are low. 相似文献
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The least mean squares adaptive line enhancer (LMS ALE) has been widely used for the enhancement of coherent sinusoids in additive wideband noise. This paper studies the behavior of the LMS ALE when applied to the enhancement of sinusoids that have been corrupted by both colored multiplicative and white additive noise. The multiplicative noise decorrelates the sinusoid, spreads its power spectrum, and acts as an additional corrupting noise. Closed-form expressions are derived for the optimum (Wiener filter) ALE output SNR as a function of the residual coherent sine wave power, the noncoherent sine wave power spectrum, and the background additive white noise. When the coherent to noncoherent sine wave power ratio is sufficiently small, it is shown that a nonlinear (e.g., square law) transformation of the ALE input results in a larger optimum ALE output SNR 相似文献
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《IEEE transactions on information theory / Professional Technical Group on Information Theory》1968,14(5):684-693
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. 相似文献
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White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the Auto-Regressive Moving Average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises. They can handle the input white noise fused filtering, prediction and smoothing problems. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances. 相似文献
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A minimum misadjustment adaptive FIR filter 总被引:1,自引:0,他引:1
The performance of an adaptive filter is limited by the misadjustment resulting from the variance of adapting parameters. This paper develops a method to reduce the misadjustment when the additive noise in the desired signal is correlated. Given a static linear model, the linear estimator that can achieve the minimum parameter variance estimate is known as the best linear unbiased estimator (BLUE). Starting from classical estimation theory and a Gaussian autoregressive (AR) noise model, a maximum likelihood (ML) estimator that jointly estimates the filter parameters and the noise statistics is established. The estimator is shown to approach the best linear unbiased estimator asymptotically. The proposed adaptive filtering method follows by modifying the commonly used mean-square error (MSE) criterion in accordance with the ML cost function. The new configuration consists of two adaptive components: a modeling filter and a noise whitening filter. Convergence study reveals that there is only one minimum in the error surface, and global convergence is guaranteed. Analysis of the adaptive system when optimized by LMS or RLS is made, together with the tracking capability investigation. The proposed adaptive method performs significantly better than a usual adaptive filter with correlated additive noise and tracks a time-varying system more effectively 相似文献
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Adaptive estimation of the linear coefficient vector in truncated expansions is considered for the purpose of modeling noisy, recurrent signals. Two different criteria are studied for block-wise processing of the signal: the mean square error (MSE) and the least squares (LS) error. The block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the MSE, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. It is demonstrated that BLMS is equivalent to an exponential averager in the subspace spanned by the truncated set of basis functions. The block recursive least squares (BRLS) solution is shown to be equivalent to the BLMS algorithm with a decreasing step size. The BRLS is unbiased at any occurrence number of the signal and has the same steady-state variance as the BLMS but with a lower variance at the transient stage. The estimation methods can be interpreted in terms of linear, time-variant filtering. The performance of the methods is studied on an ECG signal, and the results show that the performance of the block algorithms is superior to that of the LMS algorithm. In addition, measurements with clinical interest are found to be more robustly estimated in noisy signals 相似文献
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本文讨论了LMS自适应噪声对消器的有限字长效应。推导了包括量化噪声和截断噪声的输出信噪比的理论公式。如果某个量化字长能反映出输出弱信号的变化,输出信噪比主要取决于自适应运算的字长。 相似文献
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In this paper, we present the derivation and analysis of the true Crame/spl acute/r-Rao lower bound (CRB) for the variance of unbiased, data-aided (DA) symbol-timing estimates, obtained from a block of K samples of a linearly modulated information signal, transmitted through an additive white Gaussian noise channel with random carrier phase. We consider a carrier-phase-independent time-delay estimation scenario wherein the carrier phase is viewed as an unwanted or nuisance parameter. The new bounds require only a moderate computational effort and are tighter than the CRB for the variance of unbiased time-delay estimates obtained under the assumption that the carrier phase is known. These bounds are particularly useful to assess the ultimate accuracy that can be achieved by pilot-assisted symbol synchronizers. Conversely, they may be used to evaluate data sequence suitability for the purpose of time-delay estimation. Comparison of the actual variance of a DA feedforward timing estimator with the new bounds show that these are attainable by practical synchronizers. 相似文献
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The paper presents an asymptotically unbiased estimator of autoregressive parameters from noisy observations. The key ingredient in the author's method is that a new and simple scheme for estimation of the variance of the white measurement noise is developed. This estimated variance is then used in conjunction with the known technique for elimination of the least-squares estimation bias when the noise statistics are known a priori. The properties of the method are illustrated by means of some simulated examples 相似文献