基于经验模态分解重构的二次相关时延估计

针对低信噪比情况下的时延估计,将二次相关( SC)时延估计与经验模态分解( EMD)算法结合,提出了EMD重构二次相关时延估计方法。该方法针对EMD重构时本征模态函数的选择,将倒谱法和谱减法相结合,提出新的本征模态函数中有用信号主导分量和噪声主导分量的区分方案。研究结果表明：EMD重构二次相关法较传统二次相关法抗噪性能更优,更能锐化二次相关峰值;在非高斯有色噪声和高斯白噪声情况下,分别将准确估计时延的信噪比降低了4 dB和2 dB。     

阵列信号处理中的时延估计

在有噪情况下，精确的时延估计是非常困难的，本文提出了一种新的时延估计方法，该方法有效地降低了噪声对时延估计的影响，和常用的自相关函数法相比较，这种算法明显降低了时延的估计误差。     

基于循环平稳性的约束自适应多径时延估计

在有多径信号、多通道非平稳干扰信号以及平稳背景噪声的情况下,不考虑多径信号传输的时延估计方法不能准确地估计时延,甚至估计性能会恶化.为此,本文提出了基于循环平稳性的约束自适应多径时延估计算法,并对算法的收敛性能进行了分析.该算法可以有效地抑制干扰和噪声的影响,在低信噪比的情况下直接地、准确地进行自适应多径时延估计,特别对噪声是空间相关的情形也适用,克服了传统算法不能直接估计非整数倍采样间隔的时延和多径时延的缺点.计算机仿真试验验证了新方法的有效性.     

一种基于遗传算法的自适应参数型多径时延估计

基于四阶累积量的自适应参数型多径时延估计（FOC—APMTDE）算法只能直接估计整数倍采样间隔的时延,为了克服此缺点,引入遗传算法进行时延估计的寻优,保留了FOC—APMTDE算法良好的抑制相关或非相关高斯噪声的性能,在低信噪比的情况下可以准确地直接估计非整数倍采样间隔的时延。计算机仿真试验验证了新方法的有效性。     

一种基于四阶累积量的约束自适应多径时延估计

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。     

脉冲噪声环境下基于分数低阶循环相关的自适应时延估计方法

以α稳定分布作为噪声模型,研究了非高斯噪声对传统的二阶循环统计量的影响,提出了分数低阶循环相关的概念,研究并证明了其性质,对传统意义上的二阶循环统计量进行了广义化,并在此基础上结合自适应技术提出了一种基于分数低阶循环相关的自适应时延估计方法。计算机模拟表明,该方法可有效估计高斯噪声和脉冲噪声条件下的时变和非时变时延值,其性能不仅优于基于二阶循环相关的自适应时延估计算法,而且优于最小平均p范数(LMP)自适应时延估计方法。     

脉冲噪声环境下基于分数低阶循环相关的自适应时延估计方法

以α稳定分布作为噪声模型，研究了非高斯噪声对传统的二阶循环统计量的影响，提出了分数低阶循环相关的概念，研究并证明了其性质，对传统意义上的二阶循环统计量进行了广义化，并在此基础上结合自适应技术提出了一种基于分数低阶循环相关的自适应时延估计方法。计算机模拟表明，该方法可有效估计高斯噪声和脉冲噪声条件下的时变和非时变时延值，其性能不仅优于基于二阶循环相关的自适应时延估计算法，而且优于最小平均P范数（LMP）自适应时延估计方法。     

SαS分布噪声模型下激光信号互相关时延估计

高斯噪声模型因其能够避免非线性问题且易于工程实现在早期多被采用,然而在实际应用中,往往存在脉冲幅度特性的噪声和干扰。因此本文对非高斯噪声背景下脉冲式激光测距互相关时延估计算法进行了研究。首先采用钟形脉冲模型对发射激光脉冲进行建模、采用SαS(Symmetric Alpha Stable)模型对脉冲噪声进行建模;然后提出了基于Sigmoid函数的互相关时延估计算法,通过检测互相关峰值位置就可以得到回波信号相对于发射激光脉冲的时延;最后对Sigmoid函数中的k值进行分析,通过大量仿真,在发射激光脉冲宽度一定时,激光脉冲幅值A与k的关系为kA≈3时,N[TX-]最小,时延估计效果最好。该方法时延估计效果与直接使用k=1的时延估计效果对比,有明显提升。     

基于三阶累积量的多径时延估计

研究多径传输条件下的时延估计问题。利用三阶累积量的一维切片作为高阶统计量,结合相关算法原理,提出一种新的时延估计算法。为提高时延估计精度,对相关数据进行了加权处理。该算法可有效抑制空间相关高斯噪声或对称分布噪声,得到非高斯信号准确的时延估计。算法具有计算量小,易于实现的优点。仿真结果表明了该算法的有效性。     

四阶统计量在时差定位中的应用

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。     

非高斯相关噪声中高斯信号的时延估计

高阶统计量在信号处理中成功的应用例子之一是估计高斯相关噪声中非高斯信号的时延参数.本文则研究非高斯相关噪声中高斯信号的时延估计问题,提出了一种解决该问题的混合方法.该方法先计算观测值的三阶累积量,然后利用累积投影公式计算观测噪声的二阶统计量,最后利用互相关方法确定信号时延参数.仿真结果验证了该方法的有效性.     

一种空间相关高斯噪声背景下的时变时延估计算法

在空间相关高斯噪声的背景下,基于二阶统计量的时延估计方法会失效,该文提出了一种基于三阶统计量的自适应时变时延估计算法,并分析了算法的收敛性,最后的仿真结果表明该算法可以有效地抑制相关高斯噪声。     

On time delay estimation with unknown spatially correlated Gaussiannoise using fourth-order cumulants and cross cumulants

The problem of estimating the difference in arrival times of a linear non-Gaussian signal at two spatially separated sensors is considered. The signal is assumed to be corrupted by spatially correlated Gaussian noises of unknown cross correlation. A parameter estimation approach is adopted, and the fourth-order cumulant statistics of the noisy measurements are exploited to obtain the time delay estimate. Specific and mild sufficient conditions are given for time delay identifiability via a persistence of excitation condition. Illustrative Monte Carlo simulation examples are also presented     

Time delay estimation using higher order statistics

The problem of time delay estimation in spatially correlated Gaussian noises is addressed. New normal equations based upon higher order statistics of the sensor measurements are established, from which a new adaptive algorithm for time delay estimation is proposed. Simulation examples are presented to illustrate the effectiveness of this algorithm     

Time delay estimation with unknown spatially correlated Gaussiannoise

The problem of estimating the difference in arrival times of a non-Gaussian signal at two spatially separated sensors is considered. The signal is assumed to be corrupted by spatially correlated Gaussian noises of unknown cross-correlation. The author proposes and analyzes two new classes of methods for time delay estimation based on higher order statistics. The proposed methods are conceptually very similar to the traditional cross-correlation-based techniques in that the proposed criteria peak at a lag value equalizing true delay. Since the proposed methods are based upon higher order cumulant statistics of the data, they result in estimators that remain unbiased in the presence of Gaussian noise     

Cumulant-based method for bearing estimation in the presence ofnonGaussian noise

Bearing estimation algorithms based on the cumulants of array data have been developed to suppress additive spatially correlated Gaussian noises. In practice, however, the noises encountered in signal processing environments are often non-Gaussian, and the applications of those cumulant-based algorithms designed for Gaussian noise to non-Gaussian environments may severely degrade the estimation performance. The authors propose a new cumulant-based method to solve this problem. This approach is based on the fourth-order cumulants of the array data transformed by DFT, and relies on the statistical central limit theorem to show that the fourth-order cumulants of the additive non-Gaussian noises approach zero in each DFT cell. Simulation results are presented to demonstrate that the proposed method can effectively estimate the bearings in both Gaussian and non-Gaussian noise environments     

Time delay estimation using the cross bispectrum

The cross bispectrum phase can be effectively used to estimate the time required for a nonGaussian signal to propagate between a pair of spatially separated sensors in the presence of highly correlated Gaussian noise. The authors present a consistent estimator of the phase of the cross bispectrum, derive the exact distribution of the phase of a complex Gaussian sample bispectrum, and show that in most cases the exact distribution can be approximated by a Gaussian distribution. Using this Gaussian approximation, the authors derive the variance of the time delay estimate computed from the sample cross bispectrum of a signal in additive correlated noise. These results allow the performance of time delay estimators based on the cross bispectrum phase to be quantified as a function of the sample size, the skewness of the signal, the signal-to-noise ratio (SNR), and the noise correlation