Pattern时延差编码水声定位的时延估计研究

基于Pattern时延编码体制,针对水下超短基线定位系统设计了呼叫信号的正、负调频时延编码结构,并提出利用二次互相关技术进行时延估计。该方法在一定程度上能够提高呼叫方的可检测信噪比,同时在复杂环境下对信道中包括直达波在内的多径信号能量进行累加,能够准确地完成峰值检测,进而达到提高时延估计精度的目的。海试实验结果表明二次相关法在不同信噪比条件下能有效地抑制界面反射产生的多径干扰。     

自适应时延估计在蛙人超短基线定位中的应用

蛙人超短基线(Ultra-Short Base Line, USBL)定位设备实现浅水环境下高精度定位的关键技术之一是精确的时延估计。由于自适应时延估计方法具备环境自适应能力强的特点,文章将混合调制的拉格朗日直接时延估计方法应用于蛙人USBL定位时的高精度测向上,它可以在信标信号中心频率已知的情况下将小数时延滤波器调制到信号中心频率处,以较低的阶数提供更高的时延估计精度;并根据USBL阵型和信号自身的特点,对混合调制的拉格朗日直接时延估计在低信噪比下的具体使用模式进行了探讨和仿真验证;结果显示,所采用的自适应时延估计方法可以在中低信噪比下提供1°～3°的定位测向精度。     

基于噪声抵消技术的时延估计

阐述了噪声抵消时延估计法(Time Delay Estimation based on Noise-canceling,TDENC)的基本原理,建立了TDENC分析的估计模型;并进行了大量仿真计算和初步海上试验。仿真结果表明:低信噪比且干扰噪声间相关性较强时,TDENC估计延时明显优于常规方法,但其主峰较宽;干扰噪声间彼此相关但信噪比高,估计时延精度则较差。海上试验结果和理论仿真一致。     

基于高阶累积量的高精度时延估计算法

针对海洋探测中由于接收信号信噪比低并存在各种噪声干扰导致时延估计精度低的问题,提出一种基于二次相关和高阶累积量的具有多种噪声抑制能力的高精度时延估计新方法——SC-HOCS法。该方法首先对两路接收信号进行自相关和互相关处理,抑制部分高斯噪声,然后利用高阶累积量一维切片法对信号进行处理,抑制相关高斯噪声和非高斯色噪声,通过对接收信号的上述处理提高信噪比,最后结合希尔伯特变换对相关峰进行锐化处理,进一步提高时延估计精度。与广义相关法、二次相关法及高阶累积量一维切片法相比,该方法能很好地抑制相关噪声并且能在更低的信噪比下获得较好的时延估计精度,同时该算法计算量较小,可满足对数据实时处理的需求。计算机仿真和水池实验验证了该方法的有效性。该方法为海洋探测中低信噪比信号的高精度时延估计提供一种新的技术途径。     

线性插值时延估计方法及误差分析

讨论了钱性插值时延估计方法及影响其测时精度的诸种因素。在一定信噪比下,这种方法的计算精度可达到微秒量级,而且所需设备较少,信号处理全部由软件完成,算法简单,很适合于多路信号的时延估计。     

音频无人机定位的时延估计模拟分析

针对无人机非平稳音频信号时差定位中,广义互相关时延估计算法抗噪性差和时延估计值精度低等问题,文章采用了一种基于广义二次相关时延估计的改进算法。算法对叠加了实际噪声（如风声、雨声、汽车鸣笛声等）的无人机音频信号进行频谱细化的广义二次相关,有效抑制了噪声干扰,融合相关峰精确插值算法,提高了互相关函数的分辨率,使得时延峰值更加明显。仿真实验结果表明,改进的广义二次相关方法在不同信噪比时,比广义互相关和广义二次相关算法的时延估计精度更高,稳定性更好。改进的广义二次相关算法对无人机定位中的时延估计具有更好的性能优势,具有较强的实际应用性。     

噪声相关条件下多传声器时延多尺度估计

提出一种基于小波分解的多尺度时延估计方法,解决非稳态、有色噪声且通道间噪声存在相关性的条件下提高时延估计精度问题,并把这一理论应用于声阵列对声源的定位.利用小波良好的时间-频率分解性能将非稳态信号分解为多个稳态信号,同一尺度内进行互相关获得多尺度时延值,并证明了小波分解不会改变信号之间的固有时延值.针对不同尺度内时延值不一致、无法识别出正确时延值这一问题,提出用三传声器之间时延匹配关系消除噪声相关性,来识别正确时延,从而实现非稳态、噪声相关条件下的时延估计.最后通过正四方形声阵列试验检验了该方法,试验证明:该方法不但提高了识别率、细化和突出了相关函数的波峰,还提高了时延估计的稳定性和精度.     

三元相关被动测距方法探讨

提出了在被动测距声纳工作背景下的三元相关被动测距方法。通过仿真分析,比较了二元极性相关、三元极性相关和三元非极性相关法在时延估计精度和目标检测性能上的差异。仿真实验结果表明,相对于二元极性相关法,三元非极性相关法在信噪比为13dB到4dB范围内的时延准确估计概率提高了6%到14%,时延估计根方差减小了5到30个采样周期,检测性能上提高了近4 dB。但三元相关法在时延估计和目标检测上所需计算量较大,需要进一步研究相应的快速算法。     

时延估计后置处理算法的应用研究

时延估计是被动声定位的关键技术。受声信号传播起伏、声测量系统误差以及时延估计算法等均能影响时延估计的精度。因此,为了提高目标定位精度,需要对时延估计进行后置处理。本文研究了ba-递推估计后置处理算法在低空飞行武装直升机被动声定位中的应用,给出了典型飞行条件下的仿真结果。结果表明,采用后置处理算法可以有效地改善时延估值的精度。     

信号相位匹配原理的正弦信号参数总体最小二乘估计方法

利用信号相位匹配原理进行正弦信号最小二乘估计可提高低信噪比时的估计精度,在此基础上进行改进,应用总体最小二乘方法对正弦信号进行参数估计,能进一步提高精度.利用信号增强技术对观测序列进行预处理,能改善估计效果.仿真实验表明,本文方法性能优于最小二乘方法.     

On the subsample time delay estimation of narrowband ultrasonicechoes

Correlation based time delay estimators, optimal under certain conditions, exhibit the well-known threshold effect of poor performance at low signal-to-noise ratio (SNR). This sudden reduction in performance of the correlation based time delay estimators at low SNR arises from the misidentification of one unique “extremum” in very noisy conditions and from the peak fitting procedure in the case of the subsample time delay estimation. In this paper, two new estimators-the MSX and MXS-for the estimation of subsample time delays in narrow-band signals are proposed. In these estimators, cross-correlations and autocorrelations are matched at a number of different lags to yield a number of time delay estimates which are subsequently combined to obtain one robust time delay estimate. They seem to perform adequately over the SNR range used in simulations of -5 to 20 dB. Their performances are compared to those of two cross-correlation based estimators. Using simulated data, it is demonstrated that all four estimators perform well at high SNR, but at low SNR the proposed MSX and MXS estimators offer significant improvements in the bias and variance of the estimates. Additionally, these findings are verified using ultrasonic experimental data at three different SNR     

广义互相关时延估计声定位算法研究

针对地面目标声定位因信噪比较低而定位精度差的现象,提出了基于广义互相关法的声定位系统,根据平面四元法目标定位计算式,研究了广义互相关算法在实际中的应用。环境噪声、军事目标声和民用目标声频谱范围大多集中,即出现目标声会与背景噪声高度重合的情况,发现一般加窗滤波法在降低噪声的同时亦会将目标声强度大大削弱,而广义互相关时延估计法是通过计算两路信号互相关函数的最大值而求得时延差,其精度高、稳定性好。通过计算机仿真得到在信噪比较低的情况下,加窗滤波因大大削弱目标声强度而造成定位精度较低,而利用广义互相关方法可得到较为精确的声定位坐标。     

The combined effect of signal decorrelation and random noise on the variance of time delay estimation

Tissue motion and elasticity imaging techniques commonly use time delay estimation (TDE) for the assessment of tissue displacement. The performance of these techniques is limited because the signals are corrupted by various factors including electronic noise, quantization, and speckle decorrelation. Speckle decorrelation is caused by changes in the coherent interference among scatterers when the tissue moves relative to the ultrasound beam. In time delay estimation, the effect of noise is usually addressed through the signal-to-noise ratio (SNR) term. Decorrelation, often a significant source of error in medical ultrasound, is commonly described in terms of the correlation coefficient. A relationship between the correlation coefficient and the SNR was previously derived in the literature, for identical signals corrupted by uncorrelated random noise. In this paper, we derive the relationship between the peak of the correlation coefficient function and the SNR for two jointly stationary signals when a delay is present between the signals. Recently, an expression for the Cramer-Rao lower bound (CRLB) has been derived in the literature for partially decorrelated signals in terms of the SNR and the correlation coefficient. Since the applicability of the CRLB is determined not only by the SNR, but also by the correlation coefficient, it is important to unify the expression for the CRLB for partially correlated signals. In this paper, we derive an expression for the CRLB in term of an equivalent SNR converted from the correlation coefficient using an SNR-p relationship, and show this expression to be equivalent to the expression for CRLB. We also corroborate the validity of the SNR-p expression with a simulation. Using this formulation, correlation measurements can be converted to SNR to obtain a composite SNR. The use of this composite SNR in lieu of those in the CRLB expression in the literature allows the extension of the literature results to the solution of the common TDE problems that involve signal decorrelation.     

基于MUSIC算法的罗兰C接收机天地波自动识别方法研究

针对常规罗兰C接收机天地波识别方法不能有效利用信号能量的缺点，根据天波到来时间的估计与合成信号频率的分离相似的特点，提出了一种基于多信号分类算法估计天波延迟的方法．该方法在低信噪比条件下可分离出地波和天波，并能根据天波的延迟变化实时地选择接收机采样基准点的最佳位置．计算机仿真结果表明，该方法可以用于天地波的自动识别，能增加基准点处的信噪比，因而能显著提高罗兰C接收机的性能．     

互相关法时延估计中的多径抑制方法

浅海信道是一个多径信道,对于基于相关法的延迟估计,多径效应常常会导致相关函数中出现伪峰,伪峰的幅度有时会超过主峰,进而增加了相关峰选取的难度。利用 AR 法滤波对信号中的多径分量加以抑制,从而削弱了伪峰的幅度,提高了延迟估计的准确性。先从理论上论证了线性预测法可以对多径干扰进行抑制,再用仿真信号和实际信号验证了方法的效果。对于仿真信号,在无噪声干扰的条件下,选择合适阶数的滤波器可以使信号中的多径分量被完全抑制;对于正信噪比的实际信号,其中的多径分量在一定程度上被抑制,从而由多径分量产生的相关峰会被明显削弱,减少了峰值位置的跳变。     

Interpolation methods for time-delay estimation using cross-correlation method for blood velocity measurement

The cross-correlation method (CCM) for blood flow velocity measurement using Doppler ultrasound is based on time delay estimation of echoes from pulse-to-pulse. The sampling frequency of the received signal is usually kept as low as possible in order to reduce computational complexity, and the peak in the correlation function is found by interpolating the correlation function. The parabolic-fit interpolation method introduces a bias at low sampling rate to the ultrasound center frequency ratio. In this study, four different methods are suggested to improve the estimation accuracy: (1) Parabolic interpolation with bias-compensation, derived from a theoretical signal model. (2) Parabolic interpolation combined with linear filter interpolation of the correlation function. (3) Parabolic interpolation to the complex correlation function envelope. (4) Matched filter interpolation applied to the correlation function. The new interpolation methods are analyzed both by computer simulated signals and RF-signals recorded from a patient with time delay larger than 1/f(0), where f(0) is the center frequency. The simulation results show that these methods are more accurate than the parabolic-fit method. From the simulation, the worst estimation accuracy is about 1.25% of 1/f(0) for the parabolic-fit interpolation, and it is improved by the above methods to less than 0.5% of 1/f(0) when the sampling rate is 10 MHz, the center frequency is 2.5 MHz and the bandwidth is 1 MHz. This improvement also can be observed in the experimental data. Furthermore, the matched filter interpolation gives the best performance when signal-to-noise ratio (SNR) is low. This is verified both by simulation and experimentation.