几种改进的LMS自适应时间延迟估计方法

本文分析研究了几种改进的ＬＭＳ自适应时间延迟估计方法，其中ＭＬＭＳ方法和归一化变步长方法均对输入信号做了归一化处理，扩展了时延估计器的动态范围。两种加窗方法是基于对输入信号的特性和自适应滤波器的特点的分析提出的，可以使自适应迭代的计算复杂度大大下降。     

自适应Notch滤波器在相位估计中的应用

本文阐述了自适应Ｎｏｔｃｈ滤波器的基本原理，并应用于超短基线水声定位系统的相位估计中，采用了先进的信号处理器ＴＭＳ３２０１０及自适应ＬＭＳ算法，模拟试验结果证明，这种相位估计方法是可行的，经过若干改进后可付诸实用。     

随机系统稳健自适应控制

对具有未建模动态特性且时滞任意的多维随机系统,通过采用有界外来激励和随机变界截尾方法,本文建立了推广最小二乘（ＥＬＳ）算法和加权ＥＬＳ算法的稳健估计,进而得到间接自适应模型参考控制的稳健性     

分析海浪方向谱的扩展本征矢方法

采用Ｌｏｎｇｕｅｔ－Ｈｉｇｇｉｎｓ形式的方向分布函数作为已知谱，用模拟数据检验了作者是所提出的估计方法ＥＥＶ合理性，并与扩展最大似然方法（ＥＭＬＭ）及Ｌｙｇｒｅ等（１９８６）的最在熵方法（ＭＥＭ）作了比较，在验证和比较中，使用纵摇－横摇浮标，星形阵形和ＣＥＲＣ阵列作为复合阵列，计算表明，ＥＥＶ优于ＥＭＬＭ和ＥＭＥ。最后将ＥＥＶ和ＥＭＬＭ两种方法应用于仪器阵列的外海观测数据，得到了比较合理的海浪方向     

LMS自适应时间延迟估计

本文研究了基于widrow的最小均方(LMS)算法的自适应时间延迟估计的基本原理、结构和算法,分析了这种估计方法的性能,并给出了计算机模拟的结果。在此基础上,讨论了自适应时间延迟估计器与广义相关时延估计算法中Roth处理器的关系,并利用维纳滤波器把二者联系和统一起来。     

平均海平面常设办事处（PSMSL）对收存资料的检查

平均海平面常设办事处（ＰＳＭＳＬ）对收存资料的检查１资料检查在以前，平均海平面常设办事处（ＰＳＭＳＬ）从未收到过有关潮汐的逐时潮高测量或连续记录图件的复制品，而收到的是却是月平均值和年平均值。这些资料是由国家有关部门提供的，是经过准确的计算后得出的，...     

时间延迟估计的基本原理和方法

本文综述时间延迟估计的基本原理和基本方法,包括相关法和广义相关法,双谱法和广义双谱法,相位谱法和广义相位谱法,自适应信号处理法等,并对各种估计方法的性能进行了分析比较。     

海洋对热带气旋响应的研究

利用中二层非线性原始方程海洋模式，研究海洋在自身不同热力结构下对热带气旋的响应。计算结果表明，初始混合层（ＭＬ）深度和层强强度对海表温（ＳＳＴ）和ＭＬ深度变化起着十分重要的作用。初始ＭＬ深度对海流量值影响较大，层结强度则较小。东海陆架区特殊的海洋热力结构，极易造成ＳＳＴ下降。海洋对７００２号台风响应的模拟结果与观测资料较一致。     

顶空气相色谱法测定海水二甲基硫和浮游植物细胞二甲基硫丙酸的研究

建立了顶空ＧＣ／ＦＰＤ测定海水中二甲基硫（ＤＭＳ）和浮游植物细胞中二甲基硫丙酸（ＤＭＳＰ）的方法，并研究盐度、温度、气液相比ＤＭＳ诸因素对ＤＭＳ顶空灵敏度的影响。该法对ＤＭＳ测定的相对标准偏差均小于６％，平均回收率为１０６％，最低检出限为２０ｎｇ／Ｌ。细胞ＤＭＳＰ先经碱作用转化为ＤＭＳ，在５０℃下作用时间不少于６ｈ，峰高与浓度的双对数线性相关系数大于０．９９。对１９９４年冬、１９９５年夏采自胶州湾     

带乘性噪声系统双滤器平滑及反褶积算法

在带乘性噪声系统（ＳＭＮ）间接平滑算法的基础上,通过构造一种新的ＳＭＮ逆向信息滤波算法,建立了ＳＭＮ的双滤器平滑和反褶积算法;为解决ＳＭＮ最优估计问题提供了新的方法。     

Spatio-temporal processing of coherent acoustic communication datain shallow water

Achieving reliable underwater communication in shallow water is a difficult task because of the random time-varying nature of multipath propagation. When the product of Doppler-related signal bandwidth spread and multipath-related time spread of the channel is larger than one, some types of adaptive signal processing may not work very well. In this paper, various methods of coherent space-time processing are compared for a condition of a marginally overspread channel operating at 50 kHz. Various combinations of suboptimal spatially adaptive and time adaptive methods are considered. The coherent path beamformer (CPB) and recursive least squares (RLS) adaptive beamformer, both in combination with RLS time filtering, are analyzed. Also considered in the analysis is the combined RLS space-time optimal adaptive processor. Many experiments using broad-band phase-shift-keyed transmissions in shallow water have been conducted to provide data for testing these various processing methods. Because of the rapid time variation of the multipath, the product of bandwidth spread and time spread at this test site approached unity. In this environment, a suboptimal approach consisting of the adaptive beamformer followed by RLS equalization reduced reverberation and transmission errors     

Two adaptive algorithms for multipath time delay estimation

The problem of time delay estimation (TDE) with multipath transmissions arises often in many sonar and radar systems. Two adaptive algorithms based on a parameter estimation approach are proposed to estimate the difference in arrival times of a signal at two separated sensors in the presence of multipath propagation. The first method uses an adaptive IIR filter to eliminate the multipath signal in each transmission channel prior to applying a constrained delay estimation algorithm to extract the time difference between the two received outputs. The second employs two constrained adaptive FIR filters to perform equalization of the multipath arrivals, and time delay is then derived using a constrained delay estimator similar to that in the first method. Computer simulations are presented to compare and contrast the tracing capability and convergence behavior of these multipath TDE methods     

基于总体最小二乘自适应切片的点云孔洞修补

针对普通孔洞修补方法在修补复杂曲面的孔洞无效或者所修补的曲面不光滑等缺点,提出基于自适应切片的孔洞修补方法。首先,在考虑点云局部特征的情况下对整体点云进行自适应切片,在得到切片后引入总体最小二乘进行拟合,最后,在线上取点填充缺失的点云。实验结果表明,采用自适应切片法修补孔洞能够顾及到点云的曲率和密度变化,保持点云的局部特征,用总体最小二乘法拟合切片得到的孔洞填充点云精度较高,光滑性好。通过对比常规方法,可以得到基于总体最小二乘自适应切片方法修补效果更优。     

基于数据挖掘的GF-1 遥感影像绿潮自适应阈值分区智能检测方法研究

由于受到云雾的影响,可见光影像能够高效用于绿潮检测的数据源较为有限,特别是云覆盖较为严重的可见光影像,基本无法用于检测绿潮。即使影像数据是在薄云、薄雾、无云覆盖的情况下获取的,由于其光谱反射值存在较大差异,依然很难采用同一阈值进行绿潮检测。基于此,为了提高可见光影像的利用率,实现不同云覆盖情况下,绿潮的高精度自适应阈值的自动检测,本文以GF-1影像为数据源,首先采用K-means聚类和C4.5决策树方法实现影像云覆盖情况的自动识别;其次,选取大量不同云覆盖情况下子图像样本（每个子图像样本中均包含绿潮和海水两类）,分析得出不同云覆盖情况下绿潮和海水的区分阈值y与影像光谱差x=band_nir-band_red之间所具有的线性关系;然后,利用分析得出的线性关系提出一种适用于GF-1影像的绿潮分区自适应阈值自动检测方法。最后,为验证提出方法的有效性,分别采用NDVI方法、EVI方法和本文提出的自适应阈值自动检测方法进行绿潮提取实验。实验结果表明,对于GF-1卫星遥感数据,本文提出的绿潮自适应阈值分区自动检测方法明显优于传统的NDVI和EVI检测方法,不仅提高了绿潮的监测精度,而且实现了绿潮提取的全自动化。     

Study of Seabed Pullout Resistance on a Quadrate Foundation Using Adaptive Time-Stepping Method

We developed a solid-fluid analysis procedure that can simulate the seabed pullout resistance based on two adaptive time-stepping methods. One is heuristic time stepping, and the other is based on the truncation error estimation in temporal discretization. The pullout resistance is investigated through experiments and numerical simulations. The results show that the proposed adaptive stepping methods can deal with the un-convergence problems caused by the time increment and can improve the calculation efficiency. The base suction, related to the area of the negative pore water pressure, is generated under the structure and provides the pullout resistance.     

水声信号时频分析方法比较及应用研究

浅海中的宽带水声信号传播呈现出频散的特点,通过高分辨率的时频分析方法可以刻画频散曲线。通过数值仿真和实验数据处理,对比分析几类常用的时频分析方法在提取宽带声信号频散曲线方面的性能。结果表明:STFT时频局部化精度不够高;在较强频散的情况下,DSTFT时频分辨率较高。WVD时频聚集性最好,但是有严重的交叉项干扰;固定核函数的CWD较好地抑制交叉项,时频分辨率虽优于STFT,但弱化了时频聚集性;AOK时频分布采用自适应高斯核函数,在抑制交叉项的同时,时频聚集性较好,有望较好地用于提取信号频散曲线。     

Spatially and temporally varying adaptive covariance inflation for ensemble filters

Ensemble filters are used in many data assimilation applications in geophysics. Basic implementations of ensemble filters are trivial but are susceptible to errors from many sources. Model error, sampling error and fundamental inconsistencies between the filter assumptions and reality combine to produce assimilations that are suboptimal or suffer from filter divergence. Several auxiliary algorithms have been developed to help filters tolerate these errors. For instance, covariance inflation combats the tendency of ensembles to have insufficient variance by increasing the variance during the assimilation. The amount of inflation is usually determined by trial and error. It is possible, however, to design Bayesian algorithms that determine the inflation adaptively. A spatially and temporally varying adaptive inflation algorithm is described. A normally distributed inflation random variable is associated with each element of the model state vector. Adaptive inflation is demonstrated in two low-order model experiments. In the first, the dominant error source is small ensemble sampling error. In the second, the model error is dominant. The adaptive inflation assimilations have better mean and variance estimates than other inflation methods.     

Low complexity adaptive equalizers for underwater acoustic communications

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.     

A Quantitative Method to Control and Adjust the Accuracy of Adaptive Grid Depth Modeling

To address the limitations of current methods to control and adjust the accuracy of depth models and relatively low accuracy, a quantitative method to control and adjust the accuracy of adaptive grid depth modeling is proposed. First, uncertainties in source data and interpolated depths are estimated, and the representation uncertainty derived from finite and discrete points representing the continuous seafloor surface is analyzed. Second, mean vertical uncertainty in an arbitrary given area is calculated. Finally, interpolation of the depths at grid nodes from source data and the distribution framework of the grid nodes are optimized in each local area, and an adaptive grid depth model is created according to the expected accuracy. The experimental results demonstrate that (1) the proposed method can control and adjust the accuracy of the depth model in each local area such that the accuracy of the constructed model meets the requirements of the expected index as closely as possible and (2) the proposed method can improve the accuracy of the depth modeling by optimizing the interpolation and distribution of the grid nodes.     

Assimilation of the data of observations and adaptive prediction of the natural processes

We propose a dynamical model for the prediction of random components of natural processes. The model is based on the system concept of adaptive balance of causes (ABC-model) and contains dynamic equations for the coefficients of influence adapted to the correlations existing in the predicted processes. To improve the accuracy of predictions, we consider two possible schemes of assimilation of the data of observations in the equations of the ABC-model, namely, the Kolmogorov and Kalman schemes. Both schemes are oriented toward the application of sample correlation coefficients for the prediction of time series of measurements and, hence, take into account the nonstationarity of actual natural processes. We present some examples of prediction of the simulated time series clarifying the algorithms of assimilation of the data of observations. A conclusion is made that the methods of systems modeling and adaptive prediction of random processes by the ABC-method are quite promising.