传感器测量衰减下的水下目标纯方位跟踪算法

为解决多传感器水下目标纯方位跟踪中的传感器测量衰减问题,建立水下目标静态多传感器纯方位跟踪模型,将传感器测量衰减建模为统计特性已知的随机变量,基于融合中心接收到的各水声传感器的原始测量值,设计了一种集中式状态估计器结构,利用最小方差方法推导出最优的集中式目标状态估计增益。通过算例仿真可以得出,所提出的算法能够在水声传感器不做机动的前提下跟踪目标,弥补了单个水声传感器观测性不足的缺点,对比传统的集中式Kalman估计器,具有更高的精度,能够有效解决传感器测量衰减问题。     

Velocity-Aided Underwater Navigation System Using Receding Horizon Kalman Filter

This paper discusses the problems related to constructing a receding horizon filter for underwater inertial navigation systems which are subject to external disturbances. Noises are assumed to be bounded, additive, and contained in both state and measurement equations. An estimator is designed according to the sliding-window strategy to minimize the receding horizon estimation cost function. The derived filter is applied to a velocity-aided inertial navigation system. Simulations show that the derived filter is more accurate than the standard Kalman filter (KF) for underwater navigation systems subject to temporary unknown disturbances     

多波束数据处理系统技术研究

针对当前多波束测深数据成果现状,探讨以Oracle 9i数据库为基础,搭建ArcGIS 8.3的SDE为空间数据引擎的多波束水深空间数据库,结合ComGIS技术开发多波束数据处理系统。系统功能包括测深数据的输入、管理、分析、显示、制图和绘图输出,改变了一般基于文件方式的多波束水深数据处理方式,成功的将空间数据库技术融入系统中,并有效运用了ComGIS开发系统前端平台,丰富了系统功能,具有一定的参考价值。     

时变水声信道中基于BEM的迭代信道估计技术

水声信道的多径时延扩展和时变特性对信道估计和均衡技术的研究带来了很大的挑战,同时也决定了水声信道是一种时频双扩展信道,提出一种水声OFDM通信系统中基于软信息的迭代信道估计技术,利用基于复指数基扩展模型(CE-BEM)进行信道估计。OFDM系统本身可以消除由于多径引起的符号间干扰(ISI)。基于导频的BEM信道估计,可以实现对时变信道的估计,结合基于软信息迭代的迭代均衡模块,将每次迭代生成的符号软判决信息作为辅助导频用于信道估计。同时,为了防止由于信道时变引起的信道子载波间干扰(ICI)对导频符号的影响,采用基于保护间隔的导频插入法插入导频。仿真结果显示基于BEM的软信息迭代信道估计性能较非迭代信道估计时明显提升。     

Monitoring moored instrument motion by optimal estimation

Many oceanographic applications require the positioning of the underwater sensor at measurement times. We consider here the case of subsurface moored tomographic instruments, where the distance between source and receiver must be known within a few meters. For that purpose, a long baseline array is deployed: this system includes a navigator, attached to the mooring element and an array of three transponders set on the ocean bottom. To process the navigation data collected with such system, we have developed a method based on optimal estimation. The triangulation problem is not a basic spherical constraints one and the specificity of deep underwater positioning, related to the variability of the ocean sound speed profile are pointed out. Correcting terms are proposed and introduced into the system. Simultaneous inversion of all data, defining an overconstrained problem allows to estimate biases and errors. The algorithm is applied here to a dataset collected in the Azores-Canary basin during CAMBIOS experiment.     

An evaluation of least-squares and closed-form dual-angle methods for CODAR surface-current applications

Extraction of surface currents from first-order CODAR sea echo requires use of a model that allows signals from two bearings to contribute to the Doppler spectrum at a given frequency. This is called the dual-angle situation, and it applies over much of the coverage area. Two dual-angle techniques have appeared in the literature: a least-squares algorithm used with a crossed-loop antenna system, and a closed-form approach applied to a four-element square array. We evaluate these methods against realistic signal and noise scenarios encountered in CODAR operations, and study noustatistical biases remaining after infinite-ensemble averaging of the input voltage cross-spectral data. Based on these simulations, biases produced with the closed-form methods exceed those for the crossed-loop system analyzed with least squares by typically 150 percent.     

Performance comparison of high resolution bearing estimationalgorithms using simulated and sea test data

The performance of both the Capon and the MUSIC high resolution bearing estimation algorithms is investigated using both simulated data and sea test data collected with an experimental planar array. The major problem with these estimators is their sensitivity to both system errors and deviations from the assumed noise model. To alleviate this problem, two methods for preprocessing the data before they are input into the high-resolution algorithm are investigated: beam space and sector focused stability. The performance of both high-resolution estimators is examined, using both types of preprocessing, and the results are compared with those for the standard element-space (ES) techniques, assuming both finite cross-spectral-matrix (CSM) averaging errors and weakening target strengths. For the Capon estimator the performance is only superior to the standard element space technique when the CSM is calculated using a small number of averages. For the MUSIC estimator, both preprocessing techniques give clearly superior results over standard space techniques, with the SFS preprocessor performing the best     

A closed-form solution to bearings-only target motion analysis

Bearings-only target motion analysis is a nonlinear state estimation problem in which the noise corrupted angle of arrival measurements of an emitted signal are used to obtain estimates of the source's range, bearing, course, and speed. The estimation process is complicated by unusual observability properties that render the quality of the estimate highly dependent on both the measurement noise levels and the source-observer geometry. Solutions that use recursive Kalman filtering approach or batch-style algorithms have been reported. The nonlinear batch style estimators for this process require iterative solution methods and under certain scenarios can be sensitive to initial conditions. Pseudolinear solutions that alleviate some of the difficulties with the iterative batch algorithms have been proposed. Although early versions of the pseudolinear filter suffered from biased estimates, subsequent improvements appear to have reduced the bias problem. This paper discusses a new pseudolinear solution based on the observable parameters from individual data segments defined by periods of constant observer velocity (termed “legs”). This solution is a true closed-form solution to the bearings-only target motion analysis problem. Although theoretically interesting, the technique does suffer under conditions of poor observability. A practical pseudolinear estimate, that does not suffer from the same observability problems, is developed and related to the first solution. Algorithm performance results, obtained from computer simulation, are presented. For the scenarios examined, the technique provides good state estimates under conditions of high observability. As observability conditions deteriorate, the solution does develop biases. However, it may still be useful for initializing an iterative nonlinear batch-style estimation algorithm     

Constitutive model for predicting stress-strain behavior of fine-grained sediments using shear-wave velocity

Abstract

The mechanical response of a sediment to an applied stress is significantly affected by variations of material properties, state conditions, and stress states. These stress state and conditions are utilized to infer input parameters for advanced soil constitutive models. Parameters such as void ratio and effective stresses have been readily inferred from shear-wave velocities under low-strain conditions. Thus, this research aimed to develop a shear-wave velocity-based constitutive model within a critical state soil mechanics framework to predict the undrained triaxial behavior of fine-grained sediments. Laboratory tests were performed for sediment samples ranging from silt-predominant to clay-predominant sediments. As result, a new two-term power function was developed that determined mean effective stress as a function of shear-wave velocity. By virtue of this new power function, the Original Cam Clay and Modified Cam Clay critical state models were adapted to estimate the stress-strain behavior and stress paths under undrained conditions, in terms of shear velocity. In addition, correlations were developed using the state and material properties to predict the input model parameters. The developed correlations allow broad application of the proposed framework to different sediment types in which clay and silt are the dominant deposits.     

双层BP人工神经网络连接权值的性质

从理论上分析和探讨具有Ｓ型连结函数的双层ＢＰ网络连接权值和阈值的性质，并进行实例验证，本文有助于神经网络内部行为的研究。     

Observation bias correction with an ensemble Kalman filter

This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme—the local ensemble transform Kalman filter (LETKF)—to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations.     

An integrated sensory-intelligent system for underwater acoustic signal-processing applications

A generic integrated sensory-intelligent system (ISIS) is developed for underwater acoustic signal-processing applications. ISIS constantly monitors the current acoustic channel conditions and smoothly integrates the outputs of the most appropriate signal-processing procedures or algorithms available to it for those conditions. The system is based on a generalization of a tuneable approximate piecewise linear (TAPL) model derived from the modified probabilistic neural network (MPNN). This model was designed to seamlessly integrate a set of local linear signal-processing algorithms within a given multidimensional data space. Depending on the input signal distortions, which are determined by environmental effects, ISIS automatically weighs and adds the outputs from a set of processing algorithms working in parallel. The weighting is related to the "closeness" of each algorithm to the sensed input signal characteristics or some other measured environmental state. A single tuning parameter is used to smoothly and seamlessly select appropriately among the parallel processing algorithm outputs. A very small tuning-parameter value selects the closest most appropriate algorithm output. At the other extreme, a fixed weighted average of all the algorithm outputs is produced with a very large value. Otherwise, a dynamic weighed average of all algorithm outputs is achieved with values in between. Some features and benefits of ISIS are demonstrated with an illustrative linear sweep chirp signal-detector estimation problem characterized by extremely variable Doppler conditions.     

带乘性噪声系统状态滤波的自适应算法

带乘性噪声系统由于其广泛的适用性,一直成为研究的热点。针对带乘性噪声系统状态最优估计的自适应算法进行研究,探讨在噪声服从平稳正态分布情况下,对未知动态噪声方差阵与观测噪声方差阵的辨识问题。在证明带乘性噪声系统新息在稳态时和线性系统新息有着相似稳定特性的前提下,通过对线性系统辨识方法的改进,完成对带乘性噪声系统噪声方差阵的辨识,并利用新息特性对该方法进行进一步改进,以提高辨识精度;最后通过仿真验证该方法的有效性。     

Statistical assessment of ocean observing networks – A study of water level measurements in the German Bight

A set of tools for the statistical assessment of ocean observing networks is presented and applied for the analysis of different instrumentation scenarios in the German Bight. An optimal linear estimator is used to re-construct ocean state parameters from observations taking into account both the prior distribution of the state and measurement errors. The proposed method enables a re-construction of any scalar parameter or vector field with linear relationship to the state. The performance of the observing network is quantified in terms of the re-construction quality. Apart from the capability of the network to provide estimates of state parameters at the time of the observations, the potential of the measurements for forecasts is investigated as well. Furthermore, a generic method to compare single measurements with continuous observations is presented. Finally, a technique is described to quantify the relative importance of different components of an observational network.The proposed methods are applied to water level measurements in the German Bight. A numerical model is used to estimate the background statistics. Synthetic measurements provided by tide gauges, satellite altimeters, and HF radar are considered in the analysis. The estimation of the complete water level field in the German Bight is compared for altimeter and tide gauge measurements. It is shown that the orientation of the satellite track with respect to the coastline is of high relevance. The importance of water level measurements taken in deeper water, e.g., at the FINO-1 platform, is demonstrated. It is shown that continuous tide gauge measurements provide more information on the area mean water level in the German Bight than altimeter observations taken by ENVISAT and JASON-1/2. It is furthermore shown how the information provided by a tide gauge propagates with the Kelvin wave. Implications for the design of an assimilation scheme are discussed.     

基于ATmega16A的低功耗高精度桥式温度测量系统设计

当前温度测量应用越来越广泛,针对工农业及科研领域对低功耗及高精度测温的要求,系统描述了一个桥式测温系统的设计方案与工作原理。系统采用低功耗AVR单片机作为控制芯片,通过单臂分压将热敏电阻分压后的电压信号传入A/D转换芯片,再将A/D转换后的数字量送入单片机计算,从而得到热敏电阻阻值,最后通过曲线拟合算出所测温度值。系统能实现低功耗、高精度测温。     

An assessment of fuzzy logic vessel path control

A fuzzy logic controller for ship path control in restricted waters is developed and evaluated. The controller uses inputs of heading, yaw rate, and lateral offset from the nominal track to produce a commanded rudder angle. Input variable fuzzification, fuzzy associative memory rules, and output set defuzzification are described. Two maneuvering situations are evaluated: track keeping along a specified path where linearized regulator control is valid; and larger maneuvers onto a specified path where nonlinear modeling and control are required. For the track keeping assessment, the controller is benchmarked against a conventional linear quadratic Gaussian (LQG) optimal controller and Kalman filter control system. The Kalman filter is used to produce the input state variable estimates for the fuzzy controller as well. An initial startup transient and regulator control performance with an external hydrodynamic disturbance are evaluated using linear model simulations of a crude oil tanker. A fully nonlinear maneuvering model for a smaller product tanker is used to assess the larger maneuvers     

线性关联滞后广义大系统的变结构控制

研究了一类线性关联滞后广义大系统的变结构控制综合问题.首先根据系统的结构和控制输入的特定形式,引入了1种新的受限系统等价分解形式,把每个子系统分解成2个低维的动态子系统：1个是不带控制项的线性关联微分差分系统,另1个是带有控制项的线性关联差分系统.然后设计了带有积分动态补偿器的切换函数, 给出了滑动模渐近稳定的充分条件.最后设计了可以保证系统的解的轨迹到达切换流形的多层分散变结构控制.这种控制对物理上分散等类型的线性关联滞后广义系统是合理的有效的.     

基于虚拟仪器技术的海洋移动气象观测系统

介绍一种自主研发的、基于Labview虚拟仪器技术的海洋移动气象观测系统,它可广泛用于各种舰船、海洋移动平台上,能够有效地去除安装平台的移动给风参数测量造成的影响,从而给出真风速、真风向、温度、湿度、气压、安装平台移动轨迹、速度等参数,并能够实时地显示相关参数的随时间的变化趋势,为海洋气象的研究及预报工作提供真实有效的原始数据分析资料。     

基于半仿真实验系统的调谐质量阻尼器等效阻尼力测量

为了研究调谐质量阻尼器(tuned mass damper,TMD)在海洋平台冰激振动中的减振效果,提出一种基于半仿真实验系统的TMD等效阻尼力测量方法,利用该系统可以直接测试结构安装TMD装置时产生的作用力.介绍该实验系统的测试原理及等效阻尼力的测量方法,并在正弦位移输入下,对TMD装置进行了测试,比较等效阻尼力的实测值与计算值,结果证明利用该系统可以测量得到TMD的等效阻尼力.