星载加速度传感器的在轨运动影响

加速度传感器测量卫星所受非引力加速度的精度是利用该技术精确恢复重力场的重要指标。根据卫星运动理论,给出了轨道升交点赤经摄动、近升距摄动、卫星运动、坐标轴旋转引起的加速度性质以及相应表达式。针对CHAMP卫星轨道,讨论了各项的影响量级。     

基于光纤光栅的高精度测温传感器研究

利用光纤光栅对温度和应变同时敏感的特性,我们设计制作了一款满足地震前兆观测中地温观测精度要求的双金属光纤光栅温度传感器,并首次对温度增敏后的光纤光栅传感器样品进行了精度分析,得到该传感器的精度达到士0.05℃,获得了现今光纤光栅温度传感器最高的分辨率0.0014℃/pm.该传感器的成功研制从实验上证明了光纤光栅用于地震前兆观测的可行性,为地震前兆观测的光学化打下了坚实的基础.     

具有国际先进水平的CTD定标检测设备

在国家863计划的支持下，海洋技术研究成功研制了一套CTD仪器的校准设备，本文介绍了设备的组成，性能，设计经过与SBE设备进行了比较，表明该设备达到了EOCECTD设备的校准要求，同时，该设备是海洋技术研究所开发自有的高度CTD剖面仪的有力工具。     

基于ECR与ECI星历数据的遥感影像定位

经典的共线方程中物方坐标系建立在地球表面,在航天摄影测量中,地面摄影测量坐标系的应用,增加了不同类型测量数据间联合处理的难度,限制了影像数据摄影测量处理的空间范围。本文介绍了基于视线方向的影像对地定位算法原理,并以此为基础建立了地心系下的航天影像摄影测量共线方程。针对地固地心系和空固地心系下星历数据的不同点,建立了基于两种地心系下星历数据的航天遥感影像定位严密模型,并对两种模型中偏流角处理的不同点进行了分析,最后给出了一个应用试验。     

海洋环境无线传感器网络监测体系与应用技术研究

针对目前海洋环境监测中急需解决的主要技术问题.对海洋环境无线传感器网络监测的体系结构进行了研究,通过对Crossbow无线传感器网络的拓扑结构和软件架构进行分析与试验.提出其技术优势和有待解决的应 用问题.     

四种新型土壤墒情传感器的对比分析

土壤墒情监测工作日益得到重视。在北京上庄试验站选择四种不同工作原理和型号的土壤墒情传感器进行了为期半年的对比分析测试,利用监测数据对土壤墒情传感器的稳定性、灵敏性、准确率三个指标进行了分析,结果表明各类传感器在监测效果方面表现各有优缺点,均表现出了较好的稳定性,对降水反应也良好,准确率略有差别,监测数据基本在烘干法值附近上下波动。通过对比分析研究,对了解土壤墒情传感器性能和大范围推广应用具有一定的参考意义。     

Optimal instrumentation of structures on flexible base for system identification

A criterion previously developed by Heredia-Zavoni and Esteva for selecting optimal sensor locations is used to analyse the optimal instrumentation of structures on soft soils. The stochastic response of a linear structural system on a flexible base is formulated for use of the criterion. The case of MDOF shear systems on flexible base, with uncertain lateral stiffness and subjected to random earthquake ground motions, is studied. The optimal location of accelerometers, the reduction of prior uncertainty on the lateral stiffness, the effects of the base flexibility, the relative influence of translation and rocking of the base, and the influence of recording noise are assessed and discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

调频式电涡传感器微机实现的探讨

分析了分频和ＣＴＣ计数采样中误读数的原因和解决方法，并利用微机软件实现非线性校正。此传感器用于位移测量系统中，具有０．０１ｍｍ的分辨率及优于１％的精度，可在－２０～６０℃温度范围内工作。     

An advanced process for evaluating a linear dielectric constant–bulk electrical conductivity model using a capacitance sensor in field conditions

Abstract

The Hilhorst model was used to convert bulk electrical conductivity (σ_b) to pore water electrical conductivity (σ_p) under laboratory conditions by using the linear relationship between the soil dielectric constant (ε_b) and σ_b. In the present study, applying the linear relationship ε_b–σ_b to data obtained from field capacitance sensors resulted in strong positive autocorrelations between the residuals of that regression. We were able to derive an accurate offset of the relationship ε_b–σ_b and to estimate the evolution of σ_p over time by including a stochastic component to the linear model, rearranging it to a time-varying dynamic linear model (DLM), and using Kalman filtering and smoothing. The offset proved to vary for each depth in the same soil profile. A reason for this might be the changes in soil temperature along the soil profile.

Editor D. Koutsoyiannis; Associate editor M.D. Fidelibus     

Efficient seismic response data storage and transmission using ARX model‐based sensor data compression algorithm

This paper presents a linear predictor (LP)‐based lossless sensor data compression algorithm for efficient transmission, storage and retrieval of seismic data. Auto‐Regressive with eXogenous input (ARX) model is selected as the model structure of LP. Since earthquake ground motion is typically measured at the base of monitored structures, the ARX model parameters are calculated in a system identification framework using sensor network data and measured input signals. In this way, sensor data compression takes advantage of structural system information to maximize the sensor data compression performance. Numerical simulation results show that several factors including LP order, measurement noise, input and limited sensor number affect the performance of the proposed lossless sensor data compression algorithm concerned. Generally, the lossless data compression algorithm is capable of reducing the size of raw sensor data while causing no information loss in the sensor data. Copyright © 2005 John Wiley & Sons, Ltd.