感知传感器的高精度高分辨率采集系统设计

随着小型化、高精度、低功耗、智能化感知传感器的快速发展,传统采集系统已无法满足高精度要求.设计一套能够测量感知传感器输出微弱电信号的高精度高分辨率采集系统,该系统选用了CodexM4系列处理器STM32F411与分辨率为24位的串行模数转换芯片AD7714,提出了一种基于改进型滑动平均滤波算法的滤波方案.详细介绍了该系统的软硬件设计方案,最后利用模拟感知传感器进行了原理样机的静态精度测试,测试结果表明,基于感知传感器的高精度高分辨率采集系统能够满足高精度、实时性好、稳定可靠的设计要求.     

基于RS485的高精度倾角传感器的设计与实现

根据MEMS加速度计倾角测量原理,设计了一种基于RS485串口通信的高精度倾角传感器。分别对该系统的硬件设计和软件设计进行阐述。倾角传感器的硬件设计主要分为MCU模块设计、传感器模块设计、电源模块设计、A/D转换模块设计和RS485数据输出模块设计;软件部分主要是下位机的程序设计。在系统中,采用手动标定的方法对数据进行标定,并利用分段线性插值法对角度进行补偿,经过多重滤波以达到良好的精度和线性。测量数据表明,该倾角传感器达到测量范围±90°、分辨率0.1mil、测量误差控制在0.1mil以内的高精度性能要求。     

基于CS5460A的高精度数据采集系统的设计

阐述了基于高精度串行A／D转换器CS5460A的数据采集系统的设计及实现方法。硬件方面,简单介绍了单片机与各芯片的接口设计及CS5460A与传感器的接口电路设计;软件方面,介绍了该系统的主程序流程图,并重点介绍了如何采用软件滤波的方法来提高系统的抗干扰能力。该系统中,单片机与上位机之间采用MODBUS协议进行通讯,并详细叙述了CRC冗余算法的实现问题。软件滤波和CRC的使用,使该系统精度得到了极大地提高,可靠性更强,适应了工程的需要。     

STM32的通信天线方位角实时快速测量系统设计

设计了一种基于STM32+ 上cGUI架构的方位角测量系统.设计了以STM32F103ZET6控制器为核心的控制模块和TFTLCD显示模块硬件电路,分析了传感器模块WDD35 D4的测角原理.软件部分移植了独立于CPU和LCD控制器的μcGUI,实现数据可视化界面设计;同时比较了滑动平均滤波、滑动中值滤波和加权滑动平均滤波对系统随机噪声的滤波效果,实验结果表明采用滑动平均滤波效果更佳.     

红外气体传感器信号调理及数据处理

针对红外气体传感器易受外界光源、电磁等干扰影响,其输出信号中存在大量噪声而影响检测精度的问题,设计了一种红外气体传感器信号调理及数据处理电路。该电路采用硬件和软件相结合的方法,在硬件上设计了信号调理电路,在保证信号真实性的同时具有非常好的放大滤波功能;在软件上采用中位值平均滤波算法对信号进行处理,可有效克服信号中混入的噪声,且对周期性和热噪声产生的干扰也有很好的抑制作用。试验结果表明,该电路可有效滤除传感器中的大量噪声,准确提取有用信号,提高检测精度及稳定性,满量程精度可达0.54%。     

基于DSP的光纤气体传感器信号处理技术

针对以往光纤气体传感器模拟信号处理电路的不足,基于DSP芯片TMS320F2812设计并实现了一种嵌入式光纤气体传感器信号处理系统;重点描述了硬件实现方法及其软件开发,其硬件主要由接口电路、高精度运放器OPA139P,32位高性能定点DSP(数字信号处理器)TMS320F2812和D/A转换芯片DAC7625组成;软件部分的主要功能是产生交流信号,完成两路输入信号滤波,相敏检波,相除、线性化处理等工作.     

电容传感器的信号处理及其在机床主轴热变形测量中的应用研究

利用电容式传感器,设计一个高精度位移测量系统,对机床主轴的热变形量进行非接触式测量.根据被测对象的特点,阐明了系统的工作原理和数据的处理方法,分析了影响传感器测量精度、稳定性的因素,提出了利用电子学技术和数字信号滤波技术提高传感器精度的技术手段,为机床主轴热变形的高精度、非接触式测量提供了一种技术途径.经长时间实验分析...     

激光遥感图像高速滤波器算法的FPGA实现

针对激光遥感信号高速高精度的要求,利用FPGA的多任务、高速并行性设计系统进行滤波。考虑到激光光电传感器设计的滤波器会有很大的信号衰减,且具有精度要求高的特点,提出了基于 FPGA内部基本逻辑单元、乘法器、存储器,经过乘法分解和时序设计,实现具有可移植性的图像高速分辨的方法。系统采用FPGA设计平台,并用Verilog语言设计程序。试验结果验证,该滤波器对激光遥感信号具有高速、精确滤波功能,同时表明利用低端FPGA设计激光滤波器必将成为未来发展趋势。     

基于FPGA的便携式多路高精度采集系统设计

为满足对旋翼桨叶表面多测点灵敏气压传感器信号的高速、并行、高精度数据采集,同时具备程控增益放大、抗混叠滤波等功能,设计了一种以FPGA作为核心控制单元的便携式多路高精度前置采集系统。待测信号先由信号调理和滤波模块处理,以提升信号质量,然后经过ADC采样模块采集,最后通过SPI总线将采集数据实时传送至主控板卡分析处理,实现多路信号同步高精度采集。测试结果表明,该系统性能稳定,在干扰较大的环境下,小信号采集幅度精度达0.1%,无杂散动态范围达60 dBc。系统采用便携化设计,体积小,成本低,扩展性强,具有很好的应用价值。     

TDC-GP2在磁致伸缩传感器中的应用

为了解决在磁致伸缩传感器中时差测量模块精度不高的问题,系统采用高档8位单片机ATmega128控制高精度时间测量芯片TDC-GP2,实现了脉冲时差的精确测量.详细介绍了TDC-GP2在磁致伸缩传感器应用中的软硬件设计,给出了硬件电路原理图、软件流程图及关键代码,并对设计中的注意事项进行了说明.同时,软件采用数字滤波消除了随机干扰和脉冲干扰.该方案有效地实现了时差测量模块对前端信号的精确测量.     

基于SNMP的IPv6无线传感器网络信息管理平台的设计与实现

无线传感器网络(Wireless sensor networks)是随机分布的传感器节点通过分布式协议自组织方式形成的网络,网络管理是保障无线传感器网络稳定、可靠、高效运行的重要条件。讨论研究了现有无线传感器网络管理技术,并结合无线传感器网络的特点和实际需求,在已有IPv6无线传感器网络底层协议及SNMP(简单网络管理协议)的基础上,设计和实现了一个切实可行的符合无线传感器网络特点的节点信息管理平台,以实现用户对无线传感器网络的远程有效管理。     

无线传感器网络智能红外控制节点的实现

无线传感器网络(WSN)是一种全新的信息获取和处理技术,其巨大的应用前景受到学术界和工业界越来越广泛的重视。红外控制技术以其通用性强、价格低廉的优点在生活中应用普遍。将无线传感器网络与红外控制技术相结合,设计了一种具有智能红外控制功能的无线传感器网络节点,该节点利用红外功能模块对红外信号学习并智能存储到EEPROM中,然后按需将预先存储的红外编码发送,达到控制红外终端的目的。另外,利用简单网络管理协议(SNMP)服务器可以对节点的红外控制功能实现远程控制。最后,对新设计的传感器节点进行了测试,证明了该方案的可行性。     

Distributed Filtering for Multi-sensor Systems with Missing Data

This paper studies distributed estimation problems for multi-sensor systems with missing data. Missing data may occur during sensor measuring or data exchanging among sensor nodes due to unreliability of communication links or external disturbances. Missing data include random missing measurements of sensor itself and random missing estimates of neighbor nodes. Three distributed Kalman filter (DKF) algorithms with the Kalman-like form are designed for each sensor node. When it is available whether a datum is missing or not at each time, an optimal DKF (ODKF) dependent on the knowledge of missing data is presented, where filter gains and covariance matrices require online computing. To reduce online computational cost, a suboptimal DKF (SDKF) is presented, where filter gains and covariance matrices dependent on missing probabilities can be computed offline. When it is unavailable whether a datum is missing or not, a probability-based DKF (PDKF) dependent on missing probabilities is presented. For each DKF algorithm, an optimal Kalman filter gain for measurements of sensor itself and different optimal consensus filter gains for state estimates of its neighbor nodes are designed in the linear unbiased minimum variance (LUMV) sense, respectively. Mean boundedness of covariance matrix of the proposed ODKF is analyzed. Stability and steady-state properties of the proposed SDKF and PDKF are analyzed. Also, the performance of three DKF algorithms is compared. Simulation examples demonstrate effectiveness of the proposed algorithms.     

基于分布式有限感知网络的多伯努利目标跟踪

针对感知范围受限的分布式传感网多目标跟踪问题, 在多伯努利滤波跟踪理论基础上提出分布式视场互补多伯努利关联算术平均融合跟踪方法. 首先, 通过视场互补扩大传感器感知范围, 其中, 局部公共区域只互补一次以降低计算成本. 其次, 每个传感器分别运行局部多伯努利滤波器, 并将滤波后验结果与相邻传感器进行泛洪通信使得每个传感器获取多个相邻传感器的后验信息. 随后, 通过距离划分进行多伯努利关联, 将对应于同一目标的伯努利分量关联到同一个子集中, 并对每个关联子集进行算术平均融合完成融合状态估计. 仿真实验表明, 所提方法在有限感知范围的分布式传感器网络中能有效地进行多目标跟踪.     

Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments

Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be significantly enhanced using the proposed method.     

SNMP在嵌入式操作系统(VxWorks)中的实现

简要分析了UCD—SNMP协议栈源码，并在此基础上介绍了SNMP简单网络管理协议在嵌入式操作系统的(VxWorks)的实现，从而提出了在各种嵌入式操作系统实现SNMP的方法。     

Impact of AVHRR filter functions on surface temperature estimation from the split window approach

With the development of long-term remotely-sensed data sets through the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder programme, decadal length studies of the land surface are being conducted. However, data from five different Advanced Very High Resolution Radiometer (AVHRR) instruments and thus five different filter functions for each thermal channel must be used. If variations in sensor filter functions are not taken into account when the split window technique is applied, considerable error in the estimation of land surface temperature can be produced. For example, the Becker and Li (1990) equation was designed to be used with data from the AVHRR instrument on the NOAA-9 satellite. If it is applied to data from an AVHRR sensor other than NOAA-9's, the error in surface temperature can be as large as 2.3 degK.     

A sequential Bayesian approach to color constancy using non-uniform filters

This paper introduces a non-uniform filter formulation into the Brainard and Freeman Bayesian color constancy technique. The formulation comprises sensor measurements taken through a non-uniform filter, of spatially-varying spectral sensitivity, placed on the camera lens. The main goal of this paper is twofold. First, it presents a framework in which sensor measurements obtained through a non-uniform filter can be sequentially incorporated into the Bayesian probabilistic formulation. Second, it shows that such additional measurements obtained reduce the effect of the prior in Bayesian color constancy. For the purposes of testing the proposed framework, we use a filter formulation of two portions of different spectral sensitivities. We show through experiments on real data that improvement in the parameter estimation can be obtained inexpensively by sequentially incorporating additional information obtained from the sensor through the different portions of a filter by Bayesian chaining. We also show that our approach outperforms previous approaches in the literature.     

跨平台的SNMP研究与实现

监控系统通常使用SNMP方法探测网络设备运行状态。而在不同的操作系统下，SNMP协议的具体实现方法各不相同，提供的编程接口也相差很大，本文对此进行深入研究并提出了一种可以跨平台的通用的SNMP实现方法。文章首先阐述了SNMP的基本概念，并分别介绍了不同平台下使用的SNMP方法，描述了在空中交通管制自动化系统中所实现的跨平台SNMP的思想和实现方法。     

在体测定小鼠肌肉组织中钙离子针型传感器

钙离子针型传感器是根据涂丝微电极的原理,以针灸针作为基体,将一定量的敏感材料、增塑剂、添加剂等溶于溶剂中制成敏感膜溶液,采用仿双层类脂膜(BLM)技术使其固定于针灸针尖端上而制成的钙离子针型传感器。用该传感器测葡萄糖酸钙片溶液的钙离子浓度时,其线性范围为10-5～10-2mol/L,响应时间为5s左右。本实验通过将该针型传感器直接插入小白鼠大腿肌肉组织中,在体测定了小鼠服用葡萄糖酸钙片后肌肉中钙离子浓度的动态变化过程。