Digital time-of-flight measurement for ultrasonic sensors

Ultrasonic sensor measurements are mostly based on the determination of the time of flight (TOF). The development of a digital algorithm for pulse-echo measurement applications, based on the use of a cross-correlation function to determine the TOF, is presented. Some experimental results are presented, and the possibility to realize a low-cost real-time measurement system is considered     

基于改进的Kalman滤波的语音增强算法

传统的kalman滤波方法在推导过程中假定观测噪声为白噪声。通常对于有色噪声需要用白噪声激励的方法予以模拟,并且需要以牺牲运算量作为代价。本文提出了一种改进的基于kalman滤波的语音增强算法,可以处理白噪声和有色噪声情况,不需要增加计算量,仿真结果表明了该算法对有色噪声的语音增强性能要优于基于传统kalman滤波方法。     

Ultrasonic time-of-flight estimation through unscented Kalman filter

This paper deals with distance or level measurements based on ultrasonic time-of-fight estimation. Moving from a past experience concerning the proposal of a method based on discrete extended Kalman filter (DEKF) to overcome some limitations of already available ultrasonic-based techniques, a new digital signal processing method capable of granting further improvements is presented. The method is based on the unscented Kalman filter (UKF), which is a new extension of the Kalman filter theory mandated to face some DEKF problems, mainly due to its inherent linearization approach. To this aim, UKF is applied to the acquired ultrasonic signal in order to estimate the returned echo envelope as well as to locate its onset more accurately. After describing key features and implementation issues of the new method, the results obtained in a number of tests on simulated and actual ultrasonic signals, which assess its reliability and effectiveness as well as advantages with respect to the previous one, are given.     

超声被动定位的一种新滤波方法

本文以距离几何学为基础，对超声被动定位提出了一种新的滤波方法，理论分析证明，利用这种方法，可以提高定位精度，本文还给出了数值的例子，说明如何使用这种新的滤波方法。     

基于自适应滤波的薄层厚度超声测量方法

针对应用超声脉冲回波法测量三层结构中的硅橡胶薄层厚度时回波混叠的问题,提出一种基于RLS(recursive least square)自适应滤波的解决方法。该方法将硅橡胶层下界面回波发生混叠的信号作为输入信号,将无硅橡层时的回波信号作为期望信号,通过RLS自适应滤波算法的处理提取出相互分离的有效下界面回波信号,实现硅橡胶薄层的测厚。研究不同滤波器阶数和遗忘因子对信号分离及测厚精度的影响,以输出信号的信噪比为指标选择最优的滤波参数。结果表明:该方法能够有效分离出发生混叠的硅橡胶薄层界下界面回波,能够测量0.15,0.17,0.19,0.21 mm 4种厚度的硅橡胶薄层,对回波部分混叠和完全混叠两种情况均有良好的适用性。     

基于极大似然估计的惯性测量组合标定方法

针对测试设备或平台框架转角误差( ASE)较大的情况,提出了一种用于惯性测量组合(IMU)标定的新方法.该方法采用相互正交的惯性器件输出的平方和作为观测信息,并以重力加速度ga和地速ωie的幅值平方作为参考基准来标定工具误差系数,这样能够消除ASE对标定结果的影响.同时针对误差项系数也较大的情况,给出了一种基于极大似然...     

An ultrasonic time-of-flight equivalent flaw sizing method

One of the challenging tasks in the field of ultrasonic NDE is to obtain quantitative flaw size, shape, and orientation information. In an equivalent flaw sizing approach, flaws in a material are reconstructed in terms of a best-fit simple shape such as an ellipsoid or ellipse. In this work, a new time-of-flight equivalent (TOFE) flaw sizing method is developed for obtaining best-fit equivalent flaws from a relatively small number of timing measurements at different transducer orientations. The performance of the method is demonstrated on synthetic numerical data and with experiments, including examples of sizing in anisotropic composite materials and in welds.     

基于自适应滤波的带宽测量方法研究

用自适应滤波(AF)算法替换带宽测量算法NEPRI中固有的往返时间(RTT)值的测量算法,提出了测量网络带宽的AF-NEPRI算法.采用AF-NEPRI算法测量链路可用带宽时,首先数据发送端发送不同速率的探测包,然后采用自适应滤波算法测量数据包的RTT值,通过相邻数据包的RTT差值计算带宽估测参数,最后估测链路可用带宽...     

基于Kalman滤波的多分辨率图像融合新算法

以尺度类似于时间,对具有不同分辨率的多幅图像建立起状态方程和观测方程;以标准Kalman滤波为工具,将具有不同性能与特点的图像进行融合,并给出了分块快速算法.利用估计误差绝对值均值对融合的性能进行了评估.多组实验与分析表明:所提出的图像融合算法不仅能有效的去除噪声和提高图像分辨率,而且通过图像融合,能够大大改善存在部分遮挡和恶劣天气等影响下获取的存在灰度、对比度变化的图像的性能.     

应用超声技术的燃烧气体温度测量方法的研究

高温气体瞬时温度的测量一直是温度测量中的难点，主要原因是温度高，压强大，温度变化速度快。传统的温度测量方法，无法实现温度的快速准确测量。在理想气体中，超声波的传播速度与气体绝对温度的平方根成正比，利用超声波频率差的测量测得气体介质的温度。基于此，本文提出了一种超声温度测量方法并实现了测量系统，同时解决了系统探头的安装、高速计数和数据的二次脉冲锁存问题。     

A method based on random-fuzzy variables for online estimation of the measurement uncertainty of DSP-based instruments

The evaluation of the uncertainty of the result of a measurement performed by a DSP-based instrument is usually a complex and difficult task. The sources of uncertainty are the instrument input stage and the analog-to-digital converter (ADC), so that each input sample to the instrument can be considered a measurement result with its associated uncertainty. The uncertainty on the final measurement result is obtained as the combination of the uncertainty values of each sample, according to the measurement algorithm implemented on the digital signal processing (DSP) system. This paper proposes an innovative approach, based on the representation of the measurement result and its associated uncertainty in terms of random-fuzzy variables, that, after having suitably characterized the metrological performance of the input stage and analog-to-digital converter, provides an online estimation of the measurement uncertainty. The method has been validated experimentally, and the results of the experimental work are reported.     

粒子滤波参数估计方法在齿轮箱剩余寿命预测中的应用研究

针对非线性非高斯系统的剩余寿命（RUL）预测问题,本文提出了一种基于粒子滤波（PF）理论的设备剩余寿命预测方法。首先建立设备的非线性状态空间模型（含有未知的时变参数）,然后通过粒子滤波算法估计出设备状态的概率密度函数（PDF）,从而根据该PDF计算出设备的RUL。此外,计算设备RUL的期望值和95%置信区间,并对模型的预测效果进行评估,验证预测的有效性和准确性。最后通过齿轮箱的全寿命实验,对本文所提方法的有效性进行实例验证,将实验结果和传统的比例风险模型（PHM）预测结果对比分析,结果表明本文提出的剩余寿命预测方法要优于传统的PHM预测方法。     

基于扩展卡尔曼滤波的轮式移动机器人定位技术

针对单一传感器或现有多传感系统在信息传递提取上的不足,应用一种信息融合方法,对机器人进行相对定位与绝对定位的融合分析,得出机器人的最优位置信息,最终实现了移动机器人的精确定位。首先,采用码盘、陀螺仪进行机器人相对定位,采用激光雷达进行机器人绝对定位;其次,建立环境地图、传感器及机器人运动模型;最后,以扩展卡尔曼滤波作为多传感器融合技术,建立多传感器信息融合模型,实现精确定位。     

Neural filtering of colored noise based on Kalman filter structure

In this paper, adaptive filtering approaches of colored noise based on the Kalman filter structure using neural networks are proposed, which need not extend the dimensions of the filter. The colored measurement noise is first modeled from a Gaussian white noise through a shaping filter. The Kalman filtering model of colored noise is then built by adopting an equivalent observation equation, which can avoid the dimension extension and complicated computations. An observation correlation-based algorithm is suggested to estimate the variance of the measurement noise by use of a single layer neural network. The Kalman gain can be obtained when a perfect knowledge of the plant model and noise variances is given. However, in some cases, the difficulties of the correlative method and the Kalman filter equations are the amount of computations and memory requirements. A neural estimator based on the Kalman filter structure is also analyzed as an alternative in this paper. The Kalman gain is replaced by a feedforward neural network whose weight adjustment permits minimization of the estimation error. The estimator has the capability of estimating the states of the plant in a stochastic environment without knowledge of noise statistics. If the noise of the plant is white and Gaussian and its statistics are well known, the neural estimator and the Kalman filter produce equally good results. The neural filtering approaches of colored noise based on the Kalman filter structure are applied to restore the cephalometric images of stomatology. Several experimental results demonstrate the feasibility and good performances of the approaches.     

结合Hough变换与Kalman滤波的车道线检测方法

车道线检测是智能驾驶系统的重要组成部分,它提供了车辆与车道位置关系的信息.针对智能车辆驾驶系统在视觉导航过程中车道线检测的精确性和鲁棒性的问题,提出一种有效的车道线检测方法.首先对原始RGB图像分别进行感兴趣区域设定、逆透视变换、灰度化和阈值处理;然后进行霍夫变换处理,利用斜率和中心点位置筛选检测结果;最后利用卡尔曼滤波对检测到的线段进行跟踪,预测当前车道线位置.实验结果表明,该算法能够有效解决图像中车道线不清晰以及一些干扰遮挡的问题,车道线检测准确率可达94%,具有较好的准确性、鲁棒性和较低的计算复杂度,有利于实时性检测系统的构建.     

非线性无序测量无迹滤波在地面跟踪目标指示器的应用

对于多传感器融合系统,在处理中心往往由于通信数据链的失误,出现滞后数据(即无序量测),这对数据不利于融合的正常进行.本文对此提出一种最优和次优的无序量测无迹卡尔曼(UKF)滤波器,基于UT变换,把多步滞后量测转化为等价一步滞后量测,用无序量测直接滤波更新.通过最优、次优UKF滤波器和扩展KF滤波器在不同情况下对地面跟踪目标指示器(GMTI)组合跟踪的无序量测处理作比较,可看出本文所述滤波器提高了非线性条件下的无序量测滤波精度.     

基于模糊自适应Kalman滤波的机械手动态图像雅可比矩阵辨识

提出了一种改进的基于模糊自适应Kalman滤波的动态图像雅可比矩阵辨识方法.该方法在机器人参数和滤波参数未知而且视觉成像模型动态变化的情况下,通过模糊逻辑自适应控制器在线监测滤波残差均值和残差协方差误差,对过程噪声参数Q和量测噪声参数R进行自适应调节,实现了未知环境下动态图像雅可比矩阵的稳定辨识.通过微装配机械手运动实验验证了该方法的有效性.     

Accurate distance measurement by an autonomous ultrasonic systemcombining time-of-flight and phase-shift methods

This paper presents an efficient algorithm for distance measurement, combining both the pulse time-of-flight method and the CW phase-shift method, It copes with a low-rate sampling technique allowed by the limited bandwidth of two ultrasonic transducers working in air at 30 kHz and with modest software resources of autonomous devices. The measuring system was implemented and tested on a compact Motorola MC68HC16-based platform, with a minimum of attached hardware. Experimental results show an accuracy better than 1 mm for a poor reflecting target at a distance of about 1 m     

A control chart based approach to monitoring supply network dynamics using Kalman filtering

In today's global market, a critical issue for companies to survive the increasing competition is how to handle uncertainty in their supply network. In this paper, we explore the application of Kalman filtering to estimate the dynamic states in a supply network. Two state-space models are developed. The first one focuses on processing each individual order which includes both waiting time and value-added processing time. Considering the correlation of consecutive orders, the second one enhances the state-space model by employing autoregressive model of waiting time. To signal potential abnormal events, the estimates from the models are further used in control charts with control limits being updated at each monitoring stage according to the related estimation error. A supply network case example is studied and we conclude in the benchmark model (without using Kalman filtering) and the first state-space model, the data collected from the bottleneck stage turns out to be most valuable for increased accuracy in detecting tardy orders. The second state-space model consistently outperforms both the benchmark model and the first state-space model for robustly early detection of abnormalities at all stages, especially the stages before the bottleneck stage, of the system.