水声传感器网络移动节点定位技术

针对水声传感器网络的移动节点定位问题,首先研究了基于距离测量值的多边定位方法(Multilateral Localization,ML);然后利用节点运动信息,提出采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)进行跟踪的方法;最后针对水下移动节点的测量值不同步问题,提出了修正扩展卡尔曼滤波(Modified Extend Kalman Filter,MEKF)以改进EKF的精度。仿真分析结果表明,MEKF的定位精度要好于EKF,而EKF和MEKF由于其用到了节点的运动信息,因此其定位精度要远好于ML。     

EKF和PF算法及其在目标跟踪中的应用

基于贝叶斯滤波的目标跟踪原理,介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)和粒子滤波(ParticleFilter,PF)的基本思想和算法实现步骤。在非线性环境下对比分析了EKF算法和PF算法的估计精度,并给出两种方法的适用条件。EKF算法采用Taylor展开的线性变换来近似非线性模型,而PF算法采用一些带有权值的随机样本来表示所需要的后验概率密度。仿真结果表明,在强非线性非高斯环境下,PF算法的跟踪性能远优于EKF算法,当系统非线性强度不大时,EKF算法和PF算法的估计精度相差不大,但PF算法计算复杂,跟踪时间长,实时性差。     

基于UWB/INS的单兵班组协同定位算法研究

针对单兵作战系统易受室外GPS(Global Positioning System)信号遮挡失锁难以定位与INS自主定位容易发散的问题,提出一种基于单兵之间相互测距信息的EKF(Extended Kalman Filter)班组协同定位算法.利用角速度均方根阈值判别法进行单兵零速修正,UWB(Ultra Wideband)进行相互测距的同时传输协作单元位置估计和协方差信息,通过构造距离量测方程在零速区间协同定位.实测试验对比单兵INS定位算法与基于相互测距信息的EKF班组协同定位算法,试验结果表明:基于UWB/INS的单兵班组协同定位算法明显优于单兵INS算法自主定位,其位置误差减小了一个数量级,有效抑制了单兵INS定位误差累积.该算法可以在我军反恐作战、抢险救灾等极端环境下单兵班组协同定位中使用.     

基于EKF-LESO级联观测的永磁直线同步电机无传感器线性自抗扰控制

为克服永磁直线同步电机(Permanent-Magnet Linear Synchronous Motor, PMLSM)敏感扰动、易受噪声影响诱发抖振的不足,摆脱位置传感器对电机伺服系统的束缚,构建了一种以线性自抗扰控制器(Linear Active Disturbance Rejection Control, LADRC)为核心、扩展卡尔曼滤波算法(Extended Kalman Filter, EKF)和线性扩张状态观测器(Linear Extended State Observer, LESO)级联观测的PMLSM无传感器矢量控制系统。首先从理论上分析了控制系统的误差来源,通过推导得出在输入量测信号存在慢时变偏差的情况下,LESO对除第1阶和第n+1阶外的"中阶"状态进行观测时观测精度不受影响的结论;进而以此为依据,提出了EKF+LESO的级联观测方式,搭建了无传感器位置环自抗扰控制系统。通过仿真对LESO的容错性、对控制系统的跟随能力和噪声抑制能力进行了验证,实验结果则进一步表明,所设计系统能够准确实现直线电机的速度观测,系统运行平稳,具有较好的实用性。     

基于二阶转换测量Kalman滤波的目标跟踪算法

为了减小传统跟踪滤波算法线性化误差,提高光电跟踪系统的跟踪速度和跟踪精度,本文在三维空间中,提出了二阶去偏转换测量卡尔曼滤波算法.该算法利用二阶泰勒展开的方法,推导出了光电跟踪系统观测方程的转换测量值误差的均值和协方差矩阵表达式,并对测量误差进行去偏差补偿处理,再经过转换测量卡尔曼滤波,可显著减小传统滤波算法的线性化误差.仿真结果表明,二阶去偏转换测量卡尔曼滤波(SCMKF)算法的跟踪精度优于非去偏转换测量卡尔曼滤波(CMKF)和扩展卡尔曼滤波(EKF),以及unscented卡尔曼滤波(UKF)算法,并且具 有更快的收敛速度,和采用统计方法的去偏转换测量卡尔曼滤波(DCMKF)的跟踪精度相当,但计算简单,提高了跟踪速度.     

基于部分加速度测量的结构Bouc-Wen非线性恢复力及质量识别

结构非线性行为识别是结构灾后损伤评估的关键。扩展卡尔曼滤波(Extended Kalman Filter,EKF)有助于解决结构动力响应测量不完备的问题,但一般要求结构质量已知。针对仅部分加速度响应已知和结构质量未知情况下结构非线性恢复力的识别问题,提出一种结合EKF和最小二乘算法的结构非线性恢复力及质量识别的迭代算法。该方法基于质量估计值和部分自由度上的加速度响应测量,通过EKF预测完整响应时程,再利用最小二乘法识别修正质量分布,循环迭代至收敛,最后基于质量收敛值实现物理参数(刚度、阻尼、非线性)的识别,进而得到非线性恢复力。以一个含Bouc-Wen磁流变阻尼器的多自由度体系的数值模型为例,考虑4种不同的质量初始误差,通过数值模拟验证该方法识别结构质量及非线性恢复力的有效性。同时考虑加速度测量噪声的影响,证明了该方法的鲁棒性。     

基于UKF的简化交互多模型视频图像机动目标跟踪算法

在视频图像运动目标的状态估计与跟踪问题中,常用的扩展卡尔曼(EKF)算法简单、计算量小,但仅适用于弱非线性和弱高斯环境下.本文提出一种基于无迹卡尔曼滤波(UKF)与简化交互多模型(IMM算法相结合的视频图像运动目标跟踪算法,有效地克服了EKF算法在强非线性状态下或对小运动目标跟踪时精度低,容易发散的问题.仿真结果表明,该算法估计和跟踪非线性目标的性能明显优于基于EKF算法,其跟踪精度可达到三阶(泰勒级数展开)精度.     

一种自适应EPF算法及其在光纤水听器中的应用

基于椭圆拟合的相位生成载波(Phase Generated Carrier,PGC)解调方法是消除非线性因素对光纤水听器PGC解调结果影响的一种有效手段,椭圆曲线参数的最优估计问题是实现该方法的关键。扩展卡尔曼粒子滤波(Extended Kalman Particle Filter,EPF)是解决此类非线性估计问题的一种常用的最优估计算法。但传统的EPF算法在用于常参数过程方程的参数或状态估计问题时,过程噪声的方差通常设置为一个常量,这使得算法难以兼顾收敛速度和估计精度,一定程度上限制了算法的整体性能。为了解决这个问题,文章对现有的EPF进行了改进,提出了一种自适应扩展卡尔曼粒子滤波(Adaptive Extended Kalman Particle Filter,AEPF)算法。模拟仿真和实验结果表明,文中所提出的AEPF算法能根据基于椭圆拟合的PGC解调方法有效地解调出待测声信号,相比EKF算法和EPF算法,AEPF算法的收敛速度和估计精度都得到了提升。此外,文章所提出的AEPF算法也适用于其他具有常参数过程方程的参数或状态估计问题,具有一定的通用性。     

基于STUKF的非线性结构系统时变参数识别

针对非线性结构系统时变参数识别问题,传统无迹卡尔曼滤波(Unscented Kalman Filter,UKF)难以有效跟踪结构参数的变化。将强跟踪滤波原理引入无迹卡尔曼滤波,提出一种强跟踪无迹卡尔曼滤波(Strong Tracking Unscented Kalman Filter,STUKF)算法,以识别结构参数的变化。在UKF量测更新后,依据输出残差计算渐消因子矩阵;引入两个渐消因子矩阵实时调整状态预测协方差矩阵,使残差序列强行正交,快速修正结构参数估计值,使STUKF具有对结构参数变化的跟踪能力;此外,为节省计算时间,调整状态预测协方差矩阵后不再进行sigma点采样,保证了算法的高效性。数值分析结果表明,该算法能有效识别非线性结构系统的参数及其变化,并具有较强的抗噪性。     

基于改进扩展卡尔曼滤波算法的静电目标跟踪

针对静电探测的数学模型结构复杂、强非线性以及实验测量数据存在极大不确定性的特点和 ExtendedKalman Filter(EKF)在处理强非线性的测量方程时会出现滤波发散的现象.为了提高滤波精度和减少计算复杂度,采用中心差分的方法计算EKF中的非线性函数的一阶导数,并结合球形静电探测器实际探测的特点形成一种新的改进的EKF算法.将改进后的EKF应用于静电目标的跟踪,建立目标跟踪滤波器.理论分析和仿真表明,采用改进后的EKF与EKF和Unscented Kalman Filter(UKF)相比较,虽然计算时间比EKF稍有增加,但比UKF的计算时间少;而计算精度比EKF有显著提高,与UKF的计算精度相当.     

Extended Kalman filter for estimating aircraft orientation from velocity measurements

A coordinated flight model for estimating the orientation of an aircraft under track from velocity measurements into an extended Kalman filter (EKF) framework is placed here. In doing so, it makes two contributions. First, the EKF provides a rigorous framework for addressing this problem, blending modelling error and measurement error. Second, the EKF supplements the estimated orientation with a measure of the uncertainty in that estimate. Such estimates of uncertainty are crucial in a number of applications, including using the orientation estimates to approximate the radar cross section of the aircraft under track, in an attempt to identify targets. The EKF's performance is demonstrated using both a straight-and-level manoeuvre and a complicated manoeuvre recorded on-board a manoeuvring F-15. In both cases, the state estimates of the EKF are similar to the results obtained from a coordinated flight model. The true orientations almost always fall within one standard deviation of the estimates, as determined by the estimated covariance.     

基于卡尔曼滤波的摄像机标定方法

本文提出了一种基于迭代扩展卡尔曼滤波的摄像机标定方法.将二维平面靶标图像上的特征点看作是匀速运动的点,以观测到的特征点图像坐标和对应世界坐标作为滤波器的输入,摄像机内外参数的估计值作为滤波器的输出,根据迭代扩展卡尔曼滤波算法得到摄像机内外参数的最优估计.通过仿真和真实实验,结果表明,对于有限数量的平面靶标标定图像数据,该算法具有较高的标定精度和较好的鲁棒性.     

Energy distribution of trapping and transport states in MDMO-PPV ([poly-(2-methoxyl, 5-(3,77dimethyloctyloxy)] para phenylenevinylene)

We have investigated charge carrier transport and trapping in the layers of [poly-(2-methoxyl, 5-(3,77dimethyloctyloxy)] para phenylenevinylene (MDMO-PPV). To reveal distribution of the trapping states the thermally stimulated current method was applied using the varying excitation conditions by light and applied voltage. To assure the selective excitation of the defect states close to the band gap edges, both extrinsic and intrinsic excitation by the light passed through the long-pass color filters with the cut-off energies ranging from 1.77 eV up to 3.1 eV was employed. Carrier transport conditions were varied by increasing applied electric field from 5 x 10(4) V/cm up to 6 x 10(5) V/cm. The effective thermal activation energy of material conductivity was dependent both on the spectral region of the exciting light and applied electric field. The superposition of several thermally activated processes, i.e., carrier generation from the trapping states and thermally stimulated mobility growth according to the Gaussian disorder model, was revealed. The energy distribution of the trapping state density was shown to follow the Gaussian distribution function. We had demonstrated that carrier trapping is effectively influenced by the extended defect states with the effective activation energy values ranging from 0.05 eV up to 0.15 eV with maximum located at about 0.07-0.08 eV. Moreover, deeper states with activation energies of 0.28-0.3 eV and 0.8-0.85 eV were identified. The results are direct indication by photo-thermo-electrical methods of distributed in energy trapping and transport states with the standard deviation of the density of states of about 0.015 eV.     

Minimum Error Entropy Based EKF for GPS Code Tracking Loop

This paper investigates the minimum error entropy based extended Kalman filter (MEEKF) for multipath parameter estimation of the Global Positioning System (GPS). The extended Kalman filter (EKF) is designed to give a preliminary estimation of the state. The scheme is designed by introducing an additional term, which is tuned according to the higher order moment of the estimation error. The minimum error entropy criterion is introduced for updating the entropy of the innovation at each time step. According to the stochastic information gradient method, an optimal filer gain matrix is obtained. The mean square error criterion is limited to the assumption of linearity and Gaussianity. However, non-Gaussian noise is often encountered in many practical environments and their performances degrade dramatically in non-Gaussian cases. Most of the existing multipath estimation algorithms are usually designed for Gaussian noise. The I (in-phase) and Q (quadrature) accumulator outputs from the GPS correlators are used as the observational measurements of the EKF to estimate the multipath parameters such as amplitude, code delay, phase, and carrier Doppler. One reasonable way to obtain an optimal estimation is based on the minimum error entropy criterion. The MEEKF algorithm provides better estimation accuracy since the error entropy involved can characterize all the randomness of the residual. Performance assessment is presented to evaluate the effectivity of the system designs for GPS code tracking loop with multipath parameter estimation using the minimum error entropy based extended Kalman filter.     

Improved GNSS Cooperation Positioning Algorithm for Indoor Localization

For situations such as indoor and underground parking lots in which satellite signals are obstructed, GNSS cooperative positioning can be used to achieve high-precision positioning with the assistance of cooperative nodes. Here we study the cooperative positioning of two static nodes, node 1 is placed on the roof of the building and the satellite observation is ideal, node 2 is placed on the indoor windowsill where the occlusion situation is more serious, we mainly study how to locate node 2 with the assistance of node 1. Firstly, the two cooperative nodes are located with pseudo-range single point positioning, and the positioning performance of cooperative node is analyzed, therefore the information of pseudo-range and position of node 1 is obtained. Secondly, the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase. Finally, the cooperative location algorithms are studied. The Extended Kalman Filtering (EKF), Unscented Kalman Filtering (UKF) and Particle Filtering (PF) are used to fuse the pseudo-range, ranging information and location information respectively. Due to the mutual influences among the cooperative nodes in cooperative positioning, the EKF, UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time. Experimental results show that after being improved, the influence between the cooperative nodes becomes smaller, and the positioning performance of the nodes is better than before.     

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.     

Numerical studies of backscattering enhancement of electromagnetic waves from two-dimensional random rough surfaces with the forward-backward/novel spectral acceleration method

The forward-backward method with a novel spectral acceleration algorithm (FB/NSA) has been shown to be a highly efficient O(Ntot) iterative method of moments, where Ntot is the total number of unknowns to be solved, for the computation of electromagnetic (EM) wave scattering from both one-dimensional and two-dimensional (2-D) rough surfaces. The efficiency of the method makes studies of backscattering enhancement from moderately rough impedance surfaces at large incident angles tractable. Variations in the characteristics of backscattering enhancement with incident angle, surface impedance, polarization, and surface statistics are investigated by use of the 2-D FB/NSA method combined with parallel computing techniques. The surfaces considered are Gaussian random processes with an isotropic Gaussian spectrum and root-mean-square surface heights and slopes ranging from 0.5 lambda to lambda and from 0.5 to 1.0, respectively, where lambda is the EM wavelength in free space. Incident angles ranging from normal incidence up to 70 degrees are considered in this study. It is found that backscattering enhancement depends strongly on all parameters of interest. America     

Studies on In-pWSe2 Schottky diode by current-voltage-temperature method

Two types of In-pWSe₂ Schottky barrier diodes were fabricated, one on as grown (uncleaved) and the other on cleaved WSe₂ surface. Current-voltage characteristics of these diodes have been analyzed over a wide span of temperature ranging from 140 K to 300 K on the basis of thermionic emission theory with Gaussian distribution model of barrier height. Below 200 K, a model has been considered where the total current is assumed to be the sum of thermionic emission, generation recombination and tunneling components. The observed deviation in barrier height, ideality factor and Richardson plot below 200 K are interpreted in terms of the contribution of these multiple charge transport mechanisms across the interface of the fabricated diodes.