水下机器人位姿控制中的精确极大似然配准算法研究

本文阐述了水下机器人位姿控制原理和多传感器信息融合的基本概念,着重研究了配准的极大似然算法(EML).该算法通过两步递归最优化方法来实现,并采用改进的高斯-牛顿法来确保算法的快速收敛性.最后利用水下机器人模型BSAV-Ⅰ水池实验的实时数据给出了仿真计算,验证了极大似然配准算法在实际环境中应用有效性.     

基于序贯最小二乘的多传感器误差配准方法

为实时估计多传感器系统偏差,针对广义最小二乘(GLS)配准方法不能实时估计传感器偏差的问题,提出了基于序贯最小二乘的多传感器误差估计方法,该方法在GLS配准模型基础上,采用最小二乘的序贯方法来估计系统偏差,不必存储过去的测量数据,能够实时估计系统偏差。仿真结果表明了该方法的有效性。     

异类传感器融合跟踪系统配准偏差的在线补偿

针对多传感器融合跟踪系统的时变配准偏差补偿问题,提出了一种配准偏差的在线估计和补偿算法.该算法首先依据多传感器提供的测量和跟踪信息,建立配准偏差的动态模型,然后利用极小化似然函数结合卡尔曼滤波方法在线估计系统偏差,利用估计的配准偏差,补偿和修正跟踪器的测量信息,实现多传感器的融合跟踪.最后针对异类传感器(雷达、红外)组成的多传感器跟踪系统,给出了应用该方法的仿真结果.     

无线传感器网络中的节点自适应周期定位

无线传感器网络节点自定位算法是无线传感器网络系统的重要组成部分,是无线传感器网络中所有应用得以实现的基础。基于最小二乘估计的自适应周期定位算法采用周期定位机制控制网络中节点定位,使用基于接收信号强度指示的测距技术获取节点间距离,启动定位周期,直至定位周期终止,完成定位。未知节点采用极大似然估计得到初解,使用最小二乘估计获得自身位置坐标的最终解。仿真实验表明,基于最小二乘估计的自适应周期定位算法能显著提高网络中未知节点的定位率,有效抑制测距误差的传播,提高了节点定位精度。     

基于传感器网络的水下声音源定位方法研究

提出一种分层结构的自组织无线传感器网络(WSN)用于水下声音源的定位研究,可以广泛应用于军事、民用监控等场景;在修正的声音源衰减模型基础上,提出一种改进的非线性最小二乘算法以及极大似然算法用于水下声音源定位;仿真试验对比研究了两种算法在不同的传感器节点以及背景噪声情况下对预估定位误差的影响;试验结果表明了这种分层结构的WSN用于水下声音源定位是可行的,同时验证了最小二乘算法以及极大似然两种算法定位的有效性。     

基于最小二乘法的K-NN航迹关联算法研究

航迹关联是分布式多平台数据融合系统中的一项关键技术,在民用空管系统、军用战场态势估计方面都有重要的应用。本文提出基于最小二乘法的K近邻域统计关联算法,用最小二乘法拟合航迹曲线,通过极大似然值来判断航迹是否关联。仿真实验表明这种算法在没有状态估计协方差的情况下是可行的。     

无线传感器网络中ORAWLS算法

为提高无线传感器网络(WSNs)节点定位的估计精度,提出了一种优化参考锚节点的加权最小二乘算法(ORAWLS).基于理论均方误差最小化,优化参考锚节点,进一步完善系统性能.仿真验证表明:对比最大似然(ML)算法和线性最小二乘(LLS)算法,ORAWLS算法能够有效地提高定位精度,并表现出良好的系统性能.     

基于高斯似然估计因子分析的点云配准算法

为解决三维点云数据在白噪声、数据不对应的情况下的配准问题,提出基于高斯似然估计因子分析的点云配准算法。采用因子分析法对点云数据进行表示,利用极大似然估计的方法求得因子载荷矩阵,从而完成对带噪声点云的配准。仿真实验表明,与CDP算法和Go-ICP算法相比,该算法不会陷入局部最小值,在快速精确配准和稳定性方面具有良好的鲁棒性。     

利用外辐射源的TDOA和FDOA目标定位算法

针对单站外辐射源条件下的目标定位问题,提出了一种基于最大似然的时差-频差联合定位算法。首先根据时差和频差的观测方程,构建目标位置和速度的最大似然估计模型。然后采用牛顿迭代算法对最大似然估计模型求解,得到目标位置和速度估计。最后,推导了算法的克拉美罗界和理论误差,并证明了二者相等。仿真结果表明,算法定位精度高于两步加权最小二乘算法和约束总体最小二乘算法,在测量误差较高时仍能达到克拉美罗界。通过对系统几何精度因子图的分析,确定目标及外辐射源数量和位置也是影响定位精度的重要因素。     

基于期望最大化与容积卡尔曼平滑器的机载多平台多传感器系统误差配准算法

针对机载多平台多传感器系统误差配准过程中出现的系统误差参数未知问题,本文提出了一种基于期望最大化(EM)与容积卡尔曼平滑器(CKS)的机载多平台多传感器系统误差配准算法.该算法将传感器的量测系统误差视为系统待估计的未知参数,构建了新的传感器量测方程.引入EM算法框架,在期望步(E–step)利用容积卡尔曼滤波器(CKF)和CKS近似计算对数似然函数的数学期望,在最大化步(M–step)对该数学期望进行最大化处理,最后通过解析更新反复迭代的方式获得各传感器系统误差的参数估计.数值仿真验证了本文提出算法的有效性.     

基于广义似然比的自适应在线配准算法

针对机载雷达配准时出现的偏差跳变问题,提出一种基于广义似然比(GLR)的在线配准算法.该算法通过对配准公式的测量残差进行检验,可以自适应地估计偏差跳变量.Monte Carlo仿真实验表明,与传统的配准算法相比,在偏差发生跳变时,该算法能迅速检测到跳变发生时刻并正确估计出跳变量的大小,偏差估计值可在较短的时间内收敛到跳变后的真实值,且估计精度较高,接近CR下界.     

基于改进头脑风暴优化算法的医学图像配准方法

针对精准医疗中图像配准方法收敛速度慢、精度不够高的问题,提出一种基于改进头脑风暴优化（Improved brain storm optimization, IBSO）算法的医学图像配准方法。配准过程分为3个阶段：首先,将待配准图像进行多分辨率分解;然后,使用IBSO算法对低分辨率图像进行全局粗配准;最后,利用单纯形搜索法对高分辨图像精配准。相比粒子群和单纯形结合算法、差分进化和Powell结合算法,以及头脑风暴和Powell结合算法,在单模态实验中,所提算法平均耗时较以上3种算法分别降低了32.89%、13.91%和13.66%,且最大误差、平均误差最小;在多模态实验中,互信息、归一化互信息、交叉累计剩余熵与归一化互相关指数均优于上述3种配准算法。实验结果表明,所提算法可以有效地提升医学图像配准的精度与速度。     

Exact maximum likelihood estimation of partially nonstationary vector ARMA models

A useful class of partially nonstationary vector autoregressive moving average (VARMA) models is considered with regard to parameter estimation. An exact maximum likelihood (EML) approach is developed on the basis of a simple transformation applied to the error-correction representation of the models considered. The employed transformation is shown to provide a standard VARMA model with the important property that it is stationary. Parameter estimation can thus be carried out by applying standard EML methods to the stationary VARMA model obtained from the error-correction representation. This approach resolves at least two problems related to the current limited availability of EML estimation methods for partially nonstationary VARMA models. Firstly, it resolves the apparent impossibility of computing the exact log-likelihood for such models using currently available methods. And secondly, it resolves the inadequacy of considering lagged endogenous variables as exogenous variables in the error-correction representation. Theoretical discussion is followed by an example using a popular data set. The example illustrates the feasibility of the EML estimation approach as well as some of its potential benefits in cases of practical interest which are easy to come across. As in the case of stationary models, the proposed EML method provides estimated model structures that are more reliable and accurate than results produced by conditional methods.     

Exact maximum likelihood estimation of partially nonstationary vector ARMA models

A useful class of partially nonstationary vector autoregressive moving average (VARMA) models is considered with regard to parameter estimation. An exact maximum likelihood (EML) approach is developed on the basis of a simple transformation applied to the error-correction representation of the models considered. The employed transformation is shown to provide a standard VARMA model with the important property that it is stationary. Parameter estimation can thus be carried out by applying standard EML methods to the stationary VARMA model obtained from the error-correction representation. This approach resolves at least two problems related to the current limited availability of EML estimation methods for partially nonstationary VARMA models. Firstly, it resolves the apparent impossibility of computing the exact log-likelihood for such models using currently available methods. And secondly, it resolves the inadequacy of considering lagged endogenous variables as exogenous variables in the error-correction representation. Theoretical discussion is followed by an example using a popular data set. The example illustrates the feasibility of the EML estimation approach as well as some of its potential benefits in cases of practical interest which are easy to come across. As in the case of stationary models, the proposed EML method provides estimated model structures that are more reliable and accurate than results produced by conditional methods.     

基于最小生成树的图像融合算法

研究遥感图像融合精度问题。图像融合存在含有冗余和互补信息,造成清晰度降低。针对传统的图像配准算法精度较低,为了提高遥感图像融合的准确度,提出了一种最小生成树遥感图像配准算法,将最小生成树算法应用到图像融合的优化过程中,算法首先提取均匀子采样点集,并在此基础上构造最小生成树,然后使用最小生成树来估计熵,对遥感图像进行配准,最后将图像间的边缘梯度信息融入到融合框架中。算法有效地克服了传统图像融合算法的缺点,仿真结果表明,改进算法有效地提高了图像融合的精确度,并为遥感图像融合提出了有效依据。     

A method for registration of 3-D shapes

The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of `shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces     

Robust Non-rigid Registration Based on Affine ICP Algorithm and Part-Based Method

The classical affine iterative closest point (ICP) algorithm is fast and accurate for affine registration between two point sets, but it is easy to fall into a local minimum. As an extension of the classical affine registration algorithm, this paper first proposes an affine ICP algorithm based on control point guided, and then applies this new method to establish a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided, meanwhile, the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.     

An Adaptive Fuzzy-Inference-Rule-Based Flexible Model for Automatic Elastic Image Registration

In this study, a fuzzy-inference-rule-based flexible model (FIR-FM) for automatic elastic image registration is proposed. First, according to the characteristics of elastic image registration, an FIR-FM is proposed to model the complex geometric transformation and feature variation in elastic image registration. Then, by introducing the concept of motion estimation and the corresponding sum-of-squared-difference (SSD) objective function, the parameter learning rules of the proposed model are derived for general image registration. Based on the likelihood objective function, particular attention is also paid to the derivation of parameter learning rules for the case of partial image registration. Thus, an FIR-FM-based automatic elastic image registration algorithm is presented here. It is distinguished by its 1) strong ability in approximating complex nonlinear transformation inherited from fuzzy inference; 2) efficiency and adaptability in obtaining precise model parameters through effective parameter learning rules; and 3) completely automatic registration process that avoids the requirement of manual control, as in many traditional landmark-based algorithms. Our experiments show that the proposed method has an obvious advantage in speed and is comparable in registration accuracy as compared with a state-of-the-art algorithm.     

二维点轮廓与矢量轮廓配准研究

在平面类零件的光学测量中,二维点轮廓与矢量轮廓的配准是关键算法,配准精度 直接影响测量精度。针对平面类零件的配准问题,提出了基于形状特征函数的粗配准算法和二维 矢量最近点迭代(ICP)精配准算法。利用角度距离图法将矢量图形的几何信息转化为独立于坐标系 的连续函数,进而实现粗配准算法。基于平面上点与曲线的最近距离算法计算配准目标函数,给 出了不同于传统的ICP 算法的直接求解目标函数的解析方法,有效提高了算法效率。利用实例验 证分析了该算法的高效性和可靠性。