Algorithms and statistical analysis for linear structured weighted total least squares problem

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

Novel passive localization algorithm based on weighted restricted total least square

A novel multi-observer passive localization algorithm based on the weighted restricted total least square （WRTLS） is proposed to solve the bearings-only localization problem in the presence of observer position errors. Firstly, the unknown matrix perturbation information is utilized to form the WRTLS problem. Then, the corresponding constrained optimization problem is transformed into an unconstrained one, which is a generalized Rayleigh quotient minimization problem. Thus, the solution can be got through the generalized eigenvalue decomposition and requires no initial state guess process. Simulation results indicate that the proposed algorithm can approach the Cramer-Rao lower bound （CRLB）, and the localization solution is asymptotically unbiased.

Properties of the total least squares estimation

Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.

Total least-squares EIO model,algorithms and applications

A functional model named EIO(Errors-In-Observations) is proposed for general TLS(total least-squares)adjustment. The EIO model only considers the correction of the observation vector, but doesn't consider to correct all elements in the design matrix as the EIV(Errors-In-Variables) model does, furthermore, the dimension of cofactor matrix is much smaller. Iterative algorithms for the parameter estimation and their precise covariance matrix are derived rigorously, and the computation steps are also presented. The proposed approach considers the correction of the observations in the coefficient matrix, and ensures their agreements in every matrix elements. Parameters and corrections can be solved at the same time.An approximate solution and a precise solution of the covariance matrix can be achieved by corresponding algorithms. Applications of EIO model and the proposed algorithms are demonstrated with several examples. The results and comparative studies show that the proposed EIO model and algorithms are feasible and reliable for general adjustment problems.

基于改进无迹卡尔曼滤波的锂电池荷电状态估计

锂电池是一种广泛应用的能源器件,高效的荷电状态估计是锂电池安全管理的基础。为了提高荷电状态估计的精度,提出了一种联合估计方法。首先,采用递归限制总体最小二乘法辨识模型参数,解决了传统递推最小二乘法存在辨识偏差导致准确性降低的问题;接着,提出了基于权重优化的无迹卡尔曼滤波算法,提高了荷电状态估计的精度;最后,引入北京公交动态压力测试工况的仿真实例对锂电池放电状态进行建模,并通过与3种先进方法进行比较,验证了所提方法在精度和收敛速度方面的优越性。

非均匀采样潜艇耐压壳体测量数据处理方法

在役潜艇的建造和修理需要检测其耐压壳体的截面圆度,因现场条件限制或多设备融合测量,测量采样点经常呈现非均匀性,导致最小二乘圆拟合的结果与实际情况不符,研究旨在减小非均匀采样带来的影响,提高拟合圆的精度。基于非均匀采样定权提出一种非均匀采样加权总体最小二乘圆拟合方法,同时考虑观测向量和系数矩阵都有扰动,用于潜艇耐压壳体测量数据的处理。通过对4种不同预设形状的采样分析数值实验表明,该方法能提高非均匀采样情况下圆拟合的精度,但当随机误差加大至1.5 mm,该方法不能完全优于最小二乘法。非均匀采样加权总体最小二乘圆拟合法计算得到的圆心偏差及半径偏差都比最小二乘法的结果更小,具有较好的工程实用价值,可用于在役潜艇耐压壳体圆度测量非均匀采样时的圆拟合计算。

Robust structured total least squares algorithm for passive location

A new approach called the robust structured total least squares(RSTLS) algorithm is described for solving location inaccuracy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares(WSTLS)framework and improved based on the robust estimation theory.Moreover, the improved Danish weight function is proposed according to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations show that when outliers appear, the result of the proposed algorithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.

水质氨氮、水质总磷校准曲线加权问题探讨

探讨了水质氨氮、水质总磷校准曲线应用普通最小二乘法(ordinary least square,OLS)和加权最小二乘法(weighted least square,WLS)拟合后对测定结果的不同影响。基于分光光度法测定水质氨氮、水质总磷的检测方法,对一年内两项目分别累计的6条校准曲线进行异方差检验,在检验有异方差现象后,通过加权最小二乘法对曲线进行拟合,对比WLS和OLS两种曲线方程在同一吸光度下推算得值与标准样品值的相对误差绝对值|δ|高低。两项目OLS曲线|δ|值在大多数浓度点都低于WLS曲线|δ|值。在0<|δ|<1范围内两项目OLS曲线|δ|值个数明显较多,|δ|>1范围内两项目WLS曲线|δ|值个数明显较多。针对实验室水质氨氮、水质总磷两项目,即使校准曲线存在异方差,通过OLS拟合的曲线也能较准确地推算结果。

半变异函数模型参数估计的综合定权法

针对半变异函数参数估计时点对数法定权未能顾及滞后距的影响,可能无法反应滞后距较小几个点重要性的问题,对多项式回归法的权重进行优化。通过平行四边形法则综合点对数和滞后距的影响,给出一种综合定权法。以球面模型为例,通过不同定权方法估计半变异函数模型参数,实验结果表明综合定权法各点权重更合理,克里金估值也更接近实测值。

一种基于双影响因子的软基沉降时序模型

为进一步提高软基沉降预测精度,对利用钻探取样、单轴固结实验、渗透—三轴固结联合实验、经验公式建立的固结沉降时序模型以及通过稳健加权总体最小二乘算法拟合得到的区块沉降时序模型进行组合,得到一种以时间为自变量、基于固结沉降和区块沉降双影响因子的软基沉降时序模型。将模型应用于广州市南沙区某软基地面监测点沉降预测中,结果表明该模型较好地反演了软基沉降规律,预测误差指标均小于其他模型,预测准确性和可靠性最高。

加权总体最小二乘在三维激光标靶拟合中的应用

针对平面标靶和球标靶拟合中最小二乘方法和总体最小二乘方法存在的问题,提出了一种加权总体最小二乘的拟合方法,根据点云激光反射强度以及对系数阵A列向量部分修正引入权阵P、PX、P0,建立较LS方法和TLS方法更加合理的模型,并给出相应的迭代算法。经实例计算证明加权总体最小二乘方法更加合理,可以获得更高精度的参数解。

加权总体最小二乘在三维基准转换中的应用

对比研究加权总体最小二乘(weighted total least-squares,WTLS)方法和混合最小二乘(LS-TLS)方法、最小二乘(least-squares,LS)方法在三维空间小角度直角坐标转换中的适用性。在两套坐标系下坐标测量值均存在误差时,用WTLS方法不但可以对观测向量y和系数矩阵A同时修改、将坐标先验精度引入平差计算,而且引入的权阵PA对系数阵A起到固定常数元素而只修改必要数据元素的作用,以得到更合适的参数解。

整体最小二乘和最小二乘拟合空间直线的比较

给出整体最小二乘法拟合空间直线的一种迭代算法。将空间直线垂直投影到坐标平面,分别采用整体最小二乘法和最小二乘法拟合直线。对坐标点等精度观测、非等精度观测和坐标分量非等精度观测3种场景进行模拟计算,比较两种方法估计的参数和验后单位权方差,并对三维激光扫描仪实测数据拟合结果进行分析。

基于加权总体最小二乘法的GPS高程拟合

在GPS高程拟合中,针对传统最小二乘方法不能解决系数矩阵存在误差的问题,提出了一种基于加权总体最小二乘的拟合方法。对平面和二次曲面多项式建立更加合理的拟合模型,并给出了相应的迭代算法。实例计算表明,加权最小二乘方法能够得到更好的估计参数,高程异常值拟合精度也相应提高。

通用EIV平差模型及其加权整体最小二乘估计

以平差基本理论为基础,提出了EIV(errors-in-variables)平差模型的通用形式,涵盖了间接平差、条件平差、附有参数的条件平差及附有限制条件的间接平差等基本EIV模型形式。基于整体最小二乘估计准则,研究了通用EIV模型的加权整体最小二乘算法,并推导了估计结果的近似精度公式。通用EIV模型及其整体最小二乘算法是对EIV模型估计理论的进一步完善,统一的整体最小二乘算法有利于软件的编程实现,有助于推动EIV模型估计理论的应用。

加权总体最小二乘点云平面拟合定权方法探讨

针对加权总体最小二乘点云数据平面拟合方法中缺少统一定权准则的问题,提出以先验入射角及距离定权两种方法。利用稳健估计构造了基于强度值定权的稳健加权最小二乘、基于先验入射角定权的加权最小二乘与基于距离定权的加权最小二乘3种新的平面拟合算法,并应用于拟合扫描不同反射材质获取的平面点云数据。算例表明,以距离定权构造的距离加权总体最小二乘法的各项精度指标均优于其他算法,拟合效果最好。

基于WTLS的井筒变形监测算法及方案设计

针对基于最小二乘的常规井筒变形监测算法中,系数矩阵往往包含观测值误差影响,难以解算出井筒变形最优解,提出一种抗差加权整体最小二乘算法。该算法通过同时对待求参数和系数矩阵进行估计,克服常规最小二乘系数矩阵受观测值随机误差影响;同时结合IGGⅢ三段权函数抗差因子,调整各观测值权函数,消除观测值粗差对最终变形估计值的影响,得到最优的井筒变形值。最后分别采用模拟和实测井筒数据对算法进行了验证,结果表明:当观测值含有随机误差时,整体最小二乘算法优于最小二乘,但都无法抵御观测粗差影响;抗差加权整体最小二乘算法可以有效抵御观测粗差,修正观测随机误差影响,且精度优于抗差最小二乘;采用抗差加权整体最小二乘算法对实测井筒变形数据进行解算结果与实际相符,可以增强变形监测结果的可靠性,提高井筒变形量精度。

加权整体最小二乘在激光跟踪仪转站中的应用

由于利用经典最小二乘原则对激光跟踪仪进行坐标转换时,系数矩阵中携带的随机测量误差会影响转站精度,故对激光跟踪仪的转站算法进行了研究。提出了基于线性EIV模型(Errors-in-Variables)和加权整体最小二乘法(WTLS)并利用间接平差形式迭代求解转站参数的方法;利用Matlab进行仿真分析并用API公司生产的激光跟踪仪进行实验。仿真结果显示WTLS法的单位权中误差的平均值和标准差分别为经典加权最小二乘法(WLS)的4/5和1/5;实验结果显示WTLS和WLS两种方法的单位权中误差分别为2.003 5mm和2.225 3mm;这些数据证明采用WTLS法的转站结果比WLS的精度更高且更稳定。该方法可为组建激光跟踪仪测量网络,优化网络布局奠定基础。

加权整体最小二乘法在光学自准直法测量挠曲角中的应用

建立了基于光学自准直法的测量系统,利用该系统提取转台的十字丝坐标,完成了对三维物体的挠曲角测量。首先,采用Sobel提取算子对CCD拍摄到的十字丝图像进行边缘检测。然后,采用了自适应的阈值分割进行直线提取;由于转台上有其它划痕存在,采用局部最小二乘进行十字丝的提取。最后,采用加权整体最小二乘法进行十字丝两条线的直线拟合,联立两方程,得出交点坐标值。结果表明,此方法获取的两直线斜率之积的精度在±1%以内,非常接近十字丝斜率之积的真值(理论真值为-1)。使用徕卡经纬仪(精度为0.5″)的角度值作为真值进行精度标定,测得α的精度为3.59″,β的为3.76″,完全满足系统挠曲角测量的精度要求。

基于加权总体最小二乘法的点云平面拟合

为了提高点云数据平面拟合精度,提出强度加权总体最小二乘法。该方法是在传统加权总体最小二乘法基础上,利用点云数据中各点强度信息确定其平面拟合权重,通过均质性不同的3种平面样本数据对该方法的适用性进行了研究。结果表明,与最小二乘法和总体最小二乘方法相比,该方法对于均质性较好的点云数据有效,可以得到更高精度的参量解;而对于均质性较差的点云数据,该方法效果不理想,总体最小二乘法是最有效方法。