粗差验后方差的无偏估计与最优稳健估计

在正态粗差假设下导出了粗差验后方差的无偏估计,对误差工膨胀模型和误差均值移动模型,两者的无偏估计公式是相同的。这证明了李德仁验后方差的朱建军方差不是无偏的。由于偏方定义的彭方法是正态粗差假设下的最优稳健估计。     

卫星信号发射时刻的无偏恢复

卫星信号发射时刻在导航定位中是一个重要的参数,但是在城市峡谷等弱信号条件下,接收机可能完成不了所有卫星信号发射时刻的组装。在伪距定位中卫星信号发射时刻的组装和粗时段导航中卫星信号发射时刻的恢复(有偏)技术基础上,提出一种无偏的卫星信号发射时刻恢复方法,前提是卫星至少能完成一颗星的伪距测量,并且具有150km误差范围内的先验位置和辅助星历。利用BDS B1和GPS L1中频数据在软件接收机平台进行了BDS,GPS信号发射时刻无偏恢复验证实验证明了方法的有效性。     

Entropy principles in the prediction of water quality values at discontinued monitoring stations

A new methodology for predicting water quality values at discontinued water quality monitoring stations is proposed. The method is based upon the Principle of Maximum Entropy (POME) and provides unbiased predictions of water quality levels at upstream tributaries and on the mainstem of a river given observed changes in the distribution of the same water quality parameter at a downstream location. Changes in the values of water quality parameters which are known a priori to have occurred upstream, but which are not sufficiently large to account for all the observed change in the same water quality parameter at the downstream location are able to be incorporated in the method through the introduction of a new term in the basic entropy expression. Application of the procedure to water quality monitoring on the Mackenzie River in Queensland, Australia indicates the method has considerable potential for prediction of water quality at discontinued stations. The method also has potential for identifying the location of causes of observed changes in water quality at a downstream station.     

Theory of integer equivariant estimation with application to GNSS

Carrier phase ambiguity resolution is the key to high-precision global navigation satellite system (GNSS) positioning and navigation. It applies to a great variety of current and future models of GPS, modernized GPS and Galileo. The so-called fixed baseline estimator is known to be superior to its float counterpart in the sense that its probability of being close to the unknown but true baseline is larger than that of the float baseline, provided that the ambiguity success rate is sufficiently close to its maximum value of one. Although this is a strong result, the necessary condition on the success rate does not make it hold for all measurement scenarios. It is discussed whether or not it is possible to take advantage of the integer nature of the ambiguities so as to come up with a baseline estimator that is always superior to both its float and its fixed counterparts. It is shown that this is indeed possible, be it that the result comes at the price of having to use a weaker performance criterion. The main result of this work is a Gauss–Markov-like theorem which introduces a new minimum variance unbiased estimator that is always superior to the well-known best linear unbiased (BLU) estimator of the Gauss–Markov theorem. This result is made possible by introducing a new class of estimators. This class of integer equivariant estimators obeys the integer remove–restore principle and is shown to be larger than the class of integer estimators as well as larger than the class of linear unbiased estimators. The minimum variance unbiased estimator within this larger class is referred to as the best integer equivariant (BIE) estimator. The theory presented applies to any model of observation equations having both integer and real-valued parameters, as well as for any probability density function the data might have. AcknowledgementsThis contribution was finalized during the authors stay, as a Tan Chin Tuan Professor, at the Nanyang Technological Universitys GPS Centre (GPSC) in Singapore. The hospitality of the GPSCs director Prof Law Choi Look and his colleagues is greatly appreciated.     

论不连序系列样本概率权重矩不偏计算公式

人们普遍认为要从理论上通过数学分析的方法来论证不连序系列样本概率权重矩的偏倚性是很困难的。本文根据次序统计量理论，分析推证了不连序系列样本概率权重矩的不偏计算公式，论证结果具有较重要的理论和实用意义。     

A bound for the Euclidean norm of the difference between the best linear unbiased estimator and a linear unbiased estimator

 A bound is established for the Euclidean norm of the difference between the best linear unbiased estimator and any linear unbiased estimator in the general linear model. The bound involves the spectral norm of the difference between the dispersion matrices of the two estimators, and the residual sum of squares, all evaluated at the assumed model, but is independent of the provenance of the observation vector at hand. The bound, a straightforward consequence of first principles in Gauss–Markov theory, generalizes previous results on the difference between the best linear unbiased estimator and the ordinary least-squares estimator. In a numerical example from repeated precise levelling, the bound is used to analyse the sensitivity of estimates of vertical motion to the choice of estimator. Received: 9 September 1999 / Accepted: 15 March 2002     

��ƫ��չ��ɫģ�ͼ����ڸ߱����α�Ԥ���е�Ӧ��

???????????к???????????е????GM(1,M)?????????????????????????????????????????????????????????????????????????????????????????,????С??????????1??????????GM(1,M)???????????????????GM(1,1)?????GM(1,M)??     

Unbiased estimation formula of unit weight standard deviation in regularization solution

Regularization method is an effective method for solving ill-posed equation. In this paper the unbiased estimation formula of unit weight standard deviation in the regularization solution is derived and the formula is verified with numerical case of 1 000 sample data by use of the typical ill-posed equation, i. e. the Fredholm integration equation of the first kind.     

Unbiased estimation formula of unit weight standard deviation in regularization solution

Regularization method is an effective method for solving ill-posed equation. In this paper the unbiased estimation formula of unit weight standard deviation in the regularization solution is derived and the formula is verified with numerical case of 1000 sample data by use of the typical ill-posed equation, i.e. the Fredholm integration equation of the first kind.     

Biases and accuracy of, and an alternative to, discrete nonlinear filters

The biases and accuracy of the extended Kalman filter (EKF) and a second-order nonlinear filter (SONF) are discussed from the point of view of a frequentist; these are often derived by applying the relevant conditional quantities to the linear Kalman algorithm under the Bayesian framework. The EKF and the SONF are biased, although the SONF has been derived in the hope of improving first-order filters. Unfortunately the biases of the SONF may be magnified further, because the second-order terms of the relevant Bayesian conditional quantities have never been properly used to derive the SONF from the frequentist point of view. The variance–covariance matrix of the SONF given in the literature is proven to be incorrect up to the second-order approximation, and the correct one is derived. Finally, also from the point of view of a frequentist, an alternative, almost unbiased SONF is proposed, if the randomness of partials is neglected. Received: 12 July 1997 / Accepted: 5 October 1998