多类观测量联合平差中方差分量估计的序贯算法

SEQUENTIAL ALGORITHM ON VARIANCE COMPONENT ESTIMATION IN JOINT ADJUSTMENT OF VARIOUS OBSERVATIONS

在赫尔默特公式的基础上,推导了多类观测数据联合平差中方差分量估计的序贯算法,它区别于以前文献中使各类观测量所对应的验后单位权方差整体趋于一致的做法,而采用逐类(次)平差法,在每次平差后进行方差分量估计,依次使各类观测量的验后单位权方差趋于相等。该算法未作任何近似,依然保留原赫尔默特方差分量估计公式所具有的优良统计特性;它克服了原来严密公式计算中占用内存大、计算繁琐的缺点,不需要额外保留逐类(次)平差的历史数据,非常适合计算机程序实现。

On the basis of Helmert＇s formula for variance component estimation, a sequential algorithm for joint adjustment of various observations is put forward in this paper. This method is different from the available methods which let the post-adjustment weight variances corresponding various observations be identical wholely. However,it is sequential adjustment, doing the variance component estimation after the adjustment of each class of observations, and then in proper order make the post-adjustment unit weight variances equal to each other. It conquers the disadvantages of using a lots of memory and calculating complicated matrices. There is not any approximate processing of the original Helmert＇s formula without saving extra historic data beyond sequential adjustment. This algorithm is very suitable for programming on computers.