2.3 The Example about Gross Errors Location by Two Step 
Iterations Method 
  
We give a brief note in Table 4 about gross errors 
localization. 
TABLE 4 GROSS ERRORS LOCATION BY TWO STEP ITERATIONS METHOD 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Point [ 1 2 3 4 5 6 7 8 9 10 | Noted in comprehehsive desions ‘ 
| | 77] 8.1 2.4 . | observational error vector = E 
First | | 4 12.5 2.8 . | gross error V,= 6, V.= -6 
Step | it=6! * | : . : : . . | point 5 as Type A observation 
| Pel 0.8: .- 3.0 . | point 8 as Type B observation 
| aede . | point 5, 8 strong correlation 
| | point 9 with good observation 
Second| tv "| 24 >. «" 1,6 ”. ’, | point 5,8 weighted zero 
Step | it:11 9 ] . : . = gui -. . -4.4 . . | point 5, 8 contained gross error 
| |MCCV| 30 «37 | 
| | observational error vector = E 
| [+ 43.9 2.9 3.6 2:1 533.4 2.7 . . «| gross error V,= -12, V:s-12 
litsl[s 1-48 3.7 8.4 4.1 8.9 -3.4 | point 1, 2, 3, 5, 6 and 7 are 
First | | | Type A observations 
Step ------------------------ T------------------- 
| | | after five iterations only 
| Iv 13.7 2.2 2.3 | point 1 as Type A observation 
| it=6 | ¢ |-4.6 | 33 . 3.6 . . + | point 5, 7 near Type A obs. 
| Pau Î d. d. : .  . | residual of point 4 is strong 
| Foon Fb 7-3. | correlation with point 1, 7 
| | point 4 as Type B observation 
| Iv | 1.9 1.1 1.3 1.7 | pointi,4, 5, 7 weighted zero 
l'it:11*$? 1331 -5.4 2.4 -7.0 | point 4, 7 need further 
Second | IMCCV| 0.6 0.2 .55 0.24 | detection 
Step ------------------------------------------------- —— 
| [v 4 3.9 4.7 | point 4, 7 weighted zero 
t'it=i te | -13.0 -13.1 | point 4, 7 contained gross error 
| |MCCV| 0.30 0.35 | 
From Table 3 and Table 4, we find that when weighted 
zero is assigned a pair observation in which residuals 
  
Comparasion of the observations in which residuals are 
are of strong correlation after iteration, the not correlated shows that the capability of localizing 
correlation of residuals have been disspated and have grvss : errors would decrease even if the critical value 
made convenient condition for decision of gross errors is 1.5 instead of 2.5. 
localization, because the magnitude of main component 
coefficient of standardized residual is decreased. 
conclusions 
Gross error location, especially for more one gross 
error, is a problem that has not been completely solved 
in adjustment. From the strategical point review, to 
develop the iterated weighted least squares method to 
two step iterations method is a powerful way to improve 
the capability and relibility for gross errors location. 
After the first step iterations, the searching gross 
error observations is in a comparatively limited area. 
The experiment proved that the second step iterations 
play an important part in correcting the mistakes of 
decision about gross error observation(s) in the first 
step iterations. In the second step, the decision about 
gross error observation(s) are concerned with the 
magnitude of of standardized residuals and weighted zero 
residuals, the correlation of residuals as well as the 
main component of standardized residual MCCV. When the 
value of MCCV is very small, the comprehensive decisions 
will be particulary difficult. One has to further 
investigate in gross errors locationin order to get more 
knowledges about comprehensive decisions.