# Principles of Least Squares by duRmY02X

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```									Principles of Least Squares
Introduction
• In surveying, we often have geometric
constraints for our measurements
– Differential leveling loop closure = 0
– Sum of interior angles of a polygon = (n-2)180°
– Closed traverse: Σlats = Σdeps = 0
• Because of measurement errors, these
constraints are generally not met exactly, so an
adjustment should be performed
Random Error Adjustment
• We assume (hope?) that all systematic errors
have been removed so only random error
remains
• Random error conforms to the laws of
probability
• Should adjust the measurements accordingly
• Why?
Definition of a Residual
If M represents the most probable value of a measured
quantity, and zi represents the ith measurement, then the ith
residual, vi is:

vi = M – zi
Fundamental Principle of Least
Squares
In order to obtain most probable values (MPVs), the sum
of squares of the residuals must be minimized. (See book
for derivation.) In the weighted case, the weighted squares
of the residuals must be minimized.

 v 2  v12  v2  v3    vn  minimum
2    2        2

 wv 2  w1v12  w2 v2  w3v3    wn vn  minimum
2      2           2

Technically the weighted form shown assumes that the
measurements are independent, but we can handle the
general case involving covariance.
Stochastic Model
• The covariances (including variances) and hence
the weights as well, form the stochastic model
• Even an “unweighted” adjustment assumes that
all observations have equal weight which is also a
stochastic model
• The stochastic model is different from the
mathematical model
• Stochastic models may be determined through
sample statistics and error propagation, but are
often a priori estimates.
Mathematical Model
• The mathematical model is a set of one or more
equations that define an adjustment condition
• Examples are the constraints mentioned earlier
• Models also include collinearity equations in
photogrammetry and the equation of a line in linear
regression
• It is important that the model properly represents
reality – for example the angles of a plane triangle
should total 180°, but if the triangle is large, spherical
excess cause a systematic error so a more elaborate
model is needed.
Types of Models
Conditional and Parametric
• A conditional model enforces geometric conditions on
the measurements and their residuals
• A parametric model expresses equations in terms of
unknowns that were not directly measured, but relate to
the measurements (e.g. a distance expressed by
coordinate inverse)
• Parametric models are more commonly used because it
can be difficult to express all of the conditions in a
complicated measurement network
Observation Equations
• Observation equations are written for the
parametric model
• One equation is written for each observation
• The equation is generally expressed as a
function of unknown variables (such as
coordinates) equals a measurement plus a
residual
• We want more measurements than unknowns
which gives a redundant adjustment
Elementary Example
Consider the following three equations involving two unknowns.
If Equations (1) and (2) are solved, x = 1.5 and y = 1.5. However,
if Equations (2) and (3) are solved, x = 1.3 and y = 1.1 and if
Equations (1) and (3) are solved, x = 1.6 and y = 1.4.

(1) x + y = 3.0
(2) 2x – y = 1.5
(3) x – y = 0.2

If we consider the right side terms to be measurements, they
have errors and residual terms must be included for consistency.
Example - Continued
x + y – 3.0 = v1
2x – y – 1.5 = v2
x – y – 0.2 = v3

To find the MPVs for x and y we use a least squares solution by
minimizing the sum of squares of residuals.

f ( x, y )   v 2  ( x  y  3.0) 2  (2 x  y  1.5) 2  ( x  y  0.2) 2
Example - Continued
To minimize, we take partial derivatives with respect to each of the
variables and set them equal to zero. Then solve the two equations.

f
 2( x  y  3.0)  2(2 x  y  1.5)(2)  2( x  y  0.2)  0
x
f
 2( x  y  3.0)  2(2 x  y  1.5)(1)  2( x  y  0.2)(1)  0
y

These equations simplify to the following normal equations.
6x – 2y = 6.2
-2x + 3y = 1.3
Example - Continued
Solve by matrix methods.

 6  2  x  6.2
 2 3   y   1.3 
              
 x  1 3 2 6.2 1.514
 y   14 2 6 1.3   1.443
                         

We should also compute residuals:
v1 = 1.514 + 1.443 – 3.0 = -0.044
v2 = 2(1.514) – 1.443 – 1.5 = 0.086
v3 = 1.514 – 1.443 – 0.2 = -0.128
Systematic Formation of Normal
Equations
Resultant Equations
Following derivation in the book results in:
Example – Systematic Approach
Now let’s try the systematic approach to the example.
(1) x + y = 3.0 + v1
(2) 2x – y = 1.5 + v2
(3) x – y = 0.2 + v3
Create a table:
a       b         l     a2       ab         b2        al         bl
1       1        3.0    1         1         1         3.0        3.0
2      -1        1.5    4        -2         1         3.0       -1.5
1      -1        0.2    1        -1         1         0.2       -0.2
Σ=6      Σ=-2       Σ=3       Σ=6.2     Σ=1.3
Note that this yields the same normal equations.
Matrix Method
Matrix form for linear observation equations:
AX = L + V
Where:

 a11   a12     a1n          x1       l1          v1 
a      a22     a2 n        x        l           v 
A   21                     X   2     L 2       V  2
                                           
a              amn         x        l           v 
 m1    am 2                  n        m           m

Note: m is the number of observations and n is the number of
unknowns. For a redundant solution, m > n .
Least Squares Solution
Applying the condition of minimizing the sum of squared residuals:

ATAX = ATL
or
NX = ATL
Solution is:
X = (ATA)-1ATL = N -1ATL
and residuals are computed from:
V = AX – L
Example – Matrix Approach
1 1            3.0  v1 
AX  2  1  x   1.5   v   L  V
       y    2 
 
1  1
               0.2 v3 
   
1 1 
A AX  
1 2 1                  x    6  2  x 
 2  1  y   2 3   y 
T

1  1  1                            
1  1
       
3.0
1 2 1    6.2
A L
T
 1.5   1.3 
1  1  1              
0.2
 
Matrix Form With Weights

Weighted linear observation equations:
WAX = WL + WV
Normal equations:
ATWAX = NX = ATWL
Matrix Form – Nonlinear System
We use a Taylor series approximation. We will need the Jacobian
matrix and a set of initial approximations.
The observation equations are:
JX = K + V
Where:        J is the Jacobian matrix (partial derivatives)
X contains corrections for the approximations
K has observed minus computed values
V has the residuals
The least squares solution is: X = (JTJ)-1JTK = N-1JTK
Weighted Form – Nonlinear System
The observation equations are:
WJX = WK + WV

The least squares solution is: X = (JTWJ)-1JTWK = N-1JTWK
Example 10.2
Determine the least squares solution for the following:
F(x,y) = x + y – 2y2 = -4
G(x,y) = x2 + y2      = 8
H(x,y) = 3x2 – y2     = 7.7
Use x0 = 2, and y0 = 2 for initial approximations.
Example - Continued
Take partial derivatives and form the Jacobian matrix.

F                 G                 H
1                  2x                6x
x                 x                 x
F                 G                 H
 1 4 y            2y                2 y
y                 y                 y

 1 1  4 y0   1  7 
J  2 x0  2 y0    4 4 
                    
6 x0  2 y0  12  4
                    
Example - Continued
Form K matrix and set up least squares solution.
  4  F ( x0 , y0 )   4  (4)  0 
K   8  G ( x0 , y0 )    8  8    0 
                                    
7.7  H ( x0 , y0 )  7.7  8   0.3
                                    

 1  7
T  1 4
J J 
12              161  39
  4 4   39 81 
 7 4       4                        
12  4
         
 0 
T  1 4
J K 
12           3.6
  0   1.2 
 7 4       4                  
 0.3
     
Example - Continued
1
 161  39  3.6  0.2125
X            1.2    0.00458 
 39 81                    

Add the corrections to get new approximations and repeat.
x0 = 2.00 – 0.02125 = 1.97875             y0 = 2.00 + 0.00458 = 2.00458
1
 157.61806  38.75082  0.12393 0.00168
X                        0.75219   0.01004
 38.75082 81.40354                      

Add the new corrections to get better approximations.
x0 = 1.97875 + 0.00168 = 1.98043           y0 = 2.00458 + 0.01004 = 2.01462
Further iterations give negligible corrections so the final solution is:
x = 1.98            y = 2.01
Linear Regression
Fitting x,y data points to a straight line: y = mx + b
Observation Equations
y A  v y A  mxA  b
y B  v y B  mxB  b
yC  v yC  mxC  b
y D  v y D  mxD  b

In matrix form: AX = L + V

 xA     1        y A  v y A 
x       1 m   y B   v y B 
 B               
 xC     1  b   yC  v yC 
 
x       1        y  v 
 D               D   yD 
Example 10.3
Fit a straight line to the        point     x       y
points in the table.
Compute m and b by
A      3.00    4.50
least squares.                     B      4.25    4.25
C      5.50    5.50
D      8.00    5.50
In matrix form:
3.00     1       4.50  v A 
4.25     1 m  4.25  vB 
                     
5.50     1  b  5.50 vC 
 
8.00     1       5.50 v 
                       D
Example - Continued
1
m         1        121.3125 20.7500 105.8125 0.246
X     ( A A) ( A L)  
T      T
  19.7500   3.663
b                    20.7500 4.0000                   

3.00   1           4.50  0.10
4.25   1 0.246 4.25  0.46 
V  AX  L                                  
3.663  5.50   0.48
5.50   1      
8.00   1           5.50  0.13 
                                
Standard Deviation of Unit Weight

S0 
  0.47  0.48
v2
mn 42

Where: m is the number of observations and
n is the number of unknowns

Question: What about x-values? Are they
observations?
Fitting a Parabola to a Set of Points

Equation:      Ax2 + Bx + C = y

This is still a linear problem in terms of the unknowns
A, B, and C.

Need more than 3 points for a redundant solution.
Example - Parabola
Parabola Fit Solution - 1
Set up matrices for observation equations

0 2    0 1               103.84
 2                       105.43
1      1 1                      
2 2    2 1               104.77
L
A 2                        102.21
3      3 1                      
4 2    4 1                98.43 
 2                              
5      5 1                93.41 
Parabola Fit Solution - 2
Solve by unweighted least squares solution
1
979 225 55 5354.53   0.813 
x  ( AT A) 1 AT L  225 55 15 1482.37   1.902 
                            
 55 15 6   608.09  104.046
                            

 0.206 
  0.295
Compute                                   
 0.172
residuals            V  AX  L  
0.225 
        
 0.216 
        
  0.180
Condition Equations
• Establish all independent, redundant
conditions
• Residual terms are treated as unknowns in the
problem
• Method is suitable for “simple” problems
where there is only one condition (e.g. interior
angles of a polygon, horizon closure)
Condition Equation Example
Condition Example - Continued
Condition Example - Continued
Condition Example - Continued

Note that the angle with the smallest standard deviation has
the smallest residual and the largest SD has the largest residual
Example Using Observation Equations
Observation Example - Continued
 1                   0 
1 0             6 .7 2     0                     13438'56" 
A 0 1                      1                     
L   8317'35" 

            W  0                   0 
 1  1
                         9. 9 2                 142 03'14"360 
1                            
 0          0

                   4 .3 2 


0.07636 0.05408                     14.7867721
A WA  
T
A WL  
T

 0.05408 0.06429
                    12.6370848

Observation Example - Continued

13439'00.2"
X  ( AT WA ) 1 ( AT WL )              
 83 17'44.1" 

a3  360  13439'00.2"8317'44.1"  142 03'15.7"

Note that the answer is the same as that obtained with
condition equations.
Simple Method for Angular Closure
Given a set of angles and associated variances and a
misclosure, C, residuals can be computed by the following:

C i2
vi     n

  i2
i 1
Angular Closure – Simple Method
3

  i2  6.7 2  9.9 2  4.32  161.39
i 1

15" (6.7) 2
v1               4.2"
161.39
15" (9.9) 2
v2               9.1"
161.39
15" (4.3) 2
v3               1.7"
161.39

```
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