Indirect and Remote measurements

Analysis of accuracy and resolution

Nikolai Shokhirev

- Basics of Indirect Measurements
- Singular Value Decomposition
- Analysis of accuracy and resolution
- Implementation


This section is a continuation of the previous tutorial (see Singular Value Decomposition). Here we do not discuss any implementation issues (see Implementation).  

The basic equation is the integral equation of the first kind:


The integral operator 


connects two in general different spaces: the space of reconstructing functions f(x) and the space of measuring functions g(x)



Let  us define:


3D-space Example

 The three-dimensional space has only three basis vectors: U = {u1 , u2 , u3}. Let U1 = {u1 , u2} then u2 = { u3}. The space F is the whole 3D-space. The subspace F1 is the (u1 , u2)-plane (blue). The subspace F2 is the u3 -axis and not the rest of the space. 

In this example the space G is also formed by the set of three basis vectors V = {v1 , v2 , v3}. The subspace G1 is the (v1 , v2)-plane (green). The subspace F2 consists of the u3 -axis.

F-space G-space

Mapping of Spaces

The operator (2) transforms the F-space in the following way

It maps the whole space F and the subspace F1 to G1. It maps the subspace F2 to 0.


Interpretation of Mapping

The last of the above equations means that the F2 components of a reconstructing function f do not produce the measuring signal in this experiment (instrument). This just reflects an incompleteness of this experiment. From the reconstruction viewpoint this is the source of instability of the inverse problem. 

The F1 components produce a signal which is a function from the subspace G1. This signal can be used for reconstruction of the unknown function. It can be done by means of pseudo-inverse operator. Although it is not necessarily the best practical method of reconstruction.

There are no components in the F-space that produce a signal in the G2 subspace. If the right-hand side of the equation (1) contains components from the G2 subspace, then the equation does not have a solution.

The following picture summarize the interpretation



Use of singular functions

The singular functions (vectors) are natural bases for the integral operator (1) or for a given method of measurement.


Experimental noise

In practice some random noise makes contribution to a signal:

It causes errors in the coefficients of reconstructed function:

The noise amplification factor can be defined as 


Reconstruction criterion 

Any f(x) that reproduces g(y) within an experimental errors is a solution of the equation (1). 


Units of Information

The above criterion further restricts the set of functions within G1: only the components with the gn above the noise level should be taken into account. It also limits the set of functions un which can be used for reconstruction. The number of singular functions involved in reconstruction is the actual number of the units of information available for this instrument and noise level.


Accuracy and Resolution

The number of functions and their shape gives the answer for the following important questions

The accuracy is the largest amplitude of the rejected functions. The resolution is the shortest half-wavelength of the functions that can be used for reconstruction.  

Typical singular function behavior 

Reliable Reconstruction Region

Extremely useful characteristics which is usually ignored, is Reliable Reconstruction Region, RRR. It can be defined as a region where are situated the nodes (zeros) of the functions un used for reconstruction. In the above picture the RRR is approximately [0.5, 10]. 


Accuracy and Interval of Measurements

The accuracy of measurements and the interval of measurements affect the overall accuracy, resolution and Reliable Reconstruction Region. Analytical calculations and numerical experiments (see Implementation) show the following:


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- Basics of Indirect Measurements
- Singular Value Decomposition
- Analysis of accuracy and resolution
- Implementation


© Nikolai Shokhirev, 2001 - 2017