In this project the aim is to investigate how the robustness and accuracy of a sensor system is influenced by the number of sensors, their frequency range of operation and their specific locations around the measurement region. The aim is also to analyse the sensors in combination with the measurement domain and the unknown objects under test.
We investigate their ability to perform robust and accurate measurements for different purposes such as measurements on different types of objects, e.g. the human body, food in the food processing industry, or medicine in
the pharmaceutical industry. The information is then converted into specifications on the actual sensors and we attempt to define figures of merit that can be used to select the optimal sensor locations.
The typical sensor systems found in industry can be categorized into the following three basic sensor arrangements: planar; cylindrical and spherical. These three basic configurations are the same for the various med tech applications that are under research. It is interesting to analyse measurability of the desired quantities as it is influenced by a number of factors specific to the shape, placement as well as their frequency range of operation.
We will attempt to approach this goal by means of one single model for the complete system that includes the modelling of microwave sensors, propagation modelling in the near-field region, the modelling of signal transmission
and reception, different signal processing algorithms, etc.
In general, it is very important to decrease manufacturing and operational costs for industrial sensor systems. For example, it is advantageous to reduce the number of sensors that are required to achieve a specified performance, since the manufacturing cost is proportional to the number of sensors. Power consumption may also be reduced. For example, a battery must be used to power some sensor systems and, in such cases, the design process involves strict requirements on the energy consumption. In contrast, other applications have very high requirements on the measurement accuracy such as high resolution microwave tomography, where higher manufacturing and operational costs can be accepted in order to achieve the desired accuracy.
Reduced number of sensors implies reduced manufacturing costs for the sensor system but this is accomplished at the expense of reduced measurement capabilities. Reduced measurement capabilities, for a system with fewer sensors, may be recovered by careful selection of sensor locations and the frequency band of operation. We intend to perform a careful and exhausting investigation of these aspects for the three main categories of sensor arrangements. This work provides specifications on the sensors required for the particular application and, to some extent, it should be feasible to define appropriate and relevant figures of merit.
The hardware components of sensor systems are becoming cheaper at the same time as their performance in increasing. This permits the exploitation of new measurement scenarios that previously were intractable due to insufficient performance or excessive costs. Optimization of sensor systems will push these limits even further. Also, development of design rules will greatly facilitate the design process of sensor systems as questions like “What is achievable, and what is not?”, “What performance can be expected?”, and “At what price?” can be answered for basic sensor arrangements and common measurement scenarios.
It should be emphasized that the measured response and its sensitivity depend substantially on the objects in the measurement region. Consequently, a wide range of possible measurement scenarios must be considered. A considerable amount of simulations must therefore be performed in order to account for these measurement scenarios. To overcome this challenge, clever approximations and methods must be developed to keep the computational time within feasible limits.
Previously, we have computed optimal sensor locations for a magnetic tracking system with a planar sensor array. The system is used for real-time organ-positioning during radiotherapy of cancer tumors.