The objective of the project is to design a system which can control the temperature of a va-porizing liquid microthruster (VLM). The liquid in a VLM is heated using a heater resistor.This resistor will be used to both heat the liquid and measure the temperature.In this thesis the subsystem responsible for the measurements and the conversion of the mea-sured signals to the digital domain will be discussed. We propose a method where short measure-ment current pulses of a fixed amplitude are applied to the heater resistor. As an optimization,these pulses are omitted when a certain current threshold has been met.Results show that the system can measure temperature with±1◦C accuracy, however more fullsystem measurements are required to ensure functionality as a whole.

A system has been developed that utilises the techniques of Ultra Wideband and Synthetic Aperture Radar to produce top view images of a scene using measurements from the side. The system consists of the PulsON P410 radar module, a set of antennas, a moving platform and an imaging algorithm. This thesis will cover all the aspects of the data acquisition part of the system with additionally an analysis on antennas.

Nowadays, localization of cars is mostly done using GPS. Some major disadvantages of this method are that it is not very accurate and that it cannot be used indoors. These disadvantages do not form a major problem when an active human driver is controlling the car, but will become more critical when the autonomy of vehicles increases. The goal of this project is to use Synthetic Aperture Radar techniques to overcome the problems of GPS. To achieve this, different imaging algorithms have been implemented, and a single method has been extended to directly accommodate the road-mapping application. Different measurements were done to test the algorithms and the results were promising. It is hoped that the use of this technology will become wide-spread and that it can help with creating a safer and more efficient world of traffic, where increased self-driving capabilities are facilitated.

For both security and analytics, much research has gone into person tracking already. As a result, many
different state of the art technologies exist. However, in darkness or without a direct line of sight, much
less technologies are capable of this. The choices become especially limited when the setup needs to
be portable.
A method for person localisation and tracking is implemented. This method consists of a localisation
part, which works with any range-based detection method. Least square estimation is used to determine
the location from the radar detections. With two or more people, it is mathematically impossible to
distinguish which locations are correct, if only the current measurement is taken into account.
Thus, the first problem to be solved is connecting ranges to targets. This is done using target association.
After this is done, one-dimensional tracking can track people at lower computational cost.
The tracking is both in one dimension (per-radar) and in two dimensions. The Hungarian algorithm
is used for keeping track of people using a Kalman filter. The Kalman filter considers the predicted
next location and the measured next location, and makes a best guess. A neural network was used for
the optimisation of location-specific noise parameters, something that has not been done before in this
context. Single person tracking and two person tracking works as expected. The tracking is relatively
cheap in terms of computational complexity. While the tracking has no limits on the maximum number
of people present, the localisation gets increasingly difficult with a complexity of O (n^n). Detecting
the correct peaks is a non-trivial problem because of multi-path reflections. In combination with UWB
radar detections, single and dual person tracking in a room is achieved. More people can be handled by
the tracking algorithm, which is detection-method-agnostic, but not by the localisation. There is some
room for improvement in the dual and triple-person case. However, going further than this is currently
unfeasible, because of the many reflections that occur. Furthermore, the large amount of possible person
locations also has an effect. This is a problem that scales with O (n^n) where n is the amount of
targets.

In this report, an indoor target detection system is developed for detecting one or multiple targets in a cluttered area based on a network of distributed ultra-wideband radars. The system is capable of acquisition of data in the form of distances between radar and targets, which can be used to localise and track one or multiple targets in real-time. The system can be used in a wide range of applications, ranging from security, such as anti-intruder systems, to commercial applications, such as tracking animals in a zoo.\newline

A network of four Time Domain PulsON 410 ultra-wideband radars are used in the sensing system, due to their availability, high spatial accuracy and performance in non-line-of-sight conditions. By using different transmission codes, interference between the sensors is kept to a minimum. Multiple antennas are considered, and the Vivaldi antenna is shown to be preferred over the dipole antenna for this application due to its slightly higher directivity, reducing the impact of multi-path effects. The data acquisition for the four radars is initiated almost simultaneously, up to the point that the radars can be considered synchronised. The received signal is filtered using background rejection and an FIR motion filter, to reveal motion of the target. Four detection algorithms are considered, of which the least-of constant false alarm rate (LO-CFAR) has a better performance for indoor multiple person tracking applications over conventional CFAR detection. The LO-CFAR algorithm can be applied to the filtered signal to detect targets in range of the sensors, and determine their distance from the sensor. Range-Doppler processing is proposed as a method to acquire a velocity estimate of the target, which can be used in a tracking system to estimate the position of the target more accurately. However, due to the relatively low slow-time sampling frequency, it is not feasible to utilise Range-Doppler processing in the current system. Only simulations of Range-Doppler processing are shown in the thesis. \newline

The system is tested in different environments; single person and multiple person situations are considered in an open and a cluttered area. It is shown that the developed system is capable of detecting a single target in both open and cluttered areas. With two targets in cluttered areas, it becomes difficult to distinguish a target from multi-path reflections, reducing the reliability of the system in these situations. In an open area, multiple targets can successfully be distinguished and detected. The sensing system can successfully detect and determine the distance to a person in non-line-of-sight conditions. Additionally, the detection system is tested on a smaller target to determine performance and accuracy of the system in situations with smaller targets. While the small target is possible to detect, the range at which reliable results are obtained is significantly reduced by the size of the target.\newline

It is concluded that there is room for improvement of the target detection system, especially in situations involving multiple targets in cluttered areas. Through-wall detection is shown to be feasible with the current sensing system. For small targets, a higher pulse integration index is required to achieve a reliable range similar to the results of person detection. Range-Doppler processing requires a higher slow-time sampling frequency in order to be feasible in a real-time tracking system. Further recommendations include testing alternative antennas to improve through-wall detection, designing a multi-static radar system and implementing more complex detection algorithms that have a better multi-person performance.