<p>The main goal of this thesis is to show the strength of combining datasets from two different types of sensors to find anomalies in their data. The sensors used in this thesis are a hyperspectral camera and a scanning 3-D laser.</p><p>The report can be divided into two main parts. The first part discusses the properties of one of the datasets and how these are used to isolate anomalies. An issue to deal with here is not only what properties to look at, but how to make the process automatic. The information retained from the first dataset is then used to make intelligent choices in the second dataset. Again, one of the challenges is to make this process automatic and accurate. The second part of the project consists of presenting the results in a way that gives the most information to the user. This is done with a graphical user interface that allows the user to manipulate the way the result is presented.</p><p>The conclusion of this project is that the information from the combined sensor datasets gives better results than the sum of the information from the individual datasets. The key of success is to play to the strengths of the datasets in question. An important block of the work in this thesis, the calibration of the two sensors, was completed by Kevin Chan as his thesis work in Electrical Engineering at the University of Lund. His contribution gave access to calibrated data that supported the results presented in this report.</p>

The main goal of this thesis is to show the strength of combining datasets from two different types of sensors to find anomalies in their data. The sensors used in this thesis are a hyperspectral camera and a scanning 3-D laser. The report can be divided into two main parts. The first part discusses the properties of one of the datasets and how these are used to isolate anomalies. An issue to deal with here is not only what properties to look at, but how to make the process automatic. The information retained from the first dataset is then used to make intelligent choices in the second dataset. Again, one of the challenges is to make this process automatic and accurate. The second part of the project consists of presenting the results in a way that gives the most information to the user. This is done with a graphical user interface that allows the user to manipulate the way the result is presented. The conclusion of this project is that the information from the combined sensor datasets gives better results than the sum of the information from the individual datasets. The key of success is to play to the strengths of the datasets in question. An important block of the work in this thesis, the calibration of the two sensors, was completed by Kevin Chan as his thesis work in Electrical Engineering at the University of Lund. His contribution gave access to calibrated data that supported the results presented in this report.

Unmanned aerial systems (UAS) are an influential technology which can enhance life in multiple ways. However, they must be able to sense and operate safely with manned aircraft. Radar is an attractive sensor for UAS because of its all-weather performance. It is challenging, though, to meet the size, weight, and power (SWaP) limitations of UAS and especially small-UAS while still maintaining the needed sensing capability. A working FMCW radar prototype has been created which meets the SWaP requirement of small-UAS. A simulation model for the radar was developed to test the processing algorithms of the radar and proved to be advantageous in that purpose. An automatic target detection algorithm was also successfully developed to allow the radar to identify targets of interest in a cluttered and dynamic environment. Fixed-wing airborne tests have been performed with the radar which show that the radar meets the SWaP requirements of small-UAS. They also show the prototype requires a higher sensitivity to detect other small-UAS. A successful redesign of the radar's receivers was done to make the radar more sensitive.

Automatic object detection is a challenging field which has been evolving over decades. The application areas span many domains such as robotics inspection, medical imaging, military targeting, and reconnaissance. Some of the most concentrated efforts in automatic object detection have been in the military domain, where most of the problems deal with automatic target detection and scene analysis in the outdoors using a variety of sensors. One of the critical problems in Automatic Target Detection (ATD) systems is multiscenario adaptation. Most of the ATD systems developed until today perform unpredictably i.e. perform well in certain scenarios, and poorly in others. Unless ATD systems can be made adaptable, their utility in battlefield missions remains questionable. This thesis describes a methodology that adapts parameterized ATD systems with image metrics as the scenario changes so that ATD system can maintain better performance. The methodology uses experimentally obtained performance models, which are functions of image metrics and system parameters, to optimize performance measures of the ATD system. Optimization is achieved by adapting system parameters with incoming image metrics based on performance models as the system works in field. A simple ATD system is also proposed in this work to describe and test the methodology.