<p>In angle-only target tracking the aim is to estimate the state of a target with use of measurement of elevation and azimuth. The state consists of relative position and velocity between the target and the platform. The platform is an Unmanned Aerial Vehicle (UAV) and the tracking system is meant to be a part of the platform’s anti-collision system. In the case where both the target and the platform travel with constant velocity the angle measurements do not provide any information of the range between the target and the platform. The platform has to maneuver to be able to estimate the range to the target.</p><p>Two filters are implemented and tested on simulated data. The first filter is based on a Extended Kalman Filter (EKF) and is designed for tracking nonmaneuvering targets. Different platform maneuvers are studied and the influence of initial errors and the geometry of the simulation scenario is investigated. The filter is able to estimate the position of the target if the platform maneuvers and the target travels with constant velocity. Maneuvering targets on the other hand can not be tracked by the filter.</p><p>The second filter is an interacting multiple model (IMM) filter, designed for tracking maneuvering targets. The filter performance is highly dependent of the geometry of the scenario. The filter has been tuned for a scenario where the target approaches the platform from the front. In this scenario the filter is able to track both maneuvering and non-maneuvering targets. If the target approaches the platform from the side on the other hand, the filter has problems with distinguish target maneuvers from measurement noise.</p>

In angle-only target tracking the aim is to estimate the state of a target with use of measurement of elevation and azimuth. The state consists of relative position and velocity between the target and the platform. The platform is an Unmanned Aerial Vehicle (UAV) and the tracking system is meant to be a part of the platform’s anti-collision system. In the case where both the target and the platform travel with constant velocity the angle measurements do not provide any information of the range between the target and the platform. The platform has to maneuver to be able to estimate the range to the target. Two filters are implemented and tested on simulated data. The first filter is based on a Extended Kalman Filter (EKF) and is designed for tracking nonmaneuvering targets. Different platform maneuvers are studied and the influence of initial errors and the geometry of the simulation scenario is investigated. The filter is able to estimate the position of the target if the platform maneuvers and the target travels with constant velocity. Maneuvering targets on the other hand can not be tracked by the filter. The second filter is an interacting multiple model (IMM) filter, designed for tracking maneuvering targets. The filter performance is highly dependent of the geometry of the scenario. The filter has been tuned for a scenario where the target approaches the platform from the front. In this scenario the filter is able to track both maneuvering and non-maneuvering targets. If the target approaches the platform from the side on the other hand, the filter has problems with distinguish target maneuvers from measurement noise.

Airborne angle-only geolocalization is the localization of objects on ground level from airborne vehicles (AV) using bearing measurements, namely azimuth and elevation. This thesis aims to introduce elevation data of the terrain to the airborne angle-only geolocalization problem and to demonstrate that it could be applicable for localization of jammers. Jammers are often used for deliberate interference with malicious intent which could interfere with the positioning system of a vehicle. It is important to locate the jammers to either avoid them or to remove them.    Three localization methods, i.e. the nonlinear least squares (NLS), the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are implemented and tested on simulated data. The methods are also compared to the theoretical lower bound, the Cramér-Rao Lower Bound (CRLB), to see if there is an efficient estimator. The simulated data are different scenarios where the number of AVs, the relative flight path of the AVs and the knowledge of the terrain can differ. Using the knowledge of the terrain elevation, the methods give more consistent localization than without it. Without elevation data, the localization relies on good geometry of the problem, i.e. the relative flight path of the AVs, while the geometry is not as critical when elevation data is available. However, the elevation data does not always improve the localization for certain geometries.    There is no method that is clearly better than the others when elevation data is used. The methods’ performances are very similar and they all converge to the CRLB but that could also be an advantage. This makes the usage of elevation data not restricted to a certain method and it leaves more up to the implementer which method they prefer.