In this paper we present the multi-sensor registration and fusion algorithms that were developed for a force
protection research project in order to detect threats against military patrol vehicles.
The fusion is performed at object level, using a hierarchical evidence aggregation approach. It first uses
expert domain knowledge about the features used to characterize the detected threats, that is implemented in
the form of a fuzzy expert system. The next level consists in fusing intra-sensor and inter-sensor information.
Here an ordered weighted averaging operator is used.
The object level fusion between candidate threats that are detected asynchronously on a moving vehicle by
sensors with different imaging geometries, requires an accurate sensor to world coordinate transformation. This
image registration will also be discussed in this paper.
High resolution sonars are required to detect and classify mines on the sea-bed. Synthetic aperture sonar increases the sonar cross range resolution by several orders of magnitudes while maintaining or increasing the area search rate. The resolution is however strongly dependent on the precision with which the motion errors of the platform can be estimated. The term micro-navigation is used to describe this very special requirement for sub-wavelength relative positioning of the platform. Therefore algorithms were designed to estimate those motion errors and to correct for them during the (ω, k)-reconstruction phase. To validate the quality of the motion estimation algorithms a single transmitter/multiple receiver simulator was build, allowing to generate multiple point targets with or without surge and/or sway and/or yaw motion errors. The surge motion estimation is shown on real data, which were taken during a sea trial in November of 2003 with the low frequency (12 kHz) side scan sonar (LFSS) moving on a rail positioned on the sea-bed near Marciana Marina on the Elba Island, Italy.