Feature Level Sensor Fusion
Abstract
This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate it can be selected as the primary sensor. However if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial false alarm reduction. The approaches are easily extendable to more than two sensors.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1999
- Accession Number
- ADA391976
Entities
People
- Fredrick Bennett
- Ken Ellis
- Mon Young
- Robert Knox
- Tamar Peli