<title>Multisource fusion for target detection</title>

Abstract

An approach to the fusion of information from airborne sensors for the purpose of target detection is described. This approach differs from alternate strategies in that the fusion occurs at the target hypothesis level, a symbolic level, rather than at the sensor level, i.e., candidate target coordinates are merged into correlated target hypotheses. Thus, a source in this approach consists of both a sensor which provides data about the target environment, and a list of candidate target coordinates generated as output from a target detection algorithm. The fusion algorithm is based on generating a statistical model for the detection and false alarm performance of each target coordinate source. Special emphasis is placed on modeling the positional misregistration which occurs when imagery is extracted from different platforms. An iterative clustering algorithm is derived from the source models based on a maximum likelihood target location estimation approach. Results of multisource fusion on several synthetic datasets are provided which indicate the encouraging performance of the system even under severe clutter and sensor misregistration conditions.