Fusion of imaging and nonimaging sensor information for airborne surveillance

摘要

Abstract: This paper presents results from an adaptable data fusion testbed (ADFT) which has been constructed to analyze simulated or real data with the help of modular algorithms for each of the main fusion functions and image interpretation algorithms. The result obtained from data fusion of information coming from an imaging SAR and non- imaging sensors on-board an airborne maritime surveillance platform are presented for two typical scenarios of Maritime Air Area Operations and Direct Fleet Support. An extensive set of realistic databases has been created that contains over 140 platforms, carrying over 170 emitters and representing targets from 24 countries. A truncated Dempster-Shafer evidential reasoning scheme is used that proves robust under countermeasures and deals efficiently with uncertain, incomplete or poor quality information. The evidential reasoning scheme can yield both single ID with an associated confidence level and more generic propositions of interest to the Commanding Officer. For nearly electromagnetically silent platforms, the Spot Adaptive mode of the SAR, which is appropriate for naval targets, it is shown to be invaluable in providing long range features that are treated by a 4-step classifier to yield ship category, type and class. Our approach of reasoning over attributes provided by the imagery will alloy the ADFT to process in the next phase both FLIR imagery and SAR imagery in different modes. !7