Estimation with Multisensor/Multiscan Detection Fusion
Final rept. 1 Aug 1991-31 Jan 1992
CONNECTICUT UNIV STORRS DEPT OF ELECTRICAL AND SYSTEMS ENGINEERING
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In this paper we present an algorithm to solve the static problem of associating data from three spatially distributed heterogeneous sensors, each with a set of detections at the same time. The sensors could be active 3D or 2D radars or passive EO sensors measuring the azimuth and elevation angles of the source. The source of a detection can be either a real target, in which case the measurement is the true observation variable of the target plus measurement noise, or a spurious one, i.e., a false alarm. In addition, the sensors may have nonunity detection probabilities. The problem is to associate the measurements from the sensors to identify the real targets, and to obtain their position estimates. Mathematically, this static measurement-target association problem leads to a generalized three-dimensional 3-D matching problem, which is known to be NP-hard. In this paper, we present a fast, but near-optimal 3-D matching algorithm suited for estimating the positions of a large number of targets 50 in a dense cluster for real-time applications. Performance results on several representative test cases solved by the algorithm are presented.
- Optical Detection and Detectors
- Active and Passive Radar Detection and Equipment