14 September 1989 Neural Network Implementations Of Data Association Algorithms For Sensor Fusion
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The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Donald E. Brown, Clarence L. Pittard, Worthy N. Martin, "Neural Network Implementations Of Data Association Algorithms For Sensor Fusion", Proc. SPIE 1100, Sensor Fusion II, (14 September 1989); doi: 10.1117/12.960488; https://doi.org/10.1117/12.960488


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