The main objective of this paper is to investigate the impact of the quality of attribute data source on the performance of a target tracking algorithm. An array of dense scenarios arranged according to the distance between closely spaced targets is studied by different confusion matrices. The used algorithm is Generalized Data Association (GDA-MTT) algorithm for multiple target tracking processing kinematic as well as attribute data. The fusion rule for attribute data is based on Dezert-Smarandache Theory (DSmT). Besides the main goal a comparison is made between the cited above algorithm and an algorithm with Kinematic based only Data Association (KDAMTT). The measures of performance are evaluated using intensive Monte Carlo simulation.
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[v1] 2017-06-13 00:53:04
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