• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.681-691
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• 2017

In the clutter environment, it is necessary to update the target tracking filter by detecting the target signal among many measured value data obtained via the radar system, the track does not diverge, and tracking performance is maintained. The method of associating the measurement most relevant to the target track among numerous measurement values is referred to as data association. PSNF and PSNF-m are data association methods of SN-series. In this paper, we provide an IPSNF-m(Integrated Probabilistic Strongest Neighbor Filter-m) algorithm with a track management method based on the track existence probability in PSNF-m algorithm. This algorithm considers not only the presence of the target but also the case where the target is present but not detected. Calculating the probability of each caseenables efficient management. In order to verify the performance of the proposed IPSNF-m, the track existence probability of the IPSNF algorithm applying the track management technique to PSNF, which is known to have similar performance to PSNF-m, is derived. Through simulation in the same environment, we compare and analyze the proposed algorithm with RMSE, Confirmed True Track, and Track Existence Probability that show better performance in terms of track retention and estimation than the existing PSNF-m and IPSNF algorithms.