基于改进的FCM和信息熵修正的航迹融合算法

doi:10.7527/S1000-6893.2021.25236

Abstract

Abstract: When the quality of local track information is not balanced, the algorithm of choosing all local tracks for track fusion will lead to degradation of track quality. To improve tracking performance, a track fusion algorithm is proposed based on the improved Fuzzy C-Means (FCM) and information entropy correction. The "quality" of the clustering data is modified by the track information filtered with the Interactive Multi-mModel (IMM). The improved FCM algorithm is used to cluster the local track. The local tracks are selected and fused by using information entropy and membership degree to modify the clustering center and improve the quality of the system track. Simulation results show that when multiple sensors are used to track maneuvering targets, the tracking performance of the proposed algorithm is better than the known track fusion algorithms in the case of changing of sensor observation accuracy and measurement loss.

Key words: incomplete information, information entropy, membership, track selection, track fusion, fuzzy clustering

CLC Number:

V243.2
TP391

ZHEN Xu, LIU Fang. Track fusion algorithm based on improved FCM and information entropy correction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 325236-325236.

References

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Track fusion algorithm based on improved FCM and information entropy correction

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