一种新的数据融合航迹关联算法

doi:10.3969/j.issn.1001-2400.2012.01.013

Abstract

Abstract:

Because of being highly dependent on the threshold and lacking the consideration of global solutions, the Nearest Neighbors Algorithm(NNA) will make some mistakes when the density of targets is high. To solve this problem, global search strategy, gating and dynamic programming are used to build a new correlation algorithm-the Global Best Track Correlation Algorithm(GBTCA). In two experiments, both of the two algorithms are run and compared to NNA, GBTCA, which shows that the new algorithm has a higher correct correlation rate and is less dependent on threshold values.

Key words: data fusion, track correlation, global search, dynamic programming, gating

CLC Number:

TP391

QUAN Yining;JIANG Zhen;HUANG Xiaodong;LI Weifeng. Study of a new track correlation algorithm in data fusion[J].J4, 2012, 39(1): 67-74.

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Study of a new track correlation algorithm in data fusion

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