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IDCDP: a novel data preprocessing method
Author(s): Yu Cheng; Lijun Liu; Xi Li Sr.; Xiaoyu Huang
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Paper Abstract

Correct tracks obtained through effective data preprocessing are important to multi-target data fusion, especially in the circumstance of dense targets and strong interference. Outliers and target switch arise in the uncertainty of measurement data in the practical applications. In this paper, we propose a novel real-time data preprocessing method for outlier detection and target switch identification to obtain correct tracks, named as IDCDP (i.e., innovation and density combined data preprocessing). Experimental results demonstrate the effectiveness of IDCDP, which achieves outlier-free adaptive filtering and provides authentic tracks for multi-target data fusion.

Paper Details

Date Published: 6 May 2019
PDF: 9 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106906 (6 May 2019); doi: 10.1117/12.2524216
Show Author Affiliations
Yu Cheng, PLA Troops (China)
Lijun Liu, PLA Troops (China)
Xi Li Sr., PLA Troops (China)
Xiaoyu Huang, PLA Troops (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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