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.
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