Abstract
The difficulty in infrared and radar fusion target detection lies in the pre-processing steps such as sample reduction and feature reduction of heterogeneous sensors. For the problem of data volume redundancy, HVS sample reduction is proposed to reduce the amount of data in the infrared data set. Infrared and radar joint feature vectors are constructed through nearest neighbor data association. For the problem of feature redundancy, a weighted mutual information feature selection method based on prior information is proposed according to the difference of feature sources. The fusion data set is then used for classification. The experiments show that the HVS-based sample reduction and the prior weighted feature selection achieved a higher detection probability and a shorter detection time, and improved the robustness in the case of large differences between the training set and the test set.
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This research was funded by the National Natural Science Foundation of China grant numbers 61271376; Natural Science Foundation of Anhui Province grant numbers 1208085MF114.
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Wei, Y., Cheng, Z., Zhu, B. et al. Infrared and radar fusion detection method based on heterogeneous data preprocessing. Opt Quant Electron 51, 339 (2019). https://doi.org/10.1007/s11082-019-2053-z
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DOI: https://doi.org/10.1007/s11082-019-2053-z