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In order to improve the rapidity and accuracy of mass targets assignment under the condition of multi-platform cooperative air defense engagement, the features and limitations of current target assignment algorithms are analyzed, and a design idea and modeling method of air defense target assignment method is proposed based on multi-feature fusion and deep learning. By selecting multiple key feature attributes that affect the target assignment as input, the structure of target assignment optimization model is constructed based on deep neural network adapted to the scale of feature. Comparing with typical methods, the simulation results show that this proposed method can achieve better effectiveness on target assignment issues, and can describe the characteristic factors of battlefield environment more comprehensively, the over fitting and under fitting problems of the target assignment model based on deep neural network are balanced and optimized, and the calculation speed and accuracy of target assignment are effectively improved.
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Basic Information:
DOI：10.16358/j.issn.1009-1300.20220259
China Classification Code:E926.4;TP18
Citation Information:
[1]Yi Kai,Zhang Xiushe,Hu Xiaoquan ,et al.Air defense target assignment method based on multi-feature fusion and deep learning[J].Tactical Missile Technology,2023,No.220(04):165-172.DOI:10.16358/j.issn.1009-1300.20220259.