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2017-03-13

A Time-Frequency Feature Fusion Algorithm Based on Neural Network for HRRP

By Lele Yuan
Progress In Electromagnetics Research M, Vol. 55, 63-71, 2017
doi:10.2528/PIERM16123002

Abstract

In this paper, a feature fusion algorithm is proposed for automatic target recognition based on High Resolution Range Profiles (HRRP). The proposed algorithm employs Convolution Neural Network (CNN) to extract fused feature from the time-frequency features of HRRP automatically. The time-frequency features used include linear transform and bilinear transform. The coding of the CNN's largest output node is the target category, and the output is compared with a threshold to decide whether the target is classified to a pre-known class or an unknown class. Simulations by four different aircraft models show that the proposed feature fusion algorithm has higher target recognition performance than single features.

Citation


Lele Yuan, "A Time-Frequency Feature Fusion Algorithm Based on Neural Network for HRRP," Progress In Electromagnetics Research M, Vol. 55, 63-71, 2017.
doi:10.2528/PIERM16123002
http://www.jpier.org/PIERM/pier.php?paper=16123002

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