One-dimension hierarchical local receptive fields based extreme learning machine for radar target HRRP recognition

Abstract

Radar automatic target recognition (RATR) aims at extracting meaningful target features from the electromagnetic echo signal and utilizing the features to automatically recognize the target types. The high-resolution range profile (HRRP) plays an important role in RATR field, HRRP is the amplitude of the echo summation for target scattering centers in each range cell of wideband radar. Using deep neural networks for HRRP radar target recognition encounters the problem of storage overhead and slow convergence rate, to resolve those issues, we propose a one-dimension local receptive fields based extreme learning auto-encoder (1D ELM-LRF-AE) network for HRRP local structures and meaningful representations learning. ELM-LRF-AE consists of an input layer, a random convolution layer, a pooling layer, several local connected layers and an output layer, it reconstructs the input with a greedy strategy that the input feature vectors are divided into several subgroups and the i-th pooling feature vector is used to reconstruct the i-th grouping input feature vector. Then we use the learned pooling feature vectors to replace the random pooling feature vectors as the learned representations. We also stack several 1D ELM-LRF-AEs to build 1D hierarchical local receptive fields based extreme learning machine (1D H-ELM-LRF) for high level HRRP abstract representations learning and recognition. Experimental results on simulated HRRP data set demonstrate the superior high recognition performance and high computational efficiency of our algorithm.