Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Abstract

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders

“…Autoencoders, and particularly denoising autoencoders, have been successfully used in many fields such as music, character recognition or medical image segmentation, but, in addition, they are currently being used in remote sensing to perform recognition and scene classification. For example, Zhao et al [ 34 ] combined Stacked Autoencoder (SAE) and Extreme Machine Learning (ELM) techniques for target recognition from raw data of High-Resolution Range Profile (HRRP) acquired from three different aircraft, achieving a faster time response than other deep learning models. Other authors such as Kang et al [ 35 ] used 23 baseline features and three-patch Local Binary Pattern (LBP) features that were cascaded and fed into an SAE for recognition of 10-class SAR targets.…”

A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

“…In ELM, activation function g(x) selects Sigmoidal function and the number of hidden layer nodes L can be obtained by grid search. Since the introduction of ELM is mainly to verify the effectiveness of the proposed feature extraction algorithm, this paper does not do much discussion on ELM, readers can refer to [29,30] for a detailed analysis of ELM. Step1: Initialize population size, quantum gene number, maximum iteration number and mutation probability of IDCQGA.…”

“…Pattern recognition theory and artificial neural network can effectively solve these problems [27,28]. Taking the support vector machine (SVM) and extreme learning machine (ELM) classifiers as example, SVM and ELM construct feature space according to feature dimension, and establish classification hyperplane between features of different categories [29,30]. The hyperplane is the result of feature vector synthesis of all dimensions.…”

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

“…(2) methods based on high-resolution range profile (HRRP) [7][8][9][10][11]. Liu et al [12] proposed a multi-scale target classification method based on the scale-space theory through extracting features from HRRP; and (3) methods based on inverse synthetic aperture radar (ISAR) [13][14][15][16][17].…”