SAR Target Recognition via Meta-Learning and Amortized Variational Inference

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

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Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data

Remote Sensing ◽

10.3390/rs13040750 ◽

2021 ◽

Vol 13 (4) ◽

pp. 750

Author(s):

Jingjing Wang ◽

Zheng Liu ◽

Rong Xie ◽

Lei Ran

Keyword(s):

Test Data ◽

Learning Strategy ◽

Target Recognition ◽

Recognition Performance ◽

Constraint Propagation ◽

Automatic Target Recognition ◽

Label Propagation ◽

Training Data ◽

Dynamic Learning ◽

Radar Automatic Target Recognition

For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.

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Synthetic aperture radar automatic target recognition with three strategies of learning and representation

Optical Engineering ◽

10.1117/1.602495 ◽

2000 ◽

Vol 39 (5) ◽

pp. 1230 ◽

Cited By ~ 38

Author(s):

Qun Zhao

Keyword(s):

Synthetic Aperture Radar ◽

Target Recognition ◽

Automatic Target Recognition ◽

Synthetic Aperture ◽

Radar Automatic Target Recognition ◽

Aperture Radar

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Hierarchical Recognition System for Target Recognition from Sparse Representations

Mathematical Problems in Engineering ◽

10.1155/2015/527095 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 10

Author(s):

Zongyong Cui ◽

Zongjie Cao ◽

Jianyu Yang ◽

Hongliang Ren

Keyword(s):

Deep Learning ◽

Deep Structure ◽

Target Recognition ◽

Recognition System ◽

Automatic Target Recognition ◽

Feature Representation ◽

Small Data ◽

Learning Method ◽

Classification Problems ◽

Restricted Boltzmann Machines

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.

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