Signal-to-clutter measure for measuring automatic target recognition performance using complimentary eigenvalue distribution analysis

2003 ◽  
Vol 42 (4) ◽  
pp. 1144 ◽  
Author(s):  
S. Richard F. Sims
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Eigenvalue Distribution ◽  
Distribution Analysis

scholarly journals D-ATR for SAR Images Based on Deep Neural Networks

2019 ◽  
Vol 11 (8) ◽  
pp. 906 ◽  
Author(s):  
Zongyong Cui ◽  
Cui Tang ◽  
Zongjie Cao ◽  
Nengyuan Liu
Keyword(s):  
Neural Network ◽  
Processing Speed ◽  
Deep Neural Networks ◽  
Accurate Result ◽  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Sar Images ◽  
Sliding Method ◽  
Fast Processing

Automatic target recognition (ATR) can obtain important information for target surveillance from Synthetic Aperture Radar (SAR) images. Thus, a direct automatic target recognition (D-ATR) method, based on a deep neural network (DNN), is proposed in this paper. To recognize targets in large-scene SAR images, the traditional methods of SAR ATR are comprised of four major steps: detection, discrimination, feature extraction, and classification. However, the recognition performance is sensitive to each step, as the processing result from each step will affect the following step. Meanwhile, these processes are independent, which means that there is still room for processing speed improvement. The proposed D-ATR method can integrate these steps as a whole system and directly recognize targets in large-scene SAR images, by encapsulating all of the computation in a single deep convolutional neural network (DCNN). Before the DCNN, a fast sliding method is proposed to partition the large image into sub-images, to avoid information loss when resizing the input images, and to avoid the target being divided into several parts. After the DCNN, non-maximum suppression between sub-images (NMSS) is performed on the results of the sub-images, to obtain an accurate result of the large-scene SAR image. Experiments on the MSTAR dataset and large-scene SAR images (with resolution 1478 × 1784) show that the proposed method can obtain a high accuracy and fast processing speed, and out-performs other methods, such as CFAR+SVM, Region-based CNN, and YOLOv2.

Analysis of uncompensated phase error on automatic target recognition performance

2009 ◽  
Author(s):  
Lee J. Montagnino ◽  
Mary L. Cassabaum ◽  
Shawn D. Halversen ◽  
Chad T. Rupp ◽  
Gregory M. Wagner ◽  
...  
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Phase Error ◽  
Automatic Target Recognition

scholarly journals FEF-Net: A Deep Learning Approach to Multiview SAR Image Target Recognition

2021 ◽  
Vol 13 (17) ◽  
pp. 3493
Author(s):  
Jifang Pei ◽  
Zhiyong Wang ◽  
Xueping Sun ◽  
Weibo Huo ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Imaging System ◽  
Target Recognition ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Sar Images ◽  
Stationary Target ◽  
Single View ◽  
Learning Modules ◽  
Deep Feature Extraction ◽  
Classification Information

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

scholarly journals An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

2021 ◽  
Vol 13 (19) ◽  
pp. 3864
Author(s):  
Changjie Cao ◽  
Zongyong Cui ◽  
Zongjie Cao ◽  
Liying Wang ◽  
Jianyu Yang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Training Set ◽  
Stationary Target ◽  
Sample Set ◽  
Filtering Approach

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

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

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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