Application Framework of Deep Learning in Radar Communication Target Recognition

2021 ◽  
Keyword(s):  
Deep Learning ◽  
Target Recognition ◽  
Application Framework

scholarly journals Hierarchical Recognition System for Target Recognition from Sparse Representations

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
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.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Prediction of Caption and Emoji of an Image using Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3721-3724
Keyword(s):  
Deep Learning ◽  
Social Networking ◽  
Web Application ◽  
Automatic Generation ◽  
Application Framework ◽  
Challenging Problem ◽  
Artificial Intelligence Research ◽  
Learning Techniques ◽  
User Friendly ◽  
Visually Impaired Persons

With the invention of deep learning, there is a good progress in image classification. But automatic generation of captions for images is still a challenging problem and is in the initial stages of artificial intelligence research. Automatic description of images has applications in social networking and will be useful to visually impaired persons. This paper concentrates on designing a user-friendly web application framework which can predict the caption of an image using deep learning techniques. The verbs and objects present in the caption are used for forming the emoji and for predicting the major color of the image

Target recognition method based on single-pixel imaging system and deep learning in the noisy environment

2020 ◽  
Vol 49 (6) ◽  
pp. 20200010
Author(s):  
石峰 Feng Shi ◽  
陆同希 Tongxi Lu ◽  
杨书宁 Shuning Yang ◽  
苗壮 Zhuang Miao ◽  
杨晔 Ye Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Target Recognition ◽  
Noisy Environment ◽  
Recognition Method ◽  
Single Pixel

scholarly journals A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1972
Author(s):  
Dhiraj Neupane ◽  
Jongwon Seok
Keyword(s):  
Deep Learning ◽  
Target Recognition ◽  
Rapid Development ◽  
Automatic Target Recognition ◽  
Data Driven ◽  
Underwater Sound ◽  
Sonar Systems ◽  
Sonar Signal Processing ◽  
Types Of Information ◽  
Data Acquisition Process

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

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