Convolutional neural networks-based ship target recognition using high resolution range profiles

Karabayir, Osman; Yucedag, Okan Mert; Kartal, M.; Serim, Huseyin Avni

doi:10.23919/irs.2017.8008207

Cited by 25 publications

(7 citation statements)

References 8 publications

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“…Commonly used sensitivity removal approaches include time-shift compensation, energy normalization, and average processing [54]- [56]. The DNN structures used for radar HRRP target recognition include the deep belief network [54], [55], recurrent attentional network [57], [58], concatenated neural network, CNNs [62]- [64], stacked auto-encoder (SAE) [65], and convolutional LSTM [66], [67].…”

Section: A Dl-based Atr Using Hrr Profilesmentioning

confidence: 99%

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Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

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A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.

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Section: A Dl-based Atr Using Hrr Profilesmentioning

confidence: 99%

“…with POFACETS & 3D facet models of aircrafts. In [64], the HRRP data for 6 military and 4 civilian ship targets are simulated based on CAD models assuming X-band maritime radar. Another feasible alternative is data augmentation with generative adversarial network (GAN).…”

Section: A Dl-based Atr Using Hrr Profilesmentioning

confidence: 99%

Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

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“…Analytical solutions can provide precise results but only for simple regular objects [ 5 ]. High-frequency methods fail to take all EM phenomena into account [ 6 , 7 , 8 ]. Considering all EM effects, the full-wave technique is widely used in EM scattering studies for complex targets [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Target Identification with Improved 2D-VMD for Carrier-Free UWB Radar

Zhu

Zhang

Zhao

et al. 2021

Sensors

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In recent years, the interest in radar automatic target recognition (RATR) based on the carrier-free ultra-wideband (UWB) radar has been increasing. Compared with narrow-band and other bandwidth radars, the echo signal of the carrier-free UWB radar includes more comprehensive and detailed information with respect to the targeted object. In this paper, we first utilized 3ds Max to acquire accurate geometric models and applied a time-domain integral equation (TDIE) for echo signal acquisition under the condition that the transmitted signals had an extremely short duration period. By comparing the simulated waveform with the actual one, the accuracy of the electromagnetic modeling is verified. Furthermore, given that the actual environment is full of noise and clutter, we propose an improved two-dimensional variational mode decomposition (2D-IVMD), and an algorithm is proposed to eliminate noise and extract edge features preliminarily, which lays a foundation for further in-depth feature extraction. Then, the deep conventional neural network (DCNN) is introduced for the final recognition. The results show that the proposed methods achieve promising classification performance under the condition of low signal-to-noise ratio (SNR) values.

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“…Their dataset originated from actual measurements of four aircraft scale models. In [14], CNN was also adopted to achieve target recognition, with the dataset being derived from the simulation calculation of 10 ship targets. In [15], when CNN was employed for target recognition, white noise was added to expand the dataset.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile

et al. 2020

Sensors

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In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures.

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Convolutional neural networks-based ship target recognition using high resolution range profiles

Cited by 25 publications

References 8 publications

Deep-Learning for Radar: A Survey

Deep-Learning for Radar: A Survey

Target Identification with Improved 2D-VMD for Carrier-Free UWB Radar

A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile

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