Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Liu, Chang; Antypenko, Ruslan; Sushko, Iryna; Zakharchenko, Oksana; Ji, Wang

doi:10.18280/ts.380531

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…The integration of BPA for the 3D mmW helical imaging system is carried out [17]. On the other hand, deep learning [43,44], which has been popular recently, has also been used in the imaging system for the improvement and reconstruction of the image [1,30,45,46].…”

Section: Related Workmentioning

confidence: 99%

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Duysak¹,

Yiğit²,

Seyfi³

2022

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Millimeter wave(mmW) imaging has spread to a wide range of applications in the last quarter. One of the most important research areas of mmW is three-dimensional (3D) imaging systems. In this study, conical differential range-based back-projection (BP) algorithm is proposed for three-dimensional mmW imaging. In the algorithm, the differential range is created using points inside a conical volume, thus the number of interpolation points is considerably reduced. The performance of the algorithm is demonstrated by simulation and experimental studies. Cylindrical scanning is carried out by means of the experimental setup. Experiments are carried out at frequencies of 26.5-40 GHz. The traditional BP algorithm (BPA) and the proposed algorithm are used to reconstruct the images. With the proposed method, it is observed that ISLR for the point target increased by about 5 dB compared to the traditional method. Moreover, the computational complexity is reduced by up to 10 times, depending on the imaging area. Thanks to the proposed method, the image of the concealed weapon under the cloth in an experimental study is more clearly focused compared to the traditional method. Therefore, it can provide images that give more accurate results for applications such as automatic target detection methods.

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Section: Related Workmentioning

confidence: 99%

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Duysak¹,

Yiğit²,

Seyfi³

2022

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“…In view of the extensive application of depth learning in computer vision, natural language processing, target detection, and gets good results, more and more researchers apply depth learning to radar signal modulation recognition [1][2][3] . At the same time, a metric learning method has emerged, which measures the distance between two samples to identify the same classes or different classes.…”

Section: Introductionmentioning

confidence: 99%

Unknown radar signal recognition based on deep metric learning

Wu¹,

Deng²

2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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In view of the fact that the current radar signal modulation recognition method based on depth learning needs enough samples to achieve good recognition effect, this paper designs a depth metric learning recognition method based on Cosine Softmax (CS Softmax), which has strong intra class aggregation and inter class separation, and can recognize unknown signals. First, the Wigner Ville distribution (WVD), Pseudo Wigner Ville distribution (PWVD) and Smooth Pseudo Wigner Ville distribution (SPWVD) of known signals are taken as the input, the vectors obtained from the network output are learned into the embedded space to identify radar signals, and several unknown signals are combined with the initial training set in the verification phase. The simulation results show that under the signal to noise ratio of 10dB, the recognition accuracy of the known signal of this method can reach about 94%, can distinguish the unknown signal from the known signal, and can distinguish the unknown signal separately, with an accuracy of about 85%.

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“…This concept is reversed in DOA estimation algorithms where, from the received signal samples the direction information of signals is estimated [1]. DOA estimation algorithm finds its application in variety of areas such as RADAR, navigation, SONAR, seismology, wireless mobile communications and many more [2].…”

Section: Introductionmentioning

confidence: 99%

Off-Grid Based DOA Estimation Algorithm Using Auto-Regression (1) Sparse Bayesian Learning with Linear Interpolation Model

Karigowda¹,

Nagaraj²

2022

MMEP

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On-grid approaches for DOA estimation majorly exhibits the problem of grid mismatch. Coarse grid leads to reduced estimation accuracy and dense grid leads to increased algorithm complexity and performance degradation due to highly correlated array manifold matrix. In this paper, a fixed off-grid DOA estimation algorithm is proposed to overcome this grid mismatch problem. Firstly, a sparsity based linear interpolation model for array manifold matrix is proposed to avoid the above limitation by introducing a bias parameter into the estimation framework. To solve this model, an Auto-regression (1) (AR (1)) based sparse Bayesian learning algorithm is proposed. To exploit the temporal correlation property of unknown DOA spatial spectrum in a MMV case, we develop this AR (1) model along with SBL to estimate the unknown DOA spectrum and expectation maximization (EM) framework to update the hyper-parameters. The results section shows that the proposed algorithm enjoys good estimation resolution and accuracy in the cases of fewer snapshots available, highly correlated signal sources, very low SNR and very closely spaced sources.

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Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Cited by 3 publications

References 25 publications

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Unknown radar signal recognition based on deep metric learning

Off-Grid Based DOA Estimation Algorithm Using Auto-Regression (1) Sparse Bayesian Learning with Linear Interpolation Model

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