Automatic Target Recognition Using Recurrent Neural Networks

This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that: (1) the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and (2) the grammatical features contained in the radar cross-section (RCS) signals present spatial-temporalfrequency (STF) diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then a STF graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.

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“…• Temporal dimension model: LSTM [24] • Frequency dimension model: FFT-based CLEAN [25] • Temporal-spatial (TS) dimension model: STGCN [18],…”

Section: Baseline Models and Experimental Settingsmentioning

confidence: 99%

Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network

Meng

Peng

Wang

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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“…They are more feasible than CNNs in 1-D applications. In this respect, the high performance of LSTMs in 1-D classification problems has been demonstrated in radar applications such as; target classification with backscattering electromagnetic signals [23], automatic target recognition with radar cross-section [24] and SAR [25]. Even though LSTMs have high performances, some applications require extended feature extraction to represent targets in a better way [25].…”

Section: Related Workmentioning

confidence: 99%

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Toprak¹,

Selver²,

Seçmen³

et al. 2021

Turk J Elec Eng & Comp Sci

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Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties, spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However, especially for metamaterials, magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect, this study shows that the utilization late-time resonant scattering electromagnetic signals of magnetic spheres provide diverse information and features, which result in superior object classification performance. For this purpose, first, time-domain late-time resonant scattering electromagnetic signals are generated numerically for single and multilayer radially symmetrical dielectric and magnetic spheres. Then, by using emerging deep learning tools, particularly convolutional neural networks trained with spheres having different material properties, a high multilayer object classification performance is achieved. Furthermore, by incorporating the frequency characteristics of the late-time resonant scattering electromagnetic signals to the classification process through Fourier transform and convolutional neural network layers for feature extraction, a convolutional neural network with long short term memory algorithm is developed. The outcome of the proposed algorithm design is shown to be particularly successful even in the case of limited available data on challenging targets. This extended strategy is also shown to outperform modern data augmentation and transfer learning techniques in terms of accuracy as well as the computational cost.

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“…The classifier's final feature vector is determined by the pseudo-Zernike moments of the subsequent transformation. The extracted features ensure resilience contrary to the target's size and velocity of rotation, as well as the target's beginning motion phase [5,16,17].…”

Section: High-resolution Range Profile (Hrrp)mentioning

confidence: 99%

Deep Learning Network for Classifying Target of Same Shape using RCS Time Series

Narasimhamurthy¹,

Khalaf²

2021

Air Traffic Management and Control

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The main intension of this work is to find the warhead and decoy classification and identification. Classification of radar target is one of the utmost imperatives and hardest practical problems in finding out the missile. Detection of target in the pool of decoys and debris is one of the major radas technologies widely used in practice. In this study we mainly focus on the radar target recognition in different shapes like cone, cylinder and sphere based on radar cross section (RCS). RCS is a critical element of the radar signature that is used in this work to identify the target. The concept is to focus on new technique of ML for analyzing the input data and to attain a better accuracy. Machine learning has had a significant impact on the entire industry as a result of its high computational competency for target prediction with precise data analysis. We investigated various machine learning classifiers methods to categorize available radar target data. This chapter summarizes conventional and deep learning technique used for classification of radar target.

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Automatic Target Recognition Using Recurrent Neural Networks

Cited by 8 publications

References 8 publications

Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network

Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Deep Learning Network for Classifying Target of Same Shape using RCS Time Series

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