Range-Doppler Mapping of Space-Based Targets Using the JRO 50 MHz Radar

Kesaraju, Saiveena; Mathews, J. D.; Milla, Marco; Vierinen, Juha

doi:10.1007/s11038-017-9510-0

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…However, millisecond resolution is available. The radar operation is at a single frequency ( f =5.125 MHz), and plots are generated by applying a sidelobe‐free decoding filter (Kesaraju et al, ; Lehtinen et al, ) to a Barker‐13‐coded radar pulse. Notice that E and F region returns are received at the same time.…”

Section: Resultsmentioning

confidence: 99%

“…where h d [n] is the decoding filter and is the inverse of the coding filter, h c [n], in frequency domain, which is the essence of this technique. Similar implementation of this technique can also be found at Kesaraju et al (2017), who provide a path to codes that verify this approach. For the case of Barker-13-coded pulses, the SNR performance of the sidelobe-free decoding is 95% with respect to a matched filter.…”

Section: • Interference Removalmentioning

confidence: 98%

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An HF Software‐Defined Radar to Study the Ionosphere

et al. 2019

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In this paper, a novel design and implementation of a software‐defined high‐frequency ionospheric radar, the Penn State Ionospheric Radar Imager (PIRI), is described. Furthermore, preliminary results produced by the system (located at 40.71° N, 77.97° W) are presented. PIRI is designed to be a modest and low‐cost radar system, which is composed mostly of commercial‐off‐the‐shelf products and utilizing open‐source software to perform pulse generation, pulse coding, downconversion, data acquisition, and signal processing. It is designed to be mobile, as it can easily be deployed at temporary locations to study local ionospheric disturbances. For the results presented herein, the radar operating frequency was 5.125 MHz. However, as the system is software defined and short active receive antennas are used, only the transmit antenna needs to be changed to operate over the entire high‐frequency (HF) band. The two orthogonal receive antennas enable both linear and circular polarization measurements. Peak transmit power of the system is 500 W. PIRI is designed to be a modest and cost‐effective alternative to the current standard HF ionospheric sounding systems and can be readily replicated.

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Section: Resultsmentioning

confidence: 99%

Section: • Interference Removalmentioning

confidence: 98%

An HF Software‐Defined Radar to Study the Ionosphere

et al. 2019

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“…However, due to the distinct intrinsic data characteristics of ISAR image, it may not be easy to directly transplant the existing SAR image ATR techniques to ISAR image ATR. Generally, the SAR target is usually cooperative, which means that the imaging geometry and relative parameters between SAR platform and the target can be precisely controlled by motion sensors and accurate compensation technology [11]. And since the imaging projection plane (IPP), radar bandwidth and imaging accumulation angle are known prior information, the true shape and size of the target could be accurately recovered based on range and azimuth resolution.…”

Section: A Problem Statementmentioning

confidence: 99%

“…In addition, due to the change of motion direction, radar bandwidth, imaging accumulation angle/time, the range and azimuth sampling rate, ISAR image ATR faces numerous challenging issues such as image rotation sensitivity, azimuth sensitivity, translation sensitivity, etc. To sum up, the data characteristics of ISAR image mainly include the following two aspects: on the one hand, ISAR image reflects the back-scattering coefficients of dominant scattering centers, which would change rapidly with the IPP and exhibit strong anisotropy [11], [12]. On the other hand, for the same target, the size and shape of its ISAR images vary with radar bandwidth and imaging accumulation angle/time even in the case of constant IPP, which brings great difficulties to extract robust feature of ISAR image ATR.…”

Section: A Problem Statementmentioning

confidence: 99%

A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

et al. 2021

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To tackle the inherent unknown deformation (e.g., translation, rotation and scaling) of the inverse synthetic aperture radar (ISAR) images, a deep polar transformer-circular convolutional neural network, i.e., PT-CCNN, is proposed to achieve deformation robust ISAR image automatic target recognition (ATR) in this paper. Inspired by human visual system and canonical coordinate of Lie-groups, we adopt a polar transformer module to transform the deformation ISAR images to the log-polar representations, before which a conventional convolutional neural network (CNN) is utilized to predict the origin of log-polar transformation. The above techniques make the proposed network invariant to translation, and equivariant to rotation and scaling. On this basis, for the log-polar representations with wrap-around structure, a circular convolutional neural network (CCNN) is further applied to extract more effective and highly discriminative features and improve recognition accuracy. The proposed network is end-to-end trainable with a classification loss, and could extract deformation-robust and essential features automatically. For multiple practical ISAR image datasets of six satellites, the performance testing and comparison experiments demonstrate that the techniques utilized in PT-CCNN are effective, and our proposed network achieve higher recognition accuracy than those previous common methods based on deep learning. For instance, our proposed PT-CCNN beats traditional CNN on rotation, scaling and practical deformation datasets by 24.5-49.3%, 9.0-40.8% and 22.3-26.7%. And it also outperforms the polar CNN without using the above techniques on rotation, scaling and practical deformation datasets by 9.2-53.7%, 5.2-54.6% and 9.0-49.9%. Additionally, the presented visualization results show the abilities and advantages of our method in terms of handling image deformation and extracting effective features.INDEX TERMS Automatic target recognition (ATR), inverse synthetic aperture radar (ISAR), deep convolutional neural network (DCNN), image deformation, log-polar transformation.

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“…Compared with optical images, ISAR images generally have no complex background, and the information available for feature extraction is usually only intensity, phase, polarization scattering, etc., and there is no available information such as optical image color, texture, etc. Moreover, the backward radiation method also leads to different amplitude values of the same component in different ISAR images, and the signal intensity changes dynamically, which makes it difficult for the classical image semantic segmentation model to mine the amplitude pattern of each component in ISAR images of the satellite target [ 11 , 12 ]. Therefore, the segmentation performance is not satisfactory when the relevant segmentation methods of optical images are used to carry out the semantic segmentation study of ISAR images [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching

Zhu,

Zhang,

et al. 2023

Sensors

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The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.

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Range-Doppler Mapping of Space-Based Targets Using the JRO 50 MHz Radar

Cited by 6 publications

References 31 publications

An HF Software‐Defined Radar to Study the Ionosphere

An HF Software‐Defined Radar to Study the Ionosphere

A Deformation Robust ISAR Image Satellite Target Recognition Method Based on PT-CCNN

An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching

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