Radar Imaging

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Toru Sato - One of the best experts on this subject based on the ideXlab platform.

  • ultra wideband Radar Imaging using a hybrid of kirchhoff migration and stolt f k migration with an inverse boundary scattering transform
    IEEE Transactions on Antennas and Propagation, 2015
    Co-Authors: Takuya Sakamoto, Toru Sato, Pascal Aubry, Alexander Yarovoy
    Abstract:

    In this paper, we propose a fast and accurate Radar-Imaging algorithm that combines Kirchhoff migration with Stolt’s frequency-wavenumber (F-K) migration. F-K migration is known as a fast-Imaging method in the F-K domain, while Kirchhoff migration is reported to be more accurate. However, Kirchhoff migration requires the reflection points to be located as a function of the antenna position and the delay time. This prevents the use of fast Fourier transforms (FFTs) because Kirchhoff migration must be processed in the time domain, and this can be extremely time consuming. The proposed algorithm overcomes this hurdle by introducing the texture angle and the inverse boundary scattering transform (IBST). These two tools enable the locations of the reflection points to be determined rapidly for each pixel of a Radar image. The Radar signals are then modified according to the Kirchhoff integral, before Stolt F-K migration is applied in the frequency domain to produce an accurate Radar image. To demonstrate the performance of the proposed method, the conventional delay-and-sum (DAS) migration, Kirchhoff migration, Stolt F-K migration, and the proposed method are applied to the same measured datasets.

  • auto focusing uwb array Radar Imaging of a target in unknown motion using muller and buffington metrics and cross range blurriness
    URSI General Assembly and Scientific Symposium, 2014
    Co-Authors: Takuya Sakamoto, Toru Sato, Pascal Aubry, Alexander Yarovoy
    Abstract:

    We propose two target speed estimation methods for an ultra-wideband Radar Imaging system. The system consists of array antennas scanning while a target is moving. The proposed methods use the Muller and Buffington sharpness metric and cross-range blurriness of Radar images to estimate the target speed, and compensate for the estimated motion to generate focused images automatically. The proposed methods are applied to the measurement of a knife, handgun and mannequin on an electrically-controlled moving stage. Measurement results showed that both the proposed methods can estimate target speeds, but with different accuracies. It was also confirmed that the proposed methods can generate high-quality images for moving targets with unknown speeds.

  • method for the three dimensional Imaging of a moving target using an ultra wideband Radar with a small number of antennas
    IEICE Transactions on Communications, 2012
    Co-Authors: Takuya Sakamoto, Yuji Matsuki, Toru Sato
    Abstract:

    Ultra wideband (UWB) Radar is considered a promising technology to complement existing camera-based surveillance systems because, unlike cameras, it provides excellent range resolution. Many of the UWB Radar Imaging algorithms are based on large-scale antenna arrays that are not necessarily practical because of their complexity and high cost. To resolve this issue, we previously developed a two-dimensional Radar Imaging algorithm that estimates unknown target shapes and motion using only three antennas. In this paper, we extend this method to obtain three-dimensional images by estimating three-dimensional motions from the outputs of five antennas. Numerical simulations confirm that the proposed method can estimate accurately the target shape under various conditions.

  • super resolution uwb Radar Imaging algorithm based on extended capon with reference signal optimization
    IEEE Transactions on Antennas and Propagation, 2011
    Co-Authors: Shouhei Kidera, Takuya Sakamoto, Toru Sato
    Abstract:

    Near field Radar employing ultrawideband (UWB) signals with its high range resolution has great promise for various sensing applications. It enables non-contact measurement of precision devices with specular surfaces like an aircraft fuselage and wing, or a robotic sensor that can identify a human body in invisible situations. As one of the most promising Radar algorithms, the range points migration (RPM) was proposed. This achieves fast and accurate surface extraction, even for complex-shaped objects, by eliminating the difficulty of connecting range points. However, in the case of a more complex shape whose variation scale is less than a pulsewidth, it still suffers from image distortion caused by multiple interference signals with different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon method, known as frequency domain interferometry (FDI). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The results obtained from numerical simulations and an experiment with bi-static extension of the RPM prove that super-resolution UWB Radar Imaging is accomplished by the combination between the RPM and the extended Capon methods, even for an extremely complex-surface target including edges.

  • super resolution uwb Radar Imaging algorithm based on extended capon with reference signal optimization
    European Conference on Antennas and Propagation, 2010
    Co-Authors: Shouhei Kidera, Takuya Sakamoto, Toru Sato
    Abstract:

    Near field Radar employing UWB (Ultra Wideband) signals with its high range resolution provides various sensing applications. It enables a robotic or security sensor that can identify a human body even in invisible situations. As one of the most efficient Radar algorithms, the RPM (Range Points Migration) is proposed. This achieves fast and accurate estimating shapes of surfaces, even for complex-shaped targets by eliminating the difficulty of connecting range points. However, in the case of a complicated target surface whose variation scale is less than wavelength, it still suffers from image distortion caused by multiple interference signals mixed together by different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon, known as FDI (Frequency Domain Interferometry). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The result obtained from numerical simulation proves that superresolution UWB Radar Imaging is accomplished by the proposed method, even for an extremely complex-shaped targets including edges.

Takuya Sakamoto - One of the best experts on this subject based on the ideXlab platform.

  • ultra wideband Radar Imaging using a hybrid of kirchhoff migration and stolt f k migration with an inverse boundary scattering transform
    IEEE Transactions on Antennas and Propagation, 2015
    Co-Authors: Takuya Sakamoto, Toru Sato, Pascal Aubry, Alexander Yarovoy
    Abstract:

    In this paper, we propose a fast and accurate Radar-Imaging algorithm that combines Kirchhoff migration with Stolt’s frequency-wavenumber (F-K) migration. F-K migration is known as a fast-Imaging method in the F-K domain, while Kirchhoff migration is reported to be more accurate. However, Kirchhoff migration requires the reflection points to be located as a function of the antenna position and the delay time. This prevents the use of fast Fourier transforms (FFTs) because Kirchhoff migration must be processed in the time domain, and this can be extremely time consuming. The proposed algorithm overcomes this hurdle by introducing the texture angle and the inverse boundary scattering transform (IBST). These two tools enable the locations of the reflection points to be determined rapidly for each pixel of a Radar image. The Radar signals are then modified according to the Kirchhoff integral, before Stolt F-K migration is applied in the frequency domain to produce an accurate Radar image. To demonstrate the performance of the proposed method, the conventional delay-and-sum (DAS) migration, Kirchhoff migration, Stolt F-K migration, and the proposed method are applied to the same measured datasets.

  • auto focusing uwb array Radar Imaging of a target in unknown motion using muller and buffington metrics and cross range blurriness
    URSI General Assembly and Scientific Symposium, 2014
    Co-Authors: Takuya Sakamoto, Toru Sato, Pascal Aubry, Alexander Yarovoy
    Abstract:

    We propose two target speed estimation methods for an ultra-wideband Radar Imaging system. The system consists of array antennas scanning while a target is moving. The proposed methods use the Muller and Buffington sharpness metric and cross-range blurriness of Radar images to estimate the target speed, and compensate for the estimated motion to generate focused images automatically. The proposed methods are applied to the measurement of a knife, handgun and mannequin on an electrically-controlled moving stage. Measurement results showed that both the proposed methods can estimate target speeds, but with different accuracies. It was also confirmed that the proposed methods can generate high-quality images for moving targets with unknown speeds.

  • method for the three dimensional Imaging of a moving target using an ultra wideband Radar with a small number of antennas
    IEICE Transactions on Communications, 2012
    Co-Authors: Takuya Sakamoto, Yuji Matsuki, Toru Sato
    Abstract:

    Ultra wideband (UWB) Radar is considered a promising technology to complement existing camera-based surveillance systems because, unlike cameras, it provides excellent range resolution. Many of the UWB Radar Imaging algorithms are based on large-scale antenna arrays that are not necessarily practical because of their complexity and high cost. To resolve this issue, we previously developed a two-dimensional Radar Imaging algorithm that estimates unknown target shapes and motion using only three antennas. In this paper, we extend this method to obtain three-dimensional images by estimating three-dimensional motions from the outputs of five antennas. Numerical simulations confirm that the proposed method can estimate accurately the target shape under various conditions.

  • super resolution uwb Radar Imaging algorithm based on extended capon with reference signal optimization
    IEEE Transactions on Antennas and Propagation, 2011
    Co-Authors: Shouhei Kidera, Takuya Sakamoto, Toru Sato
    Abstract:

    Near field Radar employing ultrawideband (UWB) signals with its high range resolution has great promise for various sensing applications. It enables non-contact measurement of precision devices with specular surfaces like an aircraft fuselage and wing, or a robotic sensor that can identify a human body in invisible situations. As one of the most promising Radar algorithms, the range points migration (RPM) was proposed. This achieves fast and accurate surface extraction, even for complex-shaped objects, by eliminating the difficulty of connecting range points. However, in the case of a more complex shape whose variation scale is less than a pulsewidth, it still suffers from image distortion caused by multiple interference signals with different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon method, known as frequency domain interferometry (FDI). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The results obtained from numerical simulations and an experiment with bi-static extension of the RPM prove that super-resolution UWB Radar Imaging is accomplished by the combination between the RPM and the extended Capon methods, even for an extremely complex-surface target including edges.

  • super resolution uwb Radar Imaging algorithm based on extended capon with reference signal optimization
    European Conference on Antennas and Propagation, 2010
    Co-Authors: Shouhei Kidera, Takuya Sakamoto, Toru Sato
    Abstract:

    Near field Radar employing UWB (Ultra Wideband) signals with its high range resolution provides various sensing applications. It enables a robotic or security sensor that can identify a human body even in invisible situations. As one of the most efficient Radar algorithms, the RPM (Range Points Migration) is proposed. This achieves fast and accurate estimating shapes of surfaces, even for complex-shaped targets by eliminating the difficulty of connecting range points. However, in the case of a complicated target surface whose variation scale is less than wavelength, it still suffers from image distortion caused by multiple interference signals mixed together by different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon, known as FDI (Frequency Domain Interferometry). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The result obtained from numerical simulation proves that superresolution UWB Radar Imaging is accomplished by the proposed method, even for an extremely complex-shaped targets including edges.

Zheng Bao - One of the best experts on this subject based on the ideXlab platform.

  • high resolution inverse synthetic aperture Radar Imaging and scaling with sparse aperture
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Mengdao Xing, Lei Zhang, Xianggen Xia, Qianqian Chen, Zheng Bao
    Abstract:

    In high-resolution Radar Imaging, the rotational motion of targets generally produces migration through resolution cells (MTRC) in inverse synthetic aperture Radar (ISAR) images. Usually, it is a challenge to realize accurate MTRC correction on sparse aperture (SA) data, which tends to degrade the performance of translational motion compensation and SA-Imaging. In this paper, we present a novel algorithm for high-resolution ISAR Imaging and scaling from SA data, which effectively incorporates the translational motion phase error and MTRC corrections. In this algorithm, the ISAR image formation is converted into a sparsity-driven optimization via maximum a posterior (MAP) estimation, where the statistics of an ISAR image is modeled as complex Laplace distribution to provide a sparse prior. The translational motion phase error compensation and cross-range MTRC correction are modeled as joint range-invariant and range-variant phase error corrections in the range-compressed phase history domain. Our proposed Imaging approach is performed by a two-step process: 1) the range-invariant and range-variant phase error estimations using a metric of minimum entropy are employed and solved by using a coordinate descent method to realize a coarse phase error correction. Meanwhile, the rotational motion can be obtained from the estimation of range-variant phase errors, which is used for ISAR scaling in the cross-range dimension; 2) under a two-dimensional (2-D) Fourier-based dictionary by involving the slant-range MTRC, joint MTRC-corrected ISAR Imaging and accurate phase adjustment are realized by solving the sparsity-driven optimization with SA data, where the residual phase errors are treated as model error and removed to achieve a fine correction. Finally, some experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

  • resolution enhancement for inversed synthetic aperture Radar Imaging under low snr via improved compressive sensing
    IEEE Transactions on Geoscience and Remote Sensing, 2010
    Co-Authors: Lei Zhang, Mengdao Xing, Chengwei Qiu, Jialian Sheng, Zheng Bao
    Abstract:

    The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture Radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR Imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in Radar Imaging, which challenges current high-resolution Imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution Imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS Imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

  • high resolution three dimensional Radar Imaging for rapidly spinning targets
    IEEE Transactions on Geoscience and Remote Sensing, 2008
    Co-Authors: Qi Wang, Mengdao Xing, Zheng Bao
    Abstract:

    A 3-D inverse synthetic aperture Radar Imaging method for rapidly spinning targets, i.e., a generalized Radon transform (GRT)-CLEAN algorithm, is proposed in this paper. The signal model is first changed into an equivalent high-speed turntable model after the compensation of the translational motion. Second, based on the relationship between the range profile variation of the spinning targets and the scatterers' positions, the GRT is utilized to estimate the scatterers' positions. Finally, combining the GRT with the modified CLEAN approach, the parameters of each scatterer and, thus, the 3-D image of the targets can be obtained. In addition to the development of the GRT-CLEAN algorithm, the estimation and compensation of the linear translational motion error in the Imaging process is also considered in this paper. Good images yielded confirm the effectiveness of the GRT-CLEAN algorithm in the simulations.

Alexander Yarovoy - One of the best experts on this subject based on the ideXlab platform.

  • ultra wideband Radar Imaging using a hybrid of kirchhoff migration and stolt f k migration with an inverse boundary scattering transform
    IEEE Transactions on Antennas and Propagation, 2015
    Co-Authors: Takuya Sakamoto, Toru Sato, Pascal Aubry, Alexander Yarovoy
    Abstract:

    In this paper, we propose a fast and accurate Radar-Imaging algorithm that combines Kirchhoff migration with Stolt’s frequency-wavenumber (F-K) migration. F-K migration is known as a fast-Imaging method in the F-K domain, while Kirchhoff migration is reported to be more accurate. However, Kirchhoff migration requires the reflection points to be located as a function of the antenna position and the delay time. This prevents the use of fast Fourier transforms (FFTs) because Kirchhoff migration must be processed in the time domain, and this can be extremely time consuming. The proposed algorithm overcomes this hurdle by introducing the texture angle and the inverse boundary scattering transform (IBST). These two tools enable the locations of the reflection points to be determined rapidly for each pixel of a Radar image. The Radar signals are then modified according to the Kirchhoff integral, before Stolt F-K migration is applied in the frequency domain to produce an accurate Radar image. To demonstrate the performance of the proposed method, the conventional delay-and-sum (DAS) migration, Kirchhoff migration, Stolt F-K migration, and the proposed method are applied to the same measured datasets.

  • auto focusing uwb array Radar Imaging of a target in unknown motion using muller and buffington metrics and cross range blurriness
    URSI General Assembly and Scientific Symposium, 2014
    Co-Authors: Takuya Sakamoto, Toru Sato, Pascal Aubry, Alexander Yarovoy
    Abstract:

    We propose two target speed estimation methods for an ultra-wideband Radar Imaging system. The system consists of array antennas scanning while a target is moving. The proposed methods use the Muller and Buffington sharpness metric and cross-range blurriness of Radar images to estimate the target speed, and compensate for the estimated motion to generate focused images automatically. The proposed methods are applied to the measurement of a knife, handgun and mannequin on an electrically-controlled moving stage. Measurement results showed that both the proposed methods can estimate target speeds, but with different accuracies. It was also confirmed that the proposed methods can generate high-quality images for moving targets with unknown speeds.

  • modified kirchhoff migration for uwb mimo array based Radar Imaging
    IEEE Transactions on Geoscience and Remote Sensing, 2010
    Co-Authors: Xiaodong Zhuge, Alexander Yarovoy, T G Savelyev, L P Ligthart
    Abstract:

    In this paper, the formulation of Kirchhoff migration is modified for multiple-input-multiple-output (MIMO) array-based Radar Imaging in both free-space and subsurface scenarios. By applying the Kirchhoff integral to the multistatic data acquisition, the integral expression for the MIMO Imaging is explicitly derived. Inclusion of the Snell's law and the Fresnel's equations into the integral formulation further expends the migration technique to subsurface Imaging. A modification of the technique for strongly offset targets is proposed as well. The developed migration techniques are able to perform Imaging with arbitrary MIMO configurations, which allow further exploration of the benefits of various array topologies. The proposed algorithms are compared with conventional diffraction stack migration on free-space synthetic data and experimentally validated by ground-penetrating Radar experiments in subsurface scenarios. The results show that the modified Kirchhoff migration is superior over the conventional diffraction stack migration in the aspects of resolution, side-lobe level, clutter rejection ratio, and the ability to reconstruct shapes of distributed targets.

  • uwb array based Radar Imaging using modified kirchhoff migration
    International Conference on Ultra-Wideband, 2008
    Co-Authors: Xiaodong Zhuge, Alexander Yarovoy, T G Savelyev, L P Ligthart
    Abstract:

    This paper presents a new modification of Kirchhoff migration algorithm for ultra-wideband (UWB) array-based Radar Imaging. The developed algorithm is evolved from traditional Kirchhoff migration which is based on the classical integral theorem of Helmholtz and Kirchhoff. The new algorithm is designed for array-based Radar Imaging with arbitrary multiple input multiple output (MIMO) configuration. The developed algorithm is compared with conventional diffraction stack migration using both synthetic data from numerical simulation and measurement data from landmine detection. The results have shown promising improvements in the aspects of beamwidth, side-lobe rejection ratio and the ability to reconstruct shapes of distributed targets.

Mengdao Xing - One of the best experts on this subject based on the ideXlab platform.

  • high resolution inverse synthetic aperture Radar Imaging and scaling with sparse aperture
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Mengdao Xing, Lei Zhang, Xianggen Xia, Qianqian Chen, Zheng Bao
    Abstract:

    In high-resolution Radar Imaging, the rotational motion of targets generally produces migration through resolution cells (MTRC) in inverse synthetic aperture Radar (ISAR) images. Usually, it is a challenge to realize accurate MTRC correction on sparse aperture (SA) data, which tends to degrade the performance of translational motion compensation and SA-Imaging. In this paper, we present a novel algorithm for high-resolution ISAR Imaging and scaling from SA data, which effectively incorporates the translational motion phase error and MTRC corrections. In this algorithm, the ISAR image formation is converted into a sparsity-driven optimization via maximum a posterior (MAP) estimation, where the statistics of an ISAR image is modeled as complex Laplace distribution to provide a sparse prior. The translational motion phase error compensation and cross-range MTRC correction are modeled as joint range-invariant and range-variant phase error corrections in the range-compressed phase history domain. Our proposed Imaging approach is performed by a two-step process: 1) the range-invariant and range-variant phase error estimations using a metric of minimum entropy are employed and solved by using a coordinate descent method to realize a coarse phase error correction. Meanwhile, the rotational motion can be obtained from the estimation of range-variant phase errors, which is used for ISAR scaling in the cross-range dimension; 2) under a two-dimensional (2-D) Fourier-based dictionary by involving the slant-range MTRC, joint MTRC-corrected ISAR Imaging and accurate phase adjustment are realized by solving the sparsity-driven optimization with SA data, where the residual phase errors are treated as model error and removed to achieve a fine correction. Finally, some experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

  • bayesian inverse synthetic aperture Radar Imaging
    IEEE Geoscience and Remote Sensing Letters, 2011
    Co-Authors: Mengdao Xing, Lei Zhang, Yabo Liu
    Abstract:

    In this letter, a novel algorithm of inverse synthetic aperture Radar (ISAR) Imaging based on Bayesian estimation is proposed, wherein the ISAR Imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.

  • resolution enhancement for inversed synthetic aperture Radar Imaging under low snr via improved compressive sensing
    IEEE Transactions on Geoscience and Remote Sensing, 2010
    Co-Authors: Lei Zhang, Mengdao Xing, Chengwei Qiu, Jialian Sheng, Zheng Bao
    Abstract:

    The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture Radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR Imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in Radar Imaging, which challenges current high-resolution Imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution Imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS Imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

  • high resolution three dimensional Radar Imaging for rapidly spinning targets
    IEEE Transactions on Geoscience and Remote Sensing, 2008
    Co-Authors: Qi Wang, Mengdao Xing, Zheng Bao
    Abstract:

    A 3-D inverse synthetic aperture Radar Imaging method for rapidly spinning targets, i.e., a generalized Radon transform (GRT)-CLEAN algorithm, is proposed in this paper. The signal model is first changed into an equivalent high-speed turntable model after the compensation of the translational motion. Second, based on the relationship between the range profile variation of the spinning targets and the scatterers' positions, the GRT is utilized to estimate the scatterers' positions. Finally, combining the GRT with the modified CLEAN approach, the parameters of each scatterer and, thus, the 3-D image of the targets can be obtained. In addition to the development of the GRT-CLEAN algorithm, the estimation and compensation of the linear translational motion error in the Imaging process is also considered in this paper. Good images yielded confirm the effectiveness of the GRT-CLEAN algorithm in the simulations.