ISAR Autofocus Imaging Algorithm for Maneuvering Targets Based on Phase Retrieval and Gabor Wavelet Transform

The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular accelerations. In this paper, on the basis of the phase retrieval algorithm and the Gabor wavelet transform (GWT), we propose a new method for phase error correction. The approach first performs the range compression on ISAR raw data to obtain range profiles, and then carries out the GWT transform as the time-frequency analysis tool for the rotational motion compensation (RMC) requirement. The time-varying terms, caused by rotational motion in the Doppler frequency shift, are able to be eliminated at the selected time frame. Furthermore, the processed backscattered signal is transformed to the one in the frequency domain while applying the phase retrieval to run the translational motion compensation (TMC). Phase retrieval plays an important role in range tracking, because the ISAR echo module is not affected by both radial velocity and the acceleration of the target. Finally, after the removal of both the rotational and translational motion errors, the time-invariant Doppler shift is generated, and radar returned signals from the same scatterer are always kept in the same range cell. Therefore, the unwanted motion effects can be removed by applying this approach to have an autofocused ISAR image of the maneuvering target. Furthermore, the method does not need to estimate any motion parameters of the maneuvering target, which has proven to be very effective for an ideal range–Doppler processing. Experimental and simulation results verify the feasibility of this approach

Ground-based ISAR imaging of cooperative and non-cooperative sea vessels with 3-D rotational motion

Includes bibliographical references (leaves 175-188).Inverse Synthetic Aperture Radar (ISAR) images of sea vessels are a rich source of information for radar cross section (RCS) measurement and ship classification. However, ISAR imaging of sea vessels is a challenging task because the 3-D rotational motion of such vessels often gives rise to blurring. Blurry ISAR images are not desirable because they lead to inaccurate parameter estimation, which reduces the probability of correct classification. The objective of this thesis is to explain how 3-D rotational motion causes blurring in ISAR imagery and to develop effective techniques for imaging cooperative and non-cooperative sea vessels for RCS measurement and ship-classification purposes respectively. Much research has been done to investigate the effect of 3-D rotational motion on an ISAR image under the assumption that an object's axis of rotation is constant over the coherent processing interval (CPI). In this thesis, a new quaternion-based system model is proposed to characterise the amount of blurring in an ISAR image when a sea vessel possesses 3-D rotational motion over a CPI. Simulations were done to characterise the migration of a scatterer through Doppler cells due to the time-varying nature of the Doppler generating axis of rotation. Simulation results with realistic 3-D rotational motion show substantial blurring in the cross-range dimension of the resulting ISAR image, and this blurring is attributed to the time-varying nature of the angle of the Doppler generating axis of rotation and the object's rotation rate over the CPI

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

ON SOME COMMON COMPRESSIVE SENSING RECOVERY ALGORITHMS AND APPLICATIONS

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its’ common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested

Short-Time Velocity Identification and Coherent-Like Detection of Ultrahigh Speed Targets

Finding a balance between observation duration and detection rates is the ultimate goal of the detection of ultrahigh speed targets. However, short observation durations, both across range unit, and Doppler frequency migration, may severely limit the detection performance of ultrahigh speed targets. Although, traditional coherent integration methods can efficiently accumulate signal energy to produce a high signal-to-noise-ratio measurement, they often need to search for unknown motion parameters. This search is time consuming and unacceptable for the real-time detection of ultrahigh speed targets. In this paper, a coherent-like detection method is designed based on the finite-dimension theory of Wigner matrices along with velocity identification. The proposed method can efficiently integrate signal energy without rendering motion parameters. We use the distribution and mean of the eigenvalues of the constructed matrix, i.e., an additive Wigner matrix, to identify velocities and detect ultrahigh speed targets, respectively. Simulation results validate the theoretical derivation, superiority and operability of the proposed method

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested