Min-Seok Kang

ISAR Cross-Range Scaling Using Iterative Processing via Principal Component Analysis and Bisection Algorithm

In this paper, we propose a novel cross-range scaling technique to estimate the rotational velocity (RV) of a maneuvering target. The proposed method includes three steps. First, a feature from accelerated segment test (FAST) is applied to two sequential inverse synthetic aperture radar (ISAR) images to find the locations of their robust feature points. Second, the rotation angle (RA) is estimated using two major axes, which are obtained using a principal component analysis (PCA) of the two feature data sets scaled by a candidate RV. Third, an RV search operation based on the measured RA is carried out via the bisection algorithm, which optimizes a newly devised cost function. Compared with the conventional method, the proposed method has two main advantages: 1) it requires no information about the rotation center of a target, and 2) it can efficiently generate a well-scaled ISAR image within a very short time. Finally, the results of experiments using point scatterers and real flying aircraft are provided to demonstrate the validity of the proposed method.

Efficient ISAR autofocus via minimization of Tsallis Entropy

Inverse synthetic aperture radar (ISAR) is a coherent imaging system formed by conducting signal processing on the received data consisting of radar cross section reflected from a maneuvering target. Autofocus is an essential step of ISAR imaging, whose performance has a great influence on the quality of the radar image. Minimum Shannon entropy phase adjustment (MSEPA) and minimum Renyi entropy-based algorithm (MREA) have been widely used to compensate for the phase error in ISAR autofocus. However, MSEPA and MREA have some drawbacks in terms of the noise sensitivity and computational efficiency. Tsallis entropy (nonextensive entropy) is a useful measure to describe the thermostatistical properties of physical systems. This paper concentrates on the performance of minimum Tsallis entropy phase adjustment (MTEPA) instead of the Shannon entropy. By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA. The order q of Tsallis entropy can be experimentally found by analyzing both an image quality metric and the computation time. In experimental results, the effectiveness of the MTEPA is illustrated and analyzed with simulated targets consisting of point scatterers and measured Boeing-747 data.

ISAR cross-range scaling using principal component analysis and exhaustive search algorithm

The rotational velocity (RV) of a target is important for inverse synthetic aperture radar (ISAR) imaging, and must be known in order to rescale ISAR images from the range-Doppler domain to the homogeneous range-cross-range domain. In this paper, we propose a novel cross-range scaling technique for estimating the correct RV from ISAR images. This method involves two steps. First, it needs to extract the locations of high-energy scattering centers from two sequential ISAR images. Second, the proposed method utilizes an exhaustive search to estimate the correct RV of two-dimensional (2D) ISAR images using principal component analysis (PCA) and an appropriate cost function. The proposed method has two main advantages: 1) it does not require information about the rotation center of the target, and 2) it can efficiently generate a well-rescaled 2D ISAR image within a very short time. We present experimental results to demonstrate the validity of the proposed method.

Bistatic-ISAR Cross-Range Scaling

In this paper, we introduce a bistatic inverse synthetic aperture radar (Bi-ISAR) cross-range scaling (CRS) method to more effectively use Bi-ISAR images in their applications. For this, we propose a method to estimate the effective rotation velocity of a target in a Bi-ISAR imaging system, and restore a linear-geometry distortion that yields a sheared shape of Bi-ISAR images. In the simulations, we observed that our proposed method is capable of performing robust and precise Bi-ISAR CRS.

Efficient ISAR Autofocus Technique Using Eigenimages

In this paper, we propose a new and efficient inverse synthetic aperture radar (ISAR) autofocus technique by introducing eigenimages to boost the speed of the traditional autofocus algorithms. First, a preprocessing step is applied to mitigate the noise components in the received data. Then, we perform an eigen-decomposition of the covariance matrix of the range-aligned data, and generate the signal eigenimage obtained by deriving the Fourier transform of a small number of eigenvectors corresponding to the dominant eigenvalues. Finally, traditional autofocus methods are combined with the proposed signal eigenimage rather than the original ISAR image to eliminate image blurring due to phase errors. The proposed method can significantly lower the computational complexity of the traditional autofocus methods because the dimensionality of the signal eigenimage is considerably smaller than that of the ISAR image. Despite the low dimensionality of the signal eigenimages, the proposed scheme provides well-focused ISAR images that are comparable to those of the traditional autofocus methods in terms of image focal quality. Several simulations and experimental results using measured data of an actual flying aircraft are presented to verify the effectiveness of the proposed method.

ISAR Imaging of High-Speed Maneuvering Target Using Gapped Stepped-Frequency Waveform and Compressive Sensing

In the case of a stepped-frequency waveform (SFW) inverse synthetic aperture radar (ISAR) system, the translational motion (TM) of a target can be usually divided into two parts: 1) target motion within a pulse repetition interval, called the inter-pulse translational motion (IPTM) and 2) target motion between bursts, called the inter-burst translational motion (IBTM). The former induces severe blurring in the ISAR images as well as range-compressed data (i.e., range profile), and the latter also causes dramatic degradation of the ISAR image quality. In this paper, a novel framework for high-resolution gapped SFW (GSFW) ISAR imaging of high-speed maneuvering target is proposed. The main novelty of the proposed method is twofold: 1) accurate TM parameter estimation in conjunction with a compressive sensing theory using a newly devised cost function and particle swarm optimization and 2) compensation for both the IPTM and IBTM phase errors simultaneously even with the GSFW data set. Simulation results using ideal point scatterers show that the proposed method is capable of precise reconstruction of ISAR image and accurate TM parameter estimation. Experimental results using real measured data verify the robustness and the effectiveness of the proposed method.In the case of a stepped-frequency waveform (SFW) inverse synthetic aperture radar (ISAR) system, the translational motion (TM) of a target can be usually divided into two parts: 1) target motion within a pulse repetition interval, called the inter-pulse translational motion (IPTM) and 2) target motion between bursts, called the inter-burst translational motion (IBTM). The former induces severe blurring in the ISAR images as well as range-compressed data (i.e., range profile), and the latter also causes dramatic degradation of the ISAR image quality. In this paper, a novel framework for high-resolution gapped SFW (GSFW) ISAR imaging of high-speed maneuvering target is proposed. The main novelty of the proposed method is twofold: 1) accurate TM parameter estimation in conjunction with a compressive sensing theory using a newly devised cost function and particle swarm optimization and 2) compensation for both the IPTM and IBTM phase errors simultaneously even with the GSFW data set. Simulation results using ideal point scatterers show that the proposed method is capable of precise reconstruction of ISAR image and accurate TM parameter estimation. Experimental results using real measured data verify the robustness and the effectiveness of the proposed method.

Bistatic-ISAR Distortion Correction and Range and Cross-Range Scaling

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted much attention because of its potential to overcome the certain limitations of monostatic ISAR imaging. However, range and cross-range scaling and bistatic distortion correction are essential for the efficient use of a bistatic ISAR (Bi-ISAR) image in their applications. In this paper, we introduce a novel range and cross-range scaling technique and bistatic distortion correction method for the restoration of a sheared (Bi-ISAR) image. The proposed method is composed of five stages: 1) extraction of the prominent scatterer using the range-bin selection method; 2) determination of the coefficients of the phase-history data in the selected range bin; 3) estimation of rotation center of the target using the optimization procedures via the particle swarm optimization algorithm; 4) range and cross-range scaling of (Bi-ISAR) image; and 5) restoration of the bistatic distortion that caused the sheared (Bi-ISAR) image. Finally, the proposed method can generate a well-scaled and restored (Bi-ISAR) image. An outstanding advantage of this method is that it requires no prior information about the bistatic configuration. Several examples are presented to validate the effectiveness of the proposed method.

Efficient Autofocus Chain for ISAR Imaging of Non-Uniformly Rotating Target

Conventional approaches to inverse synthetic aperture radar (ISAR) imaging of a non-uniformly rotating target are not optimal in terms of reconstructed image quality and/or computation time. Motivated by the problems of conventional approaches, we introduce an efficient autofocus chain for ISAR imaging of a non-uniformly rotating target. The most distinct feature of the proposed autofocus chain is found in the processing sequence to compensate for the phase error induced by the translational and rotational motion (RM) of the target. In contrast to conventional approaches, RM compensation is first implemented in the proposed autofocus chain immediately after range-alignment. Next, further focusing of the ISAR image is performed by estimating the residual phase errors, providing globally well-focused ISAR images. From the experimental results using real data sets, we can conclude that the proposed autofocus chain is highly efficient in forming ISAR images of a non-uniformly rotating target in terms of both image quality and computational efficiency.

ISAR cross-range scaling via joint estimation of rotation center and velocity [Correspondence]

Particle swarm optimization coupled with exhaustive search method (PSO-ESM) is proposed for inverse synthetic aperture radar (ISAR) cross-range scaling (CRS). Robust scatterers against angular scintillation are extracted using scale-invariant feature transform, and locations of the extracted scatterers are applied to PSO-ESM that estimate not only the rotation center (RC), but also rotation velocity (RV). In simulations, it was observed that PSO-ESM can perform robust CRS owing to the joint estimation of RC and RV.

ISAR autofocus by minimizing entropy of eigenimages

In this paper, we propose a novel and efficient inverse synthetic aperture radar (ISAR) autofocus technique by applying an eigenimage with a preprocessing step to minimum entropy phase adjustment (MEPA) algorithm. Several experimental results using measured data of an actual flying aircraft demonstrated that the proposed scheme can lead to substantially fast convergence to global minimum of the entropy cost surface and reduce the computational complexity of ISAR autofocus, compared to the traditional MEPA.