Ji-Hoon Bae

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 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.

Performance of Sparse Recovery Algorithms for the Reconstruction of Radar Images From Incomplete RCS Data

In this letter, we compare the performances of sparse recovery algorithms (SRAs) for the reconstruction of a 2-D inverse synthetic aperture radar (ISAR) image from incomplete radar-cross-section (RCS) data. The three methods considered for the SRA include the basis pursuit (BP), the BP denoising, and the orthogonal matching pursuit methods. The performances of the methods in terms of the reconstruction accuracy of the ISAR image are compared using the incomplete RCS data. In addition, traditional interpolation methods such as nearest-neighbor interpolation, linear interpolation, and spline interpolation are applied to the incomplete RCS data to reconstruct ISAR images, and their performances are compared to that of the SRAs.

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.

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

Particle swarm optimization coupled with exhaustive search method (PSO-ESM) is proposed for inverse synthetic aperture radar 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 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.

Classification of ISAR images using sparse recovery algorithms

In this study, we evaluate the classification accuracy of inverse synthetic aperture radar (ISAR) images reconstructed using the conventional Fourier transform (FT) and sparse recovery algorithms based on compressive sensing (CS) from incomplete radar cross section (RCS) data. When data are missing from the received RCS dataset, we cannot obtain correct ISAR images using the FT-based method. To alleviate this problem, we propose the use of sparse recovery algorithms. Results show that performing ISAR classification using sparse recovery algorithms can provide reliable classification accuracy, even though the received RCS datasets are incomplete, whereas the FT-based method is unable to do so.

Simultaneous estimation of rotation velocity and center for ISAR cross-range scaling

In this paper, a novel algorithm estimating not only targets rotation velocity (RV) but also rotation center (RC) is proposed for inverse synthetic aperture radar (ISAR) image cross-range scaling. Scale invariant feature transform (SIFT) is applied to two different ISAR images formed at different aspect angles for extracting non-fluctuating scattering points. Then, a criterion based on the distance between RC and locations of extracted features is optimized through the proposed algorithm based on particle swarm optimization (PSO) combined with exhaustive search method. Simulation results show that the proposed method can accurately estimate both RV and RC of a target.

A Study on the ISAR Image Reconstruction Algorithm Using Compressive Sensing Theory under Incomplete RCS Data

본 논문에서는 불완전한 radar-cross-section(RCS) 데이터로부터 inverse synthetic aperture radar(ISAR) 영상 복원과 동시에 표적의 회전각도를 추정하기 위한 compressive sensing(CS) 기반의 레이더 신호 모델을 적용한 parametric sparse 복원 알고리즘을 제안하고자 한다. Sparse 복원 알고리즘으로는 iteratively-reweighted-least-square(IRLS) 기법을 이용하여 각도 방향(cross-range)에서 모르는 처프 비율(chirp rate)의 처프 성분을 포함하는 레이더 신호 모델과 결합한다. 그리고, particle swarm optimization(PSO) 최적화 알고리즘을 이용하여 표적의 회전각도와 연관된 파라미터들을 추출한다. 따라서, RCS데이터 샘플에 데이터 손실이 발생하더라도 본 논문의 IRLS 기반 parametric sparse 복원 알고리즘에 따라 효율적으로 ISAR 영상을 복원할 수 있고, 동시에 표적의 회전각도를 추정할 수 있다. 또한, 불완전한 RCS 데이터 샘플에 대하여 영상의 엔트로피 관점에서 본 논문에서 제안한 방법의 성능과 전통적인 보간법의 성능을 서로 비교 관찰한다.In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimi- zation(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.