Eunjung Yang

Adaptive beamforming for low-angle target tracking under multipath interference

Angle of arrival estimation of a low-altitude target over the sea surface is a difficult problem due to the multipath coherent interference from the image target. We present a new monopulse technique utilizing an iterative interference cancellation algorithm based on the array antenna structure to overcome performance degradation. Our algorithm is compared with the maximum likelihood estimator, as well as with the existing algorithm employing the specular reflected error finding function.

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.

Bistatic ISAR image reconstruction using sparse-recovery interpolation of missing data

When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR (Bi-ISAR) images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new Bi-ISAR image reconstruction method that includes three steps: suboptimal estimation of parameters regarding the bistatic angle in the Bi-ISAR signal model via an orthogonal matching pursuit-type group-searching scheme, Bi-ISAR signal reconstruction using the estimated parameters, and Bi-ISAR image generation using the FT-based imaging algorithm applied to the reconstructed Bi-ISAR signal. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the physical optics technique as well as the point-scatterer model are used for Bi-ISAR image reconstruction. The results show that the proposed sparse-recovery-interpolation approach based on the Bi-ISAR signal model reconstruction combined with the classical FT-based algorithm can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional numerical interpolation methods and existing direct sparse reconstruction techniques.

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.

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.

표적 식별 성능 향상을 위한 EMD를 이용한 HRRP의 잡음 제거 기법

본 논문에서는 레이다 표적식별 성능을 향상시키기 위하여 고해상도 거리측면도(High Resolution Range Profile: HRRP)에 포함된 잡음을 효과적으로 제거하는 방법을 제안한다. 제안된 기법은 HRRP에 포함된 잡음의 통계적인 특성과 EMD (Empirical Mode Decomposition) 알고리즘을 이용하여 HRRP에 포함된 잡음을 효과적으로 제거한다. 잡음 제거 실험 결과에서는, 본 논문에서 제안한 기법이 잡음을 효과적으로 제거하면서, 표적 식별 성능을 크게 향상시키는 것을 수치적으로 확인할 수 있었다.

Analysis of Target Identification Performances Using Bistatic ISAR Images

Inverse synthetic aperture radar(ISAR) image generated from bistatic radar(Bi-ISAR) represents two-dimensional scattering distribution of a target, and the Bi-ISAR can be used for bistatic target identification. However, Bi-ISAR has large variability in scattering mechanisms depending on bistatic configurations and do not represent exact range-Doppler information of a target due to inherent distortion. Thus, an efficient training DB construction is the most important factor in target identification using Bi-ISARs. Recently, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic target identification, was applied to target identification using high resolution range profiles(HRRPs) generated from bistatic radar(BiHRRPs), to construct efficient training DB under bistatic configurations. Consequently, high identification performance was achieved using only small amount of training Bi-HRRPs, when the target is a considerable distance away from the bistatic radar. Thus, flight scenarios based training DB construction is applied to target identification using Bi-ISARs. Then, the capability and efficiency of the method is analyzed.

Analysis of Target Identification Performances against the Moving Targets Using a Bistatic Radar

AbstractBistatric radar can perform detection and identification for stealth targets that are rarely detected by the conventional monostatic radar. However, high resolution range profile(HRRP) generated from the received signal in the bistatic radar cannot show exact range information of the target because the bistatic geometry lead to the distortions of the bistatic HRRP. In addition, electromagnetic scattering mechanisms of the target are varied depending on the bistatic geometry. Thus, effi-cient database construction is a crucial factor to achieve successful classification capability in bistatic target identification. In this paper, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic radar, is applied to bistatic target identification. Then, the capability and efficiency of the method is analyzed. Simulation results show that reliable identification performance can be achieved using the database construction based on the flight scenarios when the target is a considerable distance away from the bistatic radar.

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.

Inter-Pulse Motion Compensation of an ISAR Image Generated by Stepped Chirp Waveform Using Improved Particle Swarm Optimization

Inverse synthetic aperture radar(ISAR) is coherent imaging system formed by conducting signal processing of received data which consists of radar cross section(RCS) reflected from maneuvering target. A novel algorithm is proposed to compensate inter-pulse motion(IPM) for the purpose of forming an well-focused ISAR image through signals generated by stepped chirp waveform(SCW). The velocity and acceleration of the target related to IPM are estimated based on particle swarm optimization (PSO) which has been widely used in optimization technique. Furthermore, a modified PSO which enables us to improve the performance of PSO is used to compensate IPM in a very short-time. Simulation results using point scatterer model of a Boeing-737 aircraft validate the performance of the proposed algorithm.