Real-time motion compensation, image formation and image enhancement of moving targets in ISAR and SAR using S-method-based approach

Thayaparan, T.; Stanković, Ljubiša; Wernik, Cezary; Daković, Miloš

doi:10.1049/iet-spr:20070093

Cited by 50 publications

(15 citation statements)

References 13 publications

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“…이 때 burst들을 따르는 각각의 시간 10°/s이며, 여기서도 상대적으로 큰 회전 속도 및 긴 CPT로 인해 FT를 통해 획득한 ISAR 영상은 영상의 흐려짐 문제를 유발한 바 있다 [3]～ [5] . 그림 8은 MIG-25에 대해 STFT와 GWT [3] , 그리고 Q=64를 갖는 DGR 를 이용하여 획득한 ISAR 영상의 결과를 보여준다.…”

Section: 영상 획득을 위한 적용unclassified

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Efficient Acquisition of High-Quality ISAR Images Using the Discrete Gabor Representation in an Oversampling Scheme

Park¹,

Yang²,

Bae³

et al. 2013

The Journal of Korean Institute of Electromagnetic Engineering

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ISAR(Inverse SAR) AbstractInverse synthetic aperture radar(ISAR) images have been widely used in non-cooperative target recognition(NCTR). One of the most important issues in ISAR imaging is the improvement of the image smeared by target motion. In this paper, we propose the discrete Gabor representation(DGR) in an oversampling scheme for efficient acquisition of high-quality ISAR images. The DGR compartmentally assigns the Gabor coefficients to unit cells of the time-frequency grid related to the given Gabor logons. Thus, it can show an excellent time-frequency concentration and effectively discriminates the Doppler components from point-scatterers. The simulation results demonstrated that the DGR not only obtained high-quality ISAR images but also retained computational efficiency.

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Section: 영상 획득을 위한 적용unclassified

“…영상의 품질 향상을 위해 최적화 기법을 접목 한 TFT가 제시되었으나, 계산량이 많아 실시간 레이 더 신호 처리에 적용하기 부적합했다 [4] . 이에 최근에 는, ISAR 영상 형성의 정확성과 효율성을 모두 기할 수 있는 TFT 기법 개발에 중점을 두고 있다 [5] . …”

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Efficient Acquisition of High-Quality ISAR Images Using the Discrete Gabor Representation in an Oversampling Scheme

Park¹,

Yang²,

Bae³

et al. 2013

The Journal of Korean Institute of Electromagnetic Engineering

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“…For the timefrequency representation method, the Fourier transform is replaced by the time-frequency representation in the azimuth focusing. The suitable time-frequency representation should be free from cross-terms and has a high time frequency resolution, such as the L-Wigner distribution (LWD) [18], L-class of fourth-order complex-lag distribution and S-method [19][20]. Compared with the parametric method, the advantage of the time-frequency representation method is the little computational load.…”

Section: Introductionmentioning

confidence: 99%

New method of time-frequency representation for ISAR imaging of ship targets

Wang

2012

J. of Syst. Eng. Electron.

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Inverse synthetic aperture radar (ISAR) imaging of ship targets is very important in the national defense. For the high maneuverability of ship targets, the Doppler frequency shift of the received signal is time-varying, which will degrade the ISAR image quality for the traditional range-Doppler (RD) algorithm. In this paper, the received signal in a range bin is characterized as the multi-component polynomial phase signal (PPS) after the motion compensation, and a new approach of time-frequency representation, generalized polynomial Wigner-Ville distribution (GPWVD), is proposed for the azimuth focusing. The GPWVD is based on the exponential matched-phase (EMP) principle. Compared with the conventional polynomial Wigner-Ville distribution (PWVD), the EMP principle transfers the non-integer lag coefficients of the PWVD to the position of the exponential of the signal, and the interpolation can be avoided completely. For the GPWVD, the cross-terms between multi-component signals can be reduced by decomposing the GPWVD into the convolution of Wigner-Ville distribution (WVD) and the spectrum of phase adjust functions. The GPWVD is used in the ISAR imaging of ship targets, and the high quality instantaneous ISAR images can be obtained. Simulation results and measurement data demonstrate the effectiveness of the proposed new method.

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“…Specifically, non-stationary ISAR signals are produced when targets undergo oscillating motions, such as in the case of ships, or when they maneuver during the radar dwell time, as it happens in many scenarios including maritime, ground and aerial targets. Time-Frequency Analysis based algorithms have been introduced to solve such problems as they are designed to handle non stationary signals [15]- [17]. Moreover, as the image quality in terms of resolution and focus is strongly related to the target's own motions, a time-window approach has been introduced to optimally and automatically select the data set time-series [18].…”

Section: A Isar Image Formationmentioning

confidence: 99%

Moving Target Analysis in ISAR Image Sequences With a Multiframe Marked Point Process Model

Benedek

2014

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper we propose a Multiframe Marked Point Process model of line segments and point groups for automatic target structure extraction and tracking in Inverse Synthetic Aperture Radar (ISAR) image sequences. For the purpose of dealing with scatterer scintillations and high speckle noise in the ISAR frames, we obtain the resulting target sequence by an iterative optimization process, which simultaneously considers the observed image data and various prior geometric interaction constraints between the target appearances in the consecutive frames. A detailed quantitative evaluation is performed on 8 real ISAR image sequences of different carrier ship and airplane targets, using a test database containing 545 manually annotated frames.

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Real-time motion compensation, image formation and image enhancement of moving targets in ISAR and SAR using S-method-based approach

Cited by 50 publications

References 13 publications

Efficient Acquisition of High-Quality ISAR Images Using the Discrete Gabor Representation in an Oversampling Scheme

Efficient Acquisition of High-Quality ISAR Images Using the Discrete Gabor Representation in an Oversampling Scheme

New method of time-frequency representation for ISAR imaging of ship targets

Moving Target Analysis in ISAR Image Sequences With a Multiframe Marked Point Process Model

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