Motion Analysis in SAR Images of Unfocused Objects Using Time–Frequency Methods

Kersten, P.R.; Jansen, Robert W.; Luc, K.; Ainsworth, T.L.

doi:10.1109/lgrs.2007.896318

Cited by 48 publications

(22 citation statements)

References 13 publications

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“…The Doppler parameters can be obtained by parameter estimation from SLC data, which is beyond the scope of this paper. A number of approaches for Doppler parameter estimation have been developed and typical examples are referred to in [43][44][45]. One should carefully examine the properties of a given system and data before Doppler parameter estimation because the efficiency of each method largely depends on the system parameters and configuration.…”

Section: Discussionmentioning

confidence: 99%

“…Among the various approaches, the joint time-frequency analysis (JTFA) has been popularly applied to measure object motion directly from a chirp signal and was successful in retrieving the velocity by estimating the Doppler frequency rate of a moving object in the time-frequency domain [40][41][42]. The JTFA demonstrated the potential to extract a time sequence of motion parameters [43,44]. It is possible to measure the velocity of a ground moving vehicle with an error of less than 5% for a velocity higher than 3 m/s [45].…”

Section: Residual Focusing Of Sar Slc Datamentioning

confidence: 99%

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Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image

Park¹,

Kim

Won

2017

Remote Sensing

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Ground moving targets distort normally-focused synthetic aperture radar (SAR) images. Since most high-resolution SAR data providers only offer single-look complex (SLC) data rather than raw signals to general users, they need to apply a simple and efficient residual SAR focusing to SLC data containing moving targets. This paper presents an efficient and effective SAR residual focusing method that is practically applicable to SLC data. The residual Doppler spectrum of the moving target is derived from a general SAR configuration and normal SAR focusing. The processing steps are simple and straightforward, with a limited size of the processing window, e.g., 64 × 64. Application results using simulation data and actual TerraSAR-X SLC data with a speed-controlled vehicle demonstrate the effectiveness of the method, which particularly improves the −3 dB width, integrated sidelobe ratio, and symmetry of the reconstructed signals. In particular, the azimuthal symmetry becomes seriously distorted when the target speed is higher than 8 m/s (or 28.8 km/h), and the symmetry is well recovered by the proposed method.

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Section: Discussionmentioning

confidence: 99%

Section: Residual Focusing Of Sar Slc Datamentioning

confidence: 99%

Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image

Park¹,

Kim

Won

2017

Remote Sensing

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“…The processed raw data are presented in Fig. 4, from which it can be seen that a focused image at the expected position is shown for the fixed target (T3); the targets (T1, T2) with range velocities show a azimuth shift phenomenon; the targets (T6, T7) with azimuth velocities represent the smear effect; the targets (T4, T5) with range and azimuth velocities show a displaced and smear phenomenon [28].…”

Section: Man-made Scenes Simulationmentioning

confidence: 99%

Efficient Strip-Mode Sar Raw Data Simulator of Extended Scenes Included Moving Targets

Yang¹,

Yu²,

Luo³

et al. 2013

PIER B

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Abstract-An accurate and efficient SAR raw data generator is of considerable value for testing system parameters and the imaging algorithms. However, most of the existing simulators concentrate on the raw signal simulation of the static extended scenes and targets. Actually the raw signal simulator of the moving targets is highly desired to quantitatively support the application of the ground moving targets indication. The raw data simulation can be exactly realized in the time domain but not efficient especially when simulating an extended scene. As for the issues, the analytical expression for the 2-D signal spectrum of moving targets with constant acceleration is derived and a fast raw data simulation method in the 2-D frequency domain based on inverse ω-κ algorithm is proposed in this paper, where the inverse STOLT interpolation is applied to simulate the range-azimuth couple. So it is more efficient than the time domain one by making use of Fast Fourier Transform (FFT). Simulation results for a man-made scene and a real SAR scene are provided to demonstrate its validity and effectiveness.

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“…If the SAR raw data contain a moving target, the processed SAR has unfocused and/or displaced areas [5]. To detect moving targets in single-channel SAR data, researchers have proposed many methods based on the spectrum filtering algorithm [6,7], shear average algorithm [8], RDM (reflectivity displacement method) algorithms [9], time-frequency analysis algorithm [5,10], sub-aperture algorithm [11], keystone transform algorithm [12], wavelet transform algorithm [13,14], and so on. To evaluate the performance of these methods, the researchers need various SAR raw data containing moving targets with different velocities in different directions, clutter model types, different noise models, etc.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

Rahmanizadeh

Amini

2017

Remote Sensing

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SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse SAR imaging methods for the raw data simulations has achieved good results in the simulation of the static targets, but for the simulation of moving targets these methods have a few shortcomings. In this paper, we propose a method to simulate a speckled scene with moving targets in the hybrid domain. First, the scene is simulated, including speckle, which is statistically in accordance with real SAR image behavior. Then, a reverse imaging algorithm (inverse chirp scaling) was applied on the scene to generate the SAR raw data. The moving target simulation was also done in the time-domain as the next step. Finally, the results from two prior steps were superposed to generate the SAR raw data with moving targets. All steps of the proposed method were evaluated separately. The speckle procedure was evaluated by comparing the speckled SAR image before the simulation and the image of the SAR simulated raw data. The results show similar variations in real and imaginary parts of these data. The correlation between the reflectivity map and the SAR images after the simulation was calculated and the obtained correlation coefficient was about 0.95 for different images. The final data were further analyzed for the displacement of moving targets' positions. The results show similar displacement between moving target SAR raw data with a background and without a background.

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Motion Analysis in SAR Images of Unfocused Objects Using Time–Frequency Methods

Cited by 48 publications

References 13 publications

Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image

Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image

Efficient Strip-Mode Sar Raw Data Simulator of Extended Scenes Included Moving Targets

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

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