Computational synthetic aperture radar (cSAR): a flexible signal simulator for multichannel SAR systems

Allan, Jeremy M.; Collins, Michael; Gierull, Christoph H.

doi:10.5589/m10-048

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…This section introduces the main methods of correlated clutter simulation based on single-pixel and correlated statistical modeling. SAR image simulation is a forward process for modeling the imaging procedure, and it is an important method for analyzing the validity and accuracy of the proposed statistical modeling [21]- [27]. Combining the two spatial correlation models introduced in the previous section, this section describes the corresponding correlated clutter simulation methods, namely, the correlated clutter simulation method based on the product model [37] and the correlated clutter simulation method based on the coherent scatterer model [32].…”

Section: Correlated Sar Clutter Simulationmentioning

confidence: 99%

“…The correlated clutter simulation methods introduced in the two previous sections replicate only the correlated clutter that satisfies the specific statistical distribution of SAR images, without considering the physical process of scattering. The computational SAR (cSAR) simulation system proposed by Allan et al [27], [32], [36] starts from the statistical modeling of the underlying scatterers and simulates correlated K-distributed clutter based on the incoherent scatterer sum model. The incoherent scatterer sum model is degraded from the coherent scatterer model when neglecting the phase [1].…”

Section: Simulation Based On the Coherent Scatterer Modelmentioning

confidence: 99%

“…It is an important tool for validating whether the properties of SAR images have been well understood. It has significant applications for despeckling [21], [22], target detection and recognition [23], [24], interpretation algorithm verification [25], [26], system design verification, and accuracy evaluation [27], [28]. It is a vital way to describe SAR clutter based on the statistical characteristics of observed data at a single-pixel and observed data at a single-pixel and the correlation structures of these data [29], [30].…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

Yue

Frery

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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Section: Correlated Sar Clutter Simulationmentioning

confidence: 99%

Section: Simulation Based On the Coherent Scatterer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

Yue

Frery

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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“…To demonstrate the effectiveness of the proposed PD-PAMF detector, we further conduct experiments by using the simulated radar data in a dense signal environment. Here, we utilize an airborne synthetic aperture radar (SAR) image scene (as shown in Figure 7, the azimuth is 2000 cells and the range is 800 cells) to simulate the echoes of an airborne array radar with 11 channels via point target simulation (details of the simulation method can be referenced in [36]). Obviously, there are many heterogeneous and strong discrete scatterers in the scene.…”

Section: Detection Performance Using Simulated Airborne Multichannel mentioning

confidence: 99%

A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

et al. 2020

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The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP) in practical applications. However, its detection performance is severely constrained by the training data, especially in a dense signal environment. Improper training data and contamination of moving target signals remarkably degrade the performance of disturbance suppression and result in target cancellation by self-whitening. To address these issues, a novel post-Doppler parametric adaptive matched filter (PD-PAMF) detector is proposed in the range-Doppler domain. Specifically, the detector is introduced via the post-Doppler matched filter (PD-MF) and the lower-diagonal-upper (LDU) decomposition of the disturbance covariance matrix, and the disturbance signals of the spatial sequence are modelled as an auto-regressive (AR) process for filtering. The purpose of detecting ground moving targets as well as for estimating their geographical positions and line-of-sight velocities is achieved when the disturbance is suppressed. The PD-PAMF is able to reach higher performances by using only a smaller training data size. More importantly, it is tolerant to moving target signals contained in the training data. The PD-PAMF also has a lower computational complexity. Numerical results are presented to demonstrate the effectiveness of the proposed detector.

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“…Each of these groups can be developed in the time or the frequency domain or hybrid time/frequency domain. Allan et al [16,17] presented a time domain SAR signal simulator for fixed and moving targets, as well as clutter, and then developed it in the multichannel SAR system. Geng et al [18] proposed a frequency spectrum model for the radar signal to simulate the SAR raw data in the frequency domain (2FD).…”

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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Computational synthetic aperture radar (cSAR): a flexible signal simulator for multichannel SAR systems

Cited by 9 publications

References 26 publications

Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

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

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