Moving target detection and characterization with circular SAR

Perlovsky, Leonid; Ilin, Roman; Deming, Ross; Linnehan, Rob; Lin, Feng

doi:10.1109/radar.2010.5494540

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Then, the algorithm searches for data samples that are more likely to be originated from those feature models. When DL is applied to analyze the SAR image generated from AFRL Gotcha data for the detection of ground moving vehicles [3], feature models for in Eq. (1) include classification features, locations, and time sequence t for various clutter types and ground moving targets.…”

Section: Signal Processing Framework Of Dynamic Logic Algorithmmentioning

confidence: 99%

“…In addition, application of DL directly to analyze SAR raw data can reduce the computational cost but requires the conceptual interpretation for analyzed results. Recently, application of DL [2,3] has demonstrated its merit in terms of assisting ISR systems to simultaneously detect, track and classify moving targets. DL can also be applied to analyze radar, electro-optical, and acoustical data and to exploit useful information for layered sensing to create situational awareness during irregular warfare which includes electronic warfare and cyber attack.…”

Section: Introductionmentioning

confidence: 99%

“…To illustrate how to establish the signal processing framework of DL for analyzing SAR raw data, in Section 3 we generated simulated X-band SAR raw data for a simulated ground scene with one moving vehicle on a road. Then, we devised new kinds of equations of progression (analogous to equations of motion used in [3]) as one of feature models for the moving target to perform target tracking and classification. In Section 4, we apply DL to analyze AFRL Gotcha 2006 X-band HH raw data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of dynamic logic algorithm to analyze AFRL gotcha data

Lin

Perlovsky

2012

SPIE Proceedings

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In this paper, we apply Dynamic Logic algorithm (DL) directly to analyze AFRL Gotcha raw data, which are sampled in terms of chirp frequencies for each pulse. This approach has not been done in the existing literature because previously, there is no visual or conceptual interpretation how to detect ground moving targets directly from synthetic aperture radar (SAR) raw data. Using Gotcha raw data taken from the severe radar clutter environment, we have demonstrated that DL not only maintains its robustness in terms of detecting small ground moving targets but also provides the effectiveness to extract specific features such as shape, size, location (along the range swath), movement direction, and traveling speed for the desired target.

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Section: Signal Processing Framework Of Dynamic Logic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of dynamic logic algorithm to analyze AFRL gotcha data

Lin

Perlovsky

2012

SPIE Proceedings

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“…In early research, researchers use long time observation characteristic of circular SAR to obtain the echo of moving target in the illuminated scene [19][20][21]. Many researchers have realized potential of using image sequence to detect moving targets in single channel SAR such as [20,22,23]. In 2015, Poisson et al [24] proposed a single channel algorithm to reconstruct the real trajectory and estimate the velocity which focuses on using circular SAR properties .…”

Section: Introductionmentioning

confidence: 99%

Single Channel Circular SAR Moving Target Detection Based on Logarithm Background Subtraction Algorithm

Shen

Lin

et al. 2018

Remote Sensing

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In this paper, a new moving target detection algorithm for single channel circular Synthetic Aperture Radar (SAR) is presented. Circular SAR can perform observation with circular trajectory and retrieve multiaspect information. Due to the circular trajectory, the moving target has long and complex apparent trace (smear trajectory), while the background (clutter) has relatively slow changing characteristic in limited azimuth integration. Thus, the background image can be filtered by using radiometric adjusted overlap subaperture images. Then, the Signal-to-Clutter Ratio (SCR) of the region where the strong backscattering clutter exists is enhanced with logarithm background subtraction technique. In the real data experiment, the maximum SCR improvement is 13 dB. Finally, the moving target can be detected using a Constant False Alarm (CFAR) detector. The algorithm is evaluated with one channel data of three channel GOTCHA-GMTI dataset.

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“…Thanks to the multiple azimuth direction, the azimuth ambiguity may be solved. A Ground Moving Target Trajectory Reconstruction in Single-Channel Circular SAR J.B. Poisson, Student Member, IEEE, H. Oriot, and F. Tupin, Senior Member, IEEE S method proposed in [21] uses multiple backprojection images as input of a framework called Dynamic Logic. This framework computes the maximum likelihood ratio between moving target Gaussian models and data, providing moving target detection and characterization.…”

Section: Introductionmentioning

confidence: 99%

Ground Moving Target Trajectory Reconstruction in Single-Channel Circular SAR

Poisson

Oriot

Tupin

2015

IEEE Trans. Geosci. Remote Sensing

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Abstract-Synthetic aperture radar has become an important technique for generating high-resolution images of the ground, because of its all-weather capabilities. SAR imaging of stationary scenes is nowadays well mastered. If targets are moving, it induces a delocalization and a defocusing effect in the azimuth direction in a SAR image. This last effect can be used to detect moving targets, to image them and to estimate their azimuthal velocity, but the main limitation is the impossibility to estimate the full target velocity vector, because of the Doppler shift dependency on azimuthal position and radial velocity. The purpose of this paper is to use several aspect angles thanks to a circular trajectory acquisition to retrieve the entire velocity and position vector. We first outline the steps of this trajectory reconstruction methodology, then we perform a mathematical analysis of this methodology and finally we present some tracking results on real data, around two French cities.

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Moving target detection and characterization with circular SAR

Cited by 10 publications

References 5 publications

Application of dynamic logic algorithm to analyze AFRL gotcha data

Application of dynamic logic algorithm to analyze AFRL gotcha data

Single Channel Circular SAR Moving Target Detection Based on Logarithm Background Subtraction Algorithm

Ground Moving Target Trajectory Reconstruction in Single-Channel Circular SAR

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