Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Ge, Beibei; An, Daoxiang; Chen, Leping; Wu, Wei; Feng, Dejun; Zhang, Zhimin

doi:10.1109/taes.2022.3141332

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…They have played a significant role in military reconnaissance, traffic monitoring, etc. [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Method for Calculating the Optimal Velocity Search Step Size for Airborne Three-Channel SAR Adaptive Clutter Suppression

Li,

Wang,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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For multichannel synthetic aperture radar (SAR) ground moving target indication (GMTI) systems, the clutter suppression method based on post-Doppler space time adaptive processing technology is popular due to its excellent performance. However, to coherently integrate a target's energy of each channel, this kind of method requires a search of target's velocity, which would introduce a large computation load when the search step size is too small. While the existing studies mainly focus on improving its performance in the cases of complex scenes and geometries, the studies on optimizing the search step size are rare. To address this issue, this paper proposes a method for calculating the optimal velocity search step size of adaptive clutter suppression for the commonly used airborne threechannel SAR-GMTI systems. First, the signal models in the range Doppler domain are developed. Then, considering that both the signal-to-clutter-noise ratio loss of clutter suppression and the coherent integration gain loss are introduced by the mismatch of target's steer vector, the relationship between the coherent accumulation gain loss and the velocity search error is utilized to develop the analytical expression for the optimal velocity search step size. Experimental results validate the proposed method.

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“…They have played a significant role in military reconnaissance, traffic monitoring, etc. [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Method for Calculating the Optimal Velocity Search Step Size for Airborne Three-Channel SAR Adaptive Clutter Suppression

Li,

Wang,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…SAR images have been widely applied in many fields, such as environmental protection, ocean monitoring, and military domains [1,2]. Target detection is a typical application of SAR images, attracting a number of research in academia and industry [3]. Amongst them, aircraft detection is a major task that plays an important role in airport management as well as battlefield reconnaissance.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, by integrating the complementary LA and CA, the proposed FLCAPN is able to learn local target features and contextual information effectively. The overall structure of FLCAPN is shown in Figure5, where( 2,3,4) i C i = represents the features extracted from the backbone network and i P represents the pyramid features finally output to the detector. This process is calculated as follows.…”

mentioning

confidence: 99%

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

Zhang,

Hu,

et al. 2023

Remote Sensing

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Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks.

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“…Synthetic aperture radar (SAR) is considered a wellestablished technology for providing day-and-night and weather-independent images, widely used in geological exploration, ocean research, disaster monitoring, military, environmental, and earth system monitoring, etc. [1], [2], [3], [4], [5], [6]. However, SAR is always an expensive means of monitoring because the expenditure of airplane flights or launching satellites is much higher than other optical or infrared imaging devices [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

Feng¹,

Daković²,

Ji³

et al. 2022

Preprint

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Generative Adversarial Networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some recent GANs (e.g., InfoGAN), are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images are with different properties due to the imaging mechanism. Despite the success of InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties, thus editing specific properties usually relies on empirical trials, unreliable and time-consuming. In this paper, we show that latent codes are disentangled to affect the properties of SAR images in a non-linear manner. By introducing some property estimators for latent codes, we are able to provide a completely analytical nonlinear model to decompose the entangled causality between latent codes and different properties. The qualitative and quantitative experimental results further reveal that the properties can be calculated by latent codes, inversely, the satisfying latent codes can be estimated given desired properties. In this case, properties can be manipulated by latent codes as we expect.

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Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Cited by 16 publications

References 40 publications

A Method for Calculating the Optimal Velocity Search Step Size for Airborne Three-Channel SAR Adaptive Clutter Suppression

A Method for Calculating the Optimal Velocity Search Step Size for Airborne Three-Channel SAR Adaptive Clutter Suppression

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

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