Synthetic aperture radar (SAR) systems, which operate under a slant-viewing geometry, inevitably entail shadow regions in the resulting radar image. Such shadow profiles contain backprojected signatures of an object’s configuration as with target profiles; however, they are rarely utilized in current SAR-based recognition techniques. A major challenge in leveraging shadow information together lies in the intrinsic limitation of current single-pathway approaches, in which the target and shadow cannot be addressed simultaneously because of their incompatible domain properties. Hence, we herein propose novel solutions that enable the successful fusion of target and shadow regions within SAR for the first time. First, we devise new image preprocessing techniques specifically customized for shadows to compensate for their unique domain characteristics, which are distinct from the target. Second, we introduce a parallelized SAR processing mechanism such that a network can independently extract features oriented toward each conflicting modality. Third, adaptive fusion strategies are proposed for the optimal integration of features from each region while considering their relative significance layer by layer. Extensive experiments on public benchmark datasets demonstrate that the proposed framework allows a network to effectively employ shadow signatures and targets, thereby outperforming previous methods significantly for all setups.

This study developed a radar resource management (RRM) framework for maritime multifunctional radar (MFR) that performs surveillance and tracking of unknown targets. The proposed scheme consists of two stages: target prioritization based on fuzzy logic and a beam scheduling approach that transmits surveillance beam and tracking beams for each target appropriately. The proposed beam scheduling scheme also takes into account the imperfect detection of targets in real situation. A conventional heuristic beam scheduling approach was modified to incorporate binary integration in radar theory into the RRM strategy. The resulting scheme efficiently handles the complicated beam scheduling problem by considering the target priority as well as the imperfect detection of targets of a real maritime MFR. Simulation results based on a realistic maritime environment demonstrated that the proposed scheme not only determines the correct order of transmission beams according to the target priority but also maintains good tracking accuracy for the threatening target.

Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their performance depends on the characteristics of the images, and both efficiency and performance deteriorate when the features are used indiscriminately. In this study, we propose a two-stage detection framework to ensure efficient and superior detection performance in TSX images, using previously studied features. The proposed method consists of two stages. The first stage uses simple features to eliminate misdetections. Next, the discrimination performance for the target and clutter of each feature is evaluated and those features suitable for the image are selected. In addition, the Karhunen–Loève (KL) transform reduces the redundancy of the selected features and maximizes discrimination performance. By applying the proposed method to actual TerraSAR-X (TSX) images, the majority of the identified clusters of false detections were excluded, and the target of interest could be distinguished.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)