Beam scheduling is necessary for a multifunction radar(MFR) to perform multiple tasks and manage the resources of the radar efficiently. In this paper, we introduce the concept of the job shop problem to solve the beam-scheduling problem, and apply heuristic algorithms to enable reasonable beam allocation by the MFR. Then, we select three indicators, frame time, total weighed tardiness, and the number of targets dropped out, by considering realistic operation of the MFR. Based on the simulation results, we analyze the scheduling of each algorithm according to the number of targets and compare the performance of the algorithms.
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.
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