The beam scanning range of frequency diversity arc array (FDAA) has all-round advantages. When it is equivalent to a linear array, it exhibits the characteristics of "The middle spacing is large, and the spacing between the two sides is gradually reduced", and there is an inverse density weighting phenomenon, which will lead to a high sidelobe of the FDAA beam. In order to further reduce the influence of sidelobe level and inverse density weighting, the amplitude weighting is carried out on the basis of the nonlinear frequency offset of the array element, but the amplitude weighting is realized by the attenuator in each channel, which will lead to the decrease of the antenna gain, which is generally used when the radar receives the signal. For the transmitter of antenna radar, this paper proposes a phase weighting method for nonlinear frequency offset. The effectiveness of this method for sidelobe suppression is proved by simulation.
Object detection in remote sensing images is a challenging task in field of computer vision since detection performance is negatively influenced by complicated background and various object size. However, most studies have only focused on object appearance, with only a few taking into account scene information, which is closely related to existence and category of objects. In this paper, we put forward a new method by integrating scene information into detection with aim of generating more powerful feature. Specifically, we made use of GRU cell, a special kind of RNN, in order to enhance object feature. The proposed method was verified through experiments on a challenging dataset, i.e., DOTA. Compared to the baseline model RoI-Transformer, the proposed method has achieved around 2.7% improvement in terms of mAP, which is initial attempt to integrate scene information into object detection.
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