Feature extraction is one of the key technologies for aerial target attitude estimation. Traditional methods rely on manual feature extraction, which only uses shallow structural features, thus limiting the accuracy of target attitude estimation. In this paper, for the first time, a deep learning method is adopted to conduct research on aerial target attitude estimation based on radar cross section (RCS). Considering the small dimension of RCS data and the complex mapping relationship between RCS and attitude, one-dimensional Cut-ResNet50 with 6 Bottleneck is proposed by pruning ResNet50, which simplifies the model while increasing the generalization performance of the model. The loss function is modified by combining cross-entropy and mean square error to enhance the learning ability of the model. Finally, attitude recognition testing results on simulated RCS dataset and comparison with various models validate the effectiveness of the proposed method.
In the design of distributed MIMO-SAR systems, the optimization of the satellite spacing along track is an important prerequisite for achieving the task of high resolution and wide swath. From the perspective of system performance optimization, this paper takes the along-track distance between adjacent satellites and PRF is used as an optimization variable, while considering the change of the vertical track baseline, combining genetic algorithm and nonlinear programming to obtain a global optimal solution. A method of optimizing the satellite spacing along track based on improved genetic algorithm is proposed and verified by simulation the correctness of the optimization method. The indicators and methods given in this article are of great significance for guiding the structural design of formation satellite systems and improving the performance of high resolution and wide swath measurement of SAR along track.
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