The main problem with the current radar target radial length extraction algorithm is its susceptibility to interference signals, which makes it difficult to optimize the boundaries of the target support area, especially the impact of noise at positions that tend to be farther away from the target support. Therefore, based on deep learning networks, this article trains and analyzes different pixel points, completes the segmentation of the target support area and background area through image semantic segmentation algorithms, obtains the target support area, and estimates the radial length of the target based on the boundary of the target support area. Finally, validate the effectiveness of the algorithm using simulation data.
The inversion of the micro motion parameters of the spatial cone target is of great significance for the detection and recognition of the spatial target. In this paper, the inversion of micro motion parameters of cone target is studied, Convolutional Neural Networks(CNN) training for time-frequency image is proposed. Different precession angles of target are classified as different categories. According to the classification results, the precession angle parameters of target are inversed. The simulation results show that the coiler neural network can invert the precession angle of the micro-motion target and has a good inversion effect.
Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).
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