Radar target constant false alarm rate detection method based on clutter knowledge
A technology for constant false alarm detection and radar target, which is applied in radio wave measurement systems, instruments, etc., and can solve problems such as the performance degradation of radar target detection.
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[0026] Step 1: Obtain clutter background knowledge
[0027] according to figure 1 As shown, firstly, the images containing dynamic information and static information are preprocessed respectively, and used as the input of the VGG-16 network, and deep learning is carried out through the convolutional layer, pooling layer and fully connected layer of the model to complete the dynamic and static environment. information for feature extraction. Then, Bi-LSTM is used to fuse the obtained deep features of the two types of information to obtain comprehensive features. The fused feature vector is used as the input node of the classification layer, and the classification of clutter is completed through the softmax classifier. This shows that the clutter background can be mapped to several specific clutter distribution models, which can provide prior knowledge of clutter for radar CFAR detection.
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