Multi-type precession warhead parameter estimation method based on double-layer double-channel convolutional neural network
A convolutional neural network and parameter estimation technology, which is applied in the field of multi-type precession warhead parameter estimation, can solve problems such as poor universality
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[0019] The present invention will be described in further detail below in conjunction with the accompanying drawings.
[0020] to combine figure 1 , the present invention is based on the ballistic missile structural parameter estimation method that centroid height parameter eliminates, comprises the following steps:
[0021] Step 1. Send a single frequency pulse to a warhead of a type with different sizes under one viewing angle, and obtain the main polarization echo data of the same type of warhead with different sizes under this viewing angle;
[0022] Step 2, launch a single frequency pulse from another angle of view, and obtain the main polarization echo data of warheads of the same type and different sizes under this angle of view;
[0023] Step 3. Repeat steps 1 and 2 for another type of warhead until all types of warheads have main polarization echoes from two viewing angles;
[0024] Step 4, performing time-frequency transformation on the obtained echo data to obtain...
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