A Robust Recognition Method of Radar Target hrrp in Noise Environment
A radar target and recognition method technology, which is applied in the field of radar target HRRP robust recognition, can solve the problems of test sample pollution, recognition rate reduction, etc., and achieve good recognition performance, reduce recognition interference, and high recognition rate.
Active Publication Date: 2020-09-29
NAT UNIV OF DEFENSE TECH
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[0005] The technical problem to be solved by the present invention is: Aiming at the problem that the test sample is polluted by noise, resulting in a greatly reduced recognition rate, a radar target HRRP robust recognition method in a noisy environment is proposed to reduce the interference of noise on recognition to obtain a stable Noise Resistant Classification Model
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[0027] In order to better illustrate the technical solution of the present invention, the implementation of the present invention will be further described below in conjunction with examples, so as to have a deeper understanding of how to apply the technical means of the present invention to solve problems and achieve the purpose of better solving practical problems. The examples given are only for explaining the present invention, not for limiting the scope of the present invention. figure 1 Be the radar target HRRP robust identification method under a kind of noise environment of the present invention, specifically comprise the following steps:
[0028] Step 1: Obtain the high-resolution range profile (HRRP) of the radar target through the high-resolution broadband radar and perform power normalization, and divide the HRRP data into a training sample set and a test sample set, where the training samples are N for each category 1 , each category of test samples N 2 , where e...
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The invention belongs to the field of radar target recognition, and discloses a radar target HRRP robust recognition method in a noisy environment. are the training sample set and the test sample set. Add noise with different signal-to-noise ratios to some training samples of each type of target, and keep the remaining training samples unchanged, and then normalize the entire training sample set of all target types. The obtained training samples are used to train a convolutional neural network that combines a residual block, an inception structure, and a noise-reducing self-encoding layer proposed by the present invention to obtain a trained convolutional neural network. Test samples under different signal-to-noise ratio conditions polluted by noise are used to obtain the recognition results. The invention can effectively reduce the interference of noise to recognition, utilize deep learning to obtain a stable anti-noise recognition model, and use this method to realize robust radar target recognition under a wide range of signal-to-noise ratio conditions.
Description
technical field [0001] The invention relates to the field of radar target recognition, and more specifically relates to a radar target HRRP robust recognition method in a noise environment. Background technique [0002] Traditional radar target recognition technology relies on artificially designed features, and the integrity and effectiveness of these features are often lacking strong guarantees, and the complex electromagnetic environment will interfere with the recognition results, leading to extreme challenges in the accuracy and robustness of traditional radar target recognition technology. big challenge. Deep learning technology can automatically extract the essential features of the target. This end-to-end learning method greatly improves the accuracy and robustness of target recognition. [0003] The application of deep learning to radar target recognition is generally divided into training phase and testing phase. At present, the training sample set is usually obt...
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IPC IPC(8): G01S7/41G01S13/89G06K9/00G06K9/62
CPCG01S7/411G01S7/417G01S13/89G06F2218/12G06F18/214G06F18/24
Inventor 杨威黎湘刘永祥张文鹏沈亲沐李玮杰
Owner NAT UNIV OF DEFENSE TECH