Radar signal detection method based on convolutional neural network

A convolutional neural network and radar signal technology, applied in the field of radar signal processing, can solve the problems of constant false alarm detector detection performance degradation, background clutter change, etc.

Active Publication Date: 2020-09-29
SHANGHAI SPACEFLIGHT ELECTRONICS & COMM EQUIP RES INST
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Problems solved by technology

The background clutter in the actual measurement is variable and there may be a strong correlation between them, which makes the detection performance of the constant false alarm detector decrease

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  • Radar signal detection method based on convolutional neural network
  • Radar signal detection method based on convolutional neural network
  • Radar signal detection method based on convolutional neural network

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[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described and discussed below in conjunction with the accompanying drawings of the present invention. Obviously, what is described here is only a part of the examples of the present invention, not all examples. Based on the present invention All other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0040] Such as figure 1 As shown, an embodiment of the present invention provides a radar signal detection method based on a convolutional neural network, the method comprising the following steps:

[0041] Step 1: Construct radar echo data under different clutter backgrounds, perform matched filtering and pulse Doppler processing, obtain distance-Doppler spectrum, form a signal detection sample database, and then make tags with and without targets corresponding to the samples . ...

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Abstract

The invention discloses a radar signal detection method based on a convolutional neural network. According to the method, a distance-Doppler frequency spectrum obtained after pulse Doppler processingis used as an image sample, different characteristics of a time-frequency spectrum with or without a target are taken as identification characteristics, radar signal detection is realized by utilizingthe classification capability of a convolutional neural network, existence of a target signal is judged, the convolutional neural network is applied to radar signal processing, and signal detection is realized from the perspective of image processing. Compared with a traditional CFAR detector, the method of the inventiom has the advantages that the detection probability is improved, the influencecaused by the correlation of background clutters when the CFAR detector is used is avoided, and the limitation that the fixed CFAR detector is suitable for a specific environment is improved.

Description

technical field [0001] The invention relates to the technical field of radar signal processing, in particular to a radar signal detection method based on a convolutional neural network, which can be used to replace a constant false alarm detector to realize signal detection. Background technique [0002] In radar signal processing, a constant false alarm (CFAR) detector is generally used for signal detection, that is, a method of setting a threshold according to the clutter background under a constant false alarm probability to achieve the maximum detection probability. Nowadays, with the complex and changeable battlefield environment, the clutter background faced by radar detection is becoming more and more complex. The constant false alarm detector usually has the best detection performance in a specific detection background, and it is established under the assumption that each reference unit obeys independent and identical distribution. The background clutter in actual m...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41
CPCG01S7/417Y02A90/10
Inventor 赵晨帆王萍叶舟邢冠培傅虹景徐嘉辉
Owner SHANGHAI SPACEFLIGHT ELECTRONICS & COMM EQUIP RES INST
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