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High-frequency ground wave radar clutter classification method based on U-Net network

A high-frequency ground wave radar and classification method technology, applied in neural learning methods, biological neural network models, image analysis, etc., can solve the problem of low accuracy, reduce the probability of false alarms, and improve detection performance.

Active Publication Date: 2021-10-15
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the accuracy rate of the existing classification method for high-frequency ground-wave radar clutter is not high, and now a kind of high-frequency ground-wave radar clutter classification method based on U-Net network is proposed

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  • High-frequency ground wave radar clutter classification method based on U-Net network
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  • High-frequency ground wave radar clutter classification method based on U-Net network

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specific Embodiment approach 1

[0021] Specific implementation mode one: combine Figure 1-7 Specifically illustrate the present embodiment, a kind of high-frequency ground wave radar clutter classification method based on U-Net network in the present embodiment, comprises the following steps:

[0022] Step 1. Obtain the range Doppler spectrum of the high-frequency ground wave radar echo (such as figure 2 shown), the range Doppler spectrum is preprocessed to obtain the ionospheric clutter enhanced spectrum and the first-order sea clutter enhanced spectrum; the ionospheric clutter enhanced spectrum is used as training set 1, and the first-order sea clutter enhanced spectrum The spectrum is used as the training set 2;

[0023] Step 2. Use the training set 1 to train the ionospheric clutter recognition network to obtain the trained ionospheric clutter recognition network, such as Figure 5 As shown, due to the uneven number of samples of clutter and noise, the present invention changes the loss function in t...

specific Embodiment approach 2

[0029] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in the step 1, data preprocessing is performed on the range Doppler spectrum to obtain the ionospheric clutter enhanced spectrum and the first-order sea clutter enhanced spectrum; The specific process includes:

[0030] Step 11, performing logarithmic processing on the data in the range Doppler spectrum to obtain the logarithmically processed range Doppler spectrum I;

[0031] Step 12: Use a bilateral filter to filter I to suppress background noise and target interference, thereby obtaining the ionospheric clutter enhanced spectrum I 1 , by using the bilateral filter to filter the range Doppler spectrum, it can not only reduce the interference of the target on clutter identification, but also separate the sea clutter and ionospheric clutter to avoid the mutual influence between the two in the identification process;

[0032] Step 13, calculate the difference between the range Doppler spectrum...

specific Embodiment approach 3

[0037] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that in the step one or two, the bilateral filter is used to filter I to obtain the ionospheric clutter enhanced spectrum I 1 , the specific process includes:

[0038]

[0039] Among them, i represents the distance dimension coordinate; j represents the Doppler dimension coordinate; k represents the distance dimension coordinate of the adjacent unit; l represents the Doppler dimension coordinate of the adjacent unit; (k,l) represents the adjacent unit of (i,j) Position coordinates; I(k,l) represents the magnitude of the distance Doppler I at the position (k,l) after coordinate logarithm processing; the weight coefficient w(i,j,k,l) ​​is defined by the domain The product of the kernel d(i,j,k,l) ​​and the range kernel r(i,j,k,l) ​​is determined, namely:

[0040] w(i,j,k,l)=d(i,j,k,l)×r(i,j,k,l).

[0041] Other steps and parameters are the same as those in Embodime...

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Abstract

The invention discloses a high-frequency ground wave radar clutter classification method based on a U-Net network, and belongs to the field of radar clutter classification. The high-frequency ground wave radar clutter classification method solves the problem that an existing high-frequency ground wave radar clutter classification method is not high in accuracy. The method comprises the following steps: acquiring a distance Doppler spectrum of a high-frequency ground wave radar echo; preprocessing the distance Doppler spectrum to obtain an ionized layer clutter enhancement spectrum and a first-order sea clutter enhancement spectrum; training the ionosphere clutter recognition network and the first-order sea clutter recognition network by using the training set to obtain a trained ionosphere clutter recognition network and a trained first-order sea clutter recognition network; inputting high-frequency ground wave radar clutters to be classified into the trained ionized layer clutter recognition network and the first-order sea clutter recognition network at the same time, and obtaining two clutter recognition results; and combining the ionized layer clutter recognition result and the first-order sea clutter recognition result to obtain a clutter classification result of the high-frequency ground wave radar echo distance Doppler spectrum. The invention is used for high-frequency ground wave radar clutter classification.

Description

technical field [0001] The invention relates to a method for classifying high-frequency ground wave radar clutter based on a U-Net network, and belongs to the technical field of radar clutter classification. Background technique [0002] Radar is a tool for target detection using radio, which is widely used in military and civilian applications. The clutter classification technology is a background perception technology, which can adaptively classify the echo data acquired by the radar through information such as the shape and statistical characteristics of the clutter. Through the clutter classification technology, the necessary parameters of the clutter can be extracted by using the classification results, and useful prior information can also be provided for subsequent target detection and tracking. [0003] The detection environment of high-frequency ground wave radar is very complex, including not only the "self-interference" generated by radar work such as sea clutter...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06T7/194
CPCG06T7/194G06N3/08G06T2207/10044G06N3/045G06F2218/12G06F18/214
Inventor 李杨王新旸张宁
Owner HARBIN INST OF TECH
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