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A radiation source individual identification method based on multi-domain feature fusion

A feature fusion and identification method technology, applied in the field of information detection and identification, can solve the problems of low individual identification rate of radiation sources, incomplete feature information, weak generalization ability of classifiers, etc., achieve good individual identification effect of radiation sources, improve The effect of generalization ability

Active Publication Date: 2022-04-12
HARBIN ENG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a multi-domain feature-based method that solves the problems of incomplete feature information, weak generalization ability of the classifier, strong subjectivity of expert analysis, and low identification rate of radiation source individuals in the existing radiation source individual identification method. Fusion individual radiation source identification method

Method used

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  • A radiation source individual identification method based on multi-domain feature fusion
  • A radiation source individual identification method based on multi-domain feature fusion
  • A radiation source individual identification method based on multi-domain feature fusion

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Experimental program
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Embodiment 1

[0048] A radiation source individual identification method based on multi-domain feature fusion, comprising the following steps:

[0049] Step 1: Input a signal set containing multiple different radar radiation source transmitters, and perform discrete processing on each signal in the signal set to obtain the original data set; divide the original data set randomly into cyclic neural network signal data set and A dataset of transmitter signals from known radiation sources;

[0050] Step 2: use the cyclic neural network signal data set to train the cyclic neural network to obtain the cyclic neural network feature extractor;

[0051] Step 3: Select a signal data x from the known radiation source transmitter signal data set i (n), put x i (n) Input to the cyclic neural network feature extractor to obtain the time-domain cyclic feature F i1 ; Wherein, i=1,2,...,R, R is the number of data in the known radiation source transmitter signal data set; n is the signal data x i The nu...

Embodiment 2

[0076] Obtain signal data x in step 4 i The time-frequency domain phase characteristic F of (n) i2 The specific method is:

[0077] Step 4.1: For signal data x i (n) Perform Cui-Williams time-frequency distribution calculation to obtain CWD(n,ω); perform time-frequency domain processing on CWD(n,ω) to obtain the phase absolute value result Φ(n,ω);

[0078]

[0079]

[0080]

[0081] Among them, * represents the conjugate operation; σ is the scaling factor; ω is the angular frequency; γ is the time shift variable; μ is the time variable; A three-dimensional phase image is obtained. The value of the angular frequency ω in Φ(n,ω) is continuous, take a positive integer and write it as The value of n is already a positive integer, and discrete phase values ​​can be obtained 2D phase image Fig for discrete 3D phase images 1 Obtain. Define a two-dimensional image, the position of any pixel in the image is defined by Indicates that the pixel is located in the nth r...

Embodiment 3

[0086] The pre-trained convolutional neural network model in described step 4.3 is the VGG-16 network model based on ImageNet image data set pre-training; Described VGG-16 network model contains input layer, hidden layer and output layer, wherein hidden layer Containing 13 convolutional layers and 3 fully connected layers, the output layer of the VGG-16 network model is the third fully connected layer.

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Abstract

The invention belongs to the technical field of information detection and identification, and in particular relates to a radiation source individual identification method based on multi-domain feature fusion. The present invention aims at problems such as incomplete feature information, weak generalization ability of classifiers, strong subjectivity of expert analysis and low identification rate of radiation source individuals in existing radiation source individual identification methods. Multi-domain features such as step domain, and design a multi-layer neural network model as a classifier to realize individual radiation source identification. The fusion of multi-domain features solves the problem of incomplete feature information, and the neural network model is used for identification and classification, which avoids the problems of relying on prior information such as the modulation mode of radiation source signals and the strong subjectivity of expert systems when identifying. At the same time, the neural network model improves the generalization ability of the system and obtains a better individual identification effect of radiation sources.

Description

technical field [0001] The invention belongs to the technical field of information detection and identification, and in particular relates to a radiation source individual identification method based on multi-domain feature fusion. Background technique [0002] As one of the important identification means, individual radiation source identification plays an irreplaceable role in battlefield target identification. Individual radiation source identification is to compare the characteristic parameters of the radiation source signal intercepted by the passive reconnaissance receiver with the radiation source characteristic parameter database, so as to confirm the identity of the radiation source and provide information support for subsequent mission execution and actions. The key steps of individual radiation source identification are feature parameter extraction and classifier design. The traditional feature extraction is based on the intentional pulse-to-pulse modulation char...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01S7/02
CPCG06N3/084G01S7/021G06N3/045G06F2218/08G06F2218/12G06F18/23213G06F18/2415G06F18/253
Inventor 高敬鹏王旭项建弘王甫高路白锦良秦鹏王上月
Owner HARBIN ENG UNIV
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