Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Radar radiation source signal identification method based on SACNN

A signal identification and radiation source technology, applied to radio wave measurement systems, instruments, etc., can solve problems such as time-consuming, low recognition accuracy, and poor real-time performance, and achieve improved recognition accuracy, sufficient feature extraction, and improved training speed Effect

Active Publication Date: 2021-07-23
PLA AIR FORCE AVIATION UNIVERSITY
View PDF5 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention aims at the low recognition accuracy of radar radiation source signals in the prior art under the condition of low signal-to-noise ratio and the existing method for recognizing radar radiation source signals based on two-dimensional time-frequency images requires time-frequency conversion, which consumes a lot of time and is real-time Poor problem, propose a radar emitter signal recognition method based on SACNN

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Radar radiation source signal identification method based on SACNN
  • Radar radiation source signal identification method based on SACNN
  • Radar radiation source signal identification method based on SACNN

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0077] A radar emitter signal recognition method based on SRNN+Attention+CNN, which includes:

[0078] 1) Build a dataset

[0079] Sampling the radar emitter signal detected by the reconnaissance equipment and intercepting a fixed length as data and labeling;

[0080] Using Matlab to generate a data set of eight different modulation types including binary coded signal, linear frequency modulation continuous wave signal, Costas signal, Frank signal and polyphase code P1~P4. The signal parameters are shown in Table 1 below:

[0081]

[0082] The signal-to-noise ratio of each modulation type is {-20dB, -18dB, -16dB, -14dB, -12dB, -10dB, -8dB, -6dB, -4dB, -2dB, 0dB, 2dB, 4dB, 6dB, 8dB, 10dB} respectively generate 2000 sample signals, that is, a total of 32000 samples for each modulation signal, and a total of 256000 samples for eight different modulation types. The number of sampling points for each sample is 1024.

[0083] Create a training set, a validation set, and a tes...

Embodiment 2

[0139] Effect of the present invention is described further below in conjunction with simulation experiment:

[0140] 1. Simulation conditions

[0141] The hardware platform and software platform used in the simulation experiment of the present invention are shown in Table 2 below.

[0142]

[0143] 2. Simulation experiment and result analysis

[0144] The radar radiation source identification simulation experiment of the present invention adopts the SRNN+Attention+CNN method proposed by the present invention to identify the modulation type of each radar radiation source signal, and counts the correctly identified samples of eight modulation type signals under each signal-to-noise ratio total to obtain the accurate recognition rate. The recognition accuracy of SRNN+Attention+CNN model for 8 kinds of signals under different signal-to-noise ratio conditions is shown in the attached Figure 4 shown.

[0145] It can be seen that when the signal-to-noise ratio is greater tha...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a radar radiation source signal identification method based on SACNN. The method comprises the following steps: 1) constructing a data set; 2) preprocessing data; 3) constructing an SRNN local feature extraction module; (4) constructing an Attention module; 5) constructing a CNN global feature extraction module; 6) training a radar radiation source identification network; and 7) identifying the radar radiation source signal. The network structure constructed by the invention is directly trained by adopting the one-dimensional time domain radar radiation source signal, and the features of the one-dimensional time domain radar radiation source signal can be directly extracted and identified; the problems that an existing radar radiation source recognition method is low in radar radiation source signal recognition accuracy under the low signal-to-noise ratio condition, and an existing radar radiation source signal recognition method based on a two-dimensional time-frequency image needs time-frequency transformation, consumes much time and is poor in real-time performance are solved.

Description

technical field [0001] The invention relates to the technical field of electronic countermeasures, in particular to a radar radiation source signal identification method based on SACNN. Background technique [0002] Radar Emitter Identification (REI) is to analyze and process the intercepted enemy radar signal, obtain the working parameters and signal characteristic parameters of the enemy radar, and judge the radar model, working mode, Position, and then master its combat platform, working status, threat level and other information, and provide intelligence support for battlefield electromagnetic situation awareness, threat warning, and combat plan formulation. With the increasing complexity of the battlefield electromagnetic environment, the traditional identification method based on Pulse Description Words (PDW) parameters can no longer meet the requirements of radar radiation source signal identification under the condition of low signal-to-noise ratio. The appearance o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/02
CPCG01S7/021Y02A90/10
Inventor 王晓峰王春雨高诗飏
Owner PLA AIR FORCE AVIATION UNIVERSITY
Features
  • Generate Ideas
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com