Supercharge Your Innovation With Domain-Expert AI Agents!

A method for identifying types of satellite navigation interference based on svm multi-classification algorithm

An interference type and satellite navigation technology, applied in the field of satellite navigation interference type identification, can solve problems such as difficulty, efficiency, and high accuracy of detection results, and achieve the effects of maintaining normal operation, enhancing classification accuracy, and improving detection efficiency

Active Publication Date: 2021-08-24
SOUTHEAST UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Among them, the energy detection method is the most widely used. Although this method is highly feasible, the setting of the detection threshold has a high impact on the accuracy of the detection results. Different threshold settings will lead to changes in the detection results. Find the threshold that best meets the interference classification. will be difficult and less efficient

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
  • A method for identifying types of satellite navigation interference based on svm multi-classification algorithm
  • A method for identifying types of satellite navigation interference based on svm multi-classification algorithm
  • A method for identifying types of satellite navigation interference based on svm multi-classification algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] This embodiment discloses a satellite navigation interference type identification method based on the SVM multi-classification algorithm, such as figure 1 shown, including:

[0046] Step 1. Calculate the eigenvectors of the satellite navigation signals of six types of interference signals: non-interference, single-frequency interference, chirp interference, BPSK interference, frequency-sweep interference and partial-band interference signals, respectively, and assign the labels corresponding to the six types of eigenvectors to The assignments are 1, 2, 3, 4, 5 and 6, and the eigenvectors and labels are combined to form a sample matrix; wherein, the eigenvectors include signal power, estimated pulse width, estimated frequency modulation slope, and bandwidth ratio before and after the square of the signal.

[0047] This step specifically includes:

[0048] (1.1) Down-convert the 6 types of interference satellite navigation signals into intermediate frequency signals. (1...

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 satellite navigation interference type identification method based on an SVM multi-classification algorithm. The SVM multi-classification algorithm is to find a hyperplane to classify the samples through given samples with different characteristics, and further apply the classification model to new samples of unknown sample categories, and the sample types are three types or above. In this process, the received satellite navigation signal is first filtered to extract the quantity that can reflect the characteristics of the interference signal, and the corresponding interference type is marked. Then the samples are input into the SVM multi-classifier for learning, and the optimal classifier is obtained. When a new interference signal feature vector enters the classifier, it will be automatically classified. The method can automatically identify multiple types of interference, and improves the efficiency and accuracy of interference identification.

Description

technical field [0001] The invention relates to wireless communication technology, in particular to a satellite navigation interference type identification method based on SVM multi-classification algorithm. Background technique [0002] With the increasingly complex electromagnetic environment, the normal operation of the satellite navigation system is seriously threatened, and the comprehensive monitoring of the satellite navigation system is particularly important. The electromagnetic interference environment refers to various electromagnetic interference signals that cause the performance degradation of the satellite navigation system. Since the satellite signal transmission power is relatively small, and the signal is transmitted over a long distance, the signal strength received by the ground is even smaller. Therefore, it is easy to be affected by external electromagnetic interference. Signal interference, thus affecting the positioning accuracy. [0003] In the prac...

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 Patents(China)
IPC IPC(8): G06K9/00
CPCG06F2218/08G06F2218/12
Inventor 祝雪芬林梦颖陈熙源汤新华
Owner SOUTHEAST UNIV
Features
  • R&D
  • 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