Radar signal feature extraction method based on residual depth learning

A radar signal and deep learning technology, applied in the field of electronic signal detection, can solve the problem of less sorting methods for radar radiation source signals, and achieve the effect of improving adaptability and reducing batches.

Active Publication Date: 2018-08-17
THE 724TH RES INST OF CHINA SHIPBUILDING IND
View PDF4 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there are few radar emitter signal sorting methods based on the deep learning framework.

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 signal feature extraction method based on residual depth learning
  • Radar signal feature extraction method based on residual depth learning
  • Radar signal feature extraction method based on residual depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The technical solutions and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings. The specific implementation steps are as follows:

[0033] (1) The structure of the training residual depth network in the present invention is as follows figure 1 shown. In view of the limited signal in our database and the real-time characteristics of the sorting process, we improved the design of the residual deep network, and greatly reduced the number of parameters trained in the network by using a low-quality decomposition method, avoiding the problem caused by insufficient data. This reduces underfitting and reduces the training time. Network compression process such as figure 2 shown.

[0034] (a) Assume that the network has 3 hidden layers, and the weight of each hidden layer l is recorded as W l (1=1,2,3). Then, put W l Do singular value decomposition, that is, W l =U l S l V l .

[0035] (b) Further...

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 relates to a radar signal feature extraction method based on residual depth learning. With a designed residual depth learning network, deep-level feature extraction is carried out on a radar radiation source signal in a complex electromagnetic environment. The method is implemented as follows: parameters of the residual depth network are trained by using the existing radar data in adatabase; the intercepted data are sent to the input terminal of the residual depth network and results are outputted after mapping of multiple hidden layers, wherein the outputted results are used asthe depth features of the pulse string; with a clustering method, the obtained depth features are clustered, and the correlation degree of each two radiation sources after clustering is calculated and processed according to a correlation criterion; and then the parameter of each radiation source after fusion is calculated and sorting is completed. Therefore, the depth features among data can be dug; the sorting precision is high; and the radar signal feature extraction method can be applied to fields of target reconnaissance and interference source localization.

Description

technical field [0001] The invention belongs to the technical field of electronic signal detection, in particular to a radar signal feature extraction method based on residual deep learning. Background technique [0002] The feature extraction of complex electromagnetic signals is the basis for in-depth analysis of the signal, and its main purpose is to transform the signal and express its essential attributes. Almost all current research on signal analysis includes the link of target feature extraction. Target signal features are divided from the development process, mainly including three types: template (Template) features, model (Model) features and pattern (Pattern) features. Among them, the US SAIP project mainly uses templates and model features. [0003] In order to mine potential information in electromagnetic signal data, remove interference redundancy, and realize dimensionality compression, a very important branch of feature extraction methods is various linear...

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): G06K9/00G06K9/62G01S7/02
CPCG01S7/02G06F18/2321G06F2218/08
Inventor 臧勤程旭严波朱玉
Owner THE 724TH RES INST OF CHINA SHIPBUILDING IND
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap