NDLI: Recognition performance analysis of instantaneous phase and its transformed features for radar emitter identification

Content Provider	IET Digital Library
Author	Ru, Xiao Hu Liu, Zheng Jiang, Wen Li Huang, Zhi Tao
Abstract	Instantaneous phase (IP) feature is a time-varying curve describing the unintentional modulation on the phase. IP is a basic feature for identifying different radar emitters, and mostly other features can always be obtained from its transformations. Thus an important issue is to choose a suitable transformation of IP which can achieve the best recognition performance, more specifically, the highest recognition ratio. Heretofore, the recognition ratios of transformed IP features were derived basically through experiments, lacking theoretical calculation and analysis. In this study, the authors first extract the IP feature from a given radar pulse, and then propose a unified model for various transformations of IP. After that, they calculate the recognition ratio theoretically based on this model. Subsequently, they prove that any linear transformation, such as derivative, smoothing, and linear dimensionality reduction cannot improve the recognition performance compared to the original IP. They also find the essentially mathematical reasons of the seemingly unimaginable results. Finally, they validate the analysis results through both computer simulations and experiments on measured data collected from 120 radar emitters in the real environment. This work provides a theoretical basis for feature extraction and feature assessment in radar emitter identification.
Starting Page	945
Ending Page	952
Page Count	8
ISSN	17518784
Volume Number	10
e-ISSN	17518792
Issue Number	Issue 5, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2014.0512
Journal	IET Radar, Sonar & Navigation
Publisher Date	2016-06-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Feature Extraction Instantaneous Phase Linear Transformation Radar Emitter Identification Radar Pulse Radar Signal Processing Radar Target Recognition Radar Theory Recognition Performance Analysis Signal Processing And Detection Time-varying Curve Transformed Feature
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Drone detection

Convolutional neural network applied to specific emitter identification based on pulse waveform images

Analysis of phase noise influence on micro-Doppler feature extraction of vibrating target

Target separation detection and motion parameter estimation method based on time-varying autoregressive model

Kernel canonical correlation analysis for specific radar emitter identification

Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion

Automatic Radar Waveform Recognition

Evaluation of unintentional modulation for pulse compression signals based on spectrum asymmetry

Coning target micro-motion feature extraction via scattering centre reconstruction

Recognition performance analysis of instantaneous phase and its transformed features for radar emitter identification

Similar Documents

Drone detection

Convolutional neural network applied to specific emitter identification based on pulse waveform images

Analysis of phase noise influence on micro-Doppler feature extraction of vibrating target

Target separation detection and motion parameter estimation method based on time-varying autoregressive model

Kernel canonical correlation analysis for specific radar emitter identification

Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion

Automatic Radar Waveform Recognition

Evaluation of unintentional modulation for pulse compression signals based on spectrum asymmetry

Coning target micro-motion feature extraction via scattering centre reconstruction

Recognition performance analysis of instantaneous phase and its transformed features for radar emitter identification