Radar Emitter Identification (REI)Radar Symposium 2014, 9th & 10th December 2014 at the KACST Headquarters,

Riyadh, Saudi Arabia

Dr. Hazza Alharbi

Royal Saudi Air Defense Forces

EW department

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Outline

• Definition / methods of REI• Importance of REI.• Type of radar signals.• ELINT systems.• Identification Categories.• Challenges to REI.• Methods of REI.• REI techniques. • Simulation example.• Proposed model for successful design.• Summary and conclusions.• References.

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REI Definition

REI: Identifying radar waveform, function, or application by analyzingintercepted signal.

* Stimson’s “Introduction to Airborne Radar”, Third Edition

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Importance of REI

Military use:• Fast signals survey.• Selecting suitable jammer.• Fast decision in a specific tactical situation.• Uncertainties of enemy threat signal in battlefield (wartime modes)• Distinguish between enemy radars and friendly radars in dense and complex

environments. • Analyze large number of recorded data. Spectrum management: restricted device – Spectrum allocations.Cognitive radars / radio : awareness of surrounding area, adapting, meet user requirements.

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Radar Signal Characterization

• Pulsed radar / Continuous radar.

• PRI modulation.

• Pulse compression:

[8] Jiandong et al , “ Automatic Recognition of Radar Signal Based on Time-Frequency Image Shape Character ,” Defence Science Journal, Vol. 63, No. 3, pp. 308-314, May 2013.

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Ideal ELINT Systems

• High probability of detection.

• Optimum design between detection measurements.

• Recording for further analysis.

• Recognition of threat, waveform recognition, and/or application

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Identification Categories / Function

ScanPW ( µ sec)PRF (Hz)Carrier (F)Radar

Circular (Slow)≤ 1≤ 400D or

lower

Early Warning

Circular – Sector -

helical0.75 –

2.5

350-1000D,E,FMedium Range

Acquisition

Lobe switching –

helical (fast)≤ 1800≤E,F,I,J,KShort range

acquisition

Lob switching –

conical ≤ 11000≤E,FFire control

circular – sector-

spiral-raster≤ 1800 ≤D,E,F,I,JSearch

(airborne)

Circular - monopulse≤ 11000≤I,JFire control

(airborne)

Circular - sector≤ 11000≤I,JBattlefield

surveillance

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http://www.rfcafe.com/references/electrical/ew-radar-handbook/receiver-types-characteristics.htm

Identification Categories

• Tracking radars, proximity fuses in missiles, and lock-on use CW illuminators.

• TWS radars needs higher SR.

• High RF band needs small RF equipment, and usually used by aircrafts.

Threat mode

Tracking Scanning Lock-on TWS

Radar Applications

Weather Command and

Control Fire control Flight control SAR OTH

Radar waveform Pulsed / CW Pulse

modulation

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Challenges

• Crowded RF environments: parameters sorting becomes difficult. • Non-ideal conditions (Non-AWGN, CFO, etc. )• Identifying radar from a single pulse (short observation time).• Interferences.• Complex radar signals characteristics ( Pulse compression )• Agility of radar features: PRI, freq. , Scanning, • Optimization between accuracy and processing time..• Reliable design needs a real data collection (Cost)

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REI Methods

Offline:o Depends on operators skills.o Needs a long time compared to online method.o Availability of trained operators (retirements, .)o Needs background in signal processing.

Automated:o Fast / online. o No need for operators.o Performance is subject to (SNR, channel, etc).o Supports real time EW applications * Stimson’s “Introduction to

Airborne Radar”, Third Edition

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Offline method • Data: recorder data or offline application.

• Tabular, panoramic display, geographical display.

• Tools: specialized software, measurement equipment.

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http://www.effectivebits.net/2011/08/radar-analysis-with-tektronix-mdo4104-6.html

Automated method

o Work with Active signal / recorded to analyze high number of recorded data.

o Needs preliminary designed algorithms.

Data set

Theoretical Background Designing Automatic

Analysis AlgorithmDatabase

Candidate threat

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Requirements

Automated REIStep 1: Inter-pulse Analysis De-interleaving (traditional method) Grouping PDW: (TOA, EF, PA,PW, and

DOA)

Other parameters are calculated such as:• PRI from TOA• Scan Period and antenna beam from (PA

and TOA).

Different files for each emitter. Pattern Recognition method can be

applied with better performance.

Step 2:

waveform recognition ( Inter-pulse recognition)

Preprocessing

Pattern Recognition

Features extraction

• Noise reduction.• Remove redundant data.• Smoothing.

• Efficient features• Robust against channel

variations • Low complex for on-line

applications

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Waveform recognition/ waveform features

• Temporal Time Domain features (Instantaneous features): Sensitive to noise level.• Relevant image features:Time frequency image (Histogram).Ambiguity Function (Ref [ 9])• higher order spectral Analysis: (HOM, HOC): Minimize effect of AWGN noise.Sensitive to spiky/heavy tailed noise.Cyclostationarity: robust against CFO, CPO, or TO,

• Transformation based features.

Fourier: extract spectral features.Wavelet: Solves effect of noise (Time and frequency domain information)

Tim

e-F

req

ue

ncy

im

age

Am

big

uit

y Fu

nct

ion

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[9] Guoyi et al, “Automatic Recognition of Intra-pulse Modulation Type of Radar Signal Based on Ambiguity Function ,” Recent Advances in Computer Science and Information Engineering , Springer, pp. 659–664., Jan. 2012

Waveform recognition/ Classifiers

• Artificial Neural networksFlexible to solve complex problemAdapt and learn itself (un-supervised)more accurate than others but needs big data set compared with others.

http://en.wikipedia.org/wiki/Artificial_neural_network

• Support Vector MachinesSolve over fitting and local minimum in ANNscomplex processing in case of using non- linear kernel function.

http://en.wikipedia.org/wiki/Support_vector_machine

• Predetermined threshold.• Genetic algorithm to select the best features or optimize ( ANN / SVM).

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Intra pulse recognition algorithms

Contributio

n

EmittersClassifierFeatureRe

f.

PRI modulation

type

a) Constant, b)

Stagger, c) Jittered,

d) Sliding, e) Dwell

and Switch, f)

Periodic.

NNPRI histogram, statistics, and pulseinterval.

[1]

3 EmittersNNRF, PRI, PW[2]5 EmittersNNRF, PRI, PW[3]

Computational

complexity

3 Emittersk-mean , SVM, RF (MHz) PRF (Hz) PW (us)[4]

Missing Data

Analysis.11 EmittersNNPRI, PW, RF, modulations, PRI,

PRF, Scanning Period, ScanningType, RF (12 Features)

[5]

3 EmittersModified PDW, Scanning rate[6]

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Waveform recognition algorithms

ClassifiersFeaturesWaveformsRef

.SVM ( Gaussian

radial basis

function (RBF) )

Time-Frequency8 Classes: COSTAS, EQFM,

BPSK, NP,FRANK, SFM,

TLFM, LFM

[8]

Probabilistic NNAmbiguity Function6 Classes: BPSK, QPSK,

FSK, LFM, SFM, NS.

[9]

(MLP) networksStatistics, Transformation,

TTD,

T-F.

LFM, (Costas codes), binary

phase, and Frank, P1, P2,

P3, and P4 polyphase codes.

[10]

FSVM, KNN Statistics, Transformation.LFM, FSK, BPSK, QPSK,

CW

[11]

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Simulation example

Radar waveform identification (CW-Constant envelope, CWFM, and Barker code)

User features (Instantaneous phase ∅𝑝 , Second Order Moment 𝑀20)

Barker code (2PSK,

N=13)

CWFM (Linear chirp)CWFeature

Theoretical value= 1,

Simulation results =

0.9792;

Theoretical value= 0,

Simulation results =

0.0320,

Theoretical value= 1.

Simulation results =

1

Instantan

eous

phase

HOM

𝑀20

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Simulation example

Radar waveform identification (CW, CWFM, and Barker code)

User features (Instantaneous phase ∅𝑝 , Second Order Moment 𝑀20)

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Proposed model for successful design

Required identification: application, waveform , PRI modulation.

Performance evaluations: field test, realistic data, simulation, processing complexity.

ClassifierFeatures selection

Assumption :Signal parameters (CFO, CPO, TO, pulse shaping)

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Problem clarification

Design

Test

Summary and conclusions

• REI algorithm two-steps: (1) Inter-pulse, (2) Intra-pulse analysis. • There are many solutions to design REI. • Extraction of emitter details depends on analysis levels. • Precisely clarify possible threat, channel assumptions, and performance

evaluations to reach optimum solution. • Building real data for the design and/or performance evaluations.

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References

[1] Kauppi, J.-P., & Martikainen, K. (2007). An efficient set of features for pulse repetition interval modulation recognition. In Proceedings of IET international conference on radar systems.[2] Ching-Sung et al, , “A Vector Neural Network for Emitter Identification,” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 50, NO. 8, AUGUST 2002.[3] Liu et al , “Incremental learning approach based on vector neural network for emitter identification,” 10.1049/iet-spr.2008.0240,IET Signal Processing.[4] Z. Yang, Z. Wu, Z. Yin, T. Quan, and H. Sun, “Hybrid Radar Emitter Recognition Based on Rough k-means Classifier and Relevance Vector Machine,” Sensors, vol. 13(1), pp. 848-864, 2013. [5] N. Petrov, I. Jordanov and J. Roe, Radar Emitter Signals Recognition and Classification with Feedforward Networks, Procedia Computer Science 22, pp. 1192-1200, 2013.[6] Z Yin, W Yang, Z Yang, L Zuo, H Gao, A study on radar emitter recognition based on SPDS neural network. Inf. Technol. J. 10(4), 883–888, 2011[7] Anjaneyulu, L. ; Murthy, N.S. ; Sarma, N., "Radar emitter classification using self-organising Neural Network models ", International Conference onMICROWAVE, 2008.[8] Jiandong et al , “ Automatic Recognition of Radar Signal Based on Time-Frequency Image Shape Character ,” Defence Science Journal, Vol. 63, No. 3, pp. 308-314, May 2013. [9] Guoyi et al, “Automatic Recognition of Intra-pulse Modulation Type of Radar Signal Based on Ambiguity Function ,” Recent Advances in Computer Science and Information Engineering , Springer, pp. 659–664., Jan. 2012[10] J. Lund´en and V. Koivunen, “Automatic radar waveform recognition,” IEEE Journal of Selected Topics in Signal Processing, vol. 1, no. 1, pp. 124–136, June 2007.[11] Ren et all , “Radar Signal Feature Extraction Based on Wavelet Ridge and High Order Spectral Analysis,” IET International Radar Conference, 2009

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References

[12] Spanish National Research and Development Program under project TEC2011-28683-C02-01.* V. Iglesias, J. Grajal, P. Royer, M. A. S´anchez, M. L´opez-Vallejo, and O. A. Yeste-Ojeda,“Real-time radar pulse parameter extractor” IEEE radar conference, 2014.* V. Iglesias, J. Grajal, P. Royer, M. A. S´anchez, M. L´opez-Vallejo, and O. A. Yeste-Ojeda, “Real-Time Low-Complexity Automatic Modulatio Classifier for Pulsed Radar Signals,”

submitted to IEEE Transactions on Aerospace and Electronic Systems.

[13] Toolbox for features analysis: http://time-frequency.net/tf/.[14] Toolbox for pattern recognition design: http://perclass.com

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Thanks