Challenges in Radar ESM Processing

R. Cassels

Isaac Newton Institute for Mathematical Sciences

27th March 2013

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Overview

What is ESM?

Radar Systems

• Basics

• Improving Performance

• Trends

Electronic Support Measures (ESM) Systems

Selected Problems

Questions

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What is ESM?

Electronic Warfare (EW)

• Use the electromagnetic (EM) spectrum to increase survivability of friendly forces

whilst denying its use to an adversary

• Electronic Support (ES)

– Use EM spectrum to give situational awareness

– Used to be called Electronic Support Measures (ESM)

• Electronic Attack (EA)

– Prevent hostile use of the EM spectrum

• Electronic Protection (EP)

– Preventing others’ EA from affecting friendly systems

Radar ESM

• Detect radar emissions to provide situational awareness

– Types, behaviour, location

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Radar in the Airborne Environment

Other uses for radar too

• Shipping

• Ground penetrating

Surveillance

• Civilian

• Military

Air Defence

• Tracking

• Illumination

• Control & Communications

Airborne

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Radar Basics

Need to determine:

• Location (Range, Bearing, Elevation)

• Velocity (Radial & Tangential)

Send out a pulse of radio energy and “listens”

for the echo

Location:

• Beam width (azimuth, elevation)

• Time of flight

Velocity:

• Doppler

• Tracking

c

Rt f

2

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Pulse Width (PW)

Pulse Repetition Interval (PRI)

Peak Pulse Amplitude

General Pulse Characteristics

Envelope of the pulse train

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Radar Performance

Range

Impact of PRI

Measurement Resolution

• Angle

• Range

• Velocity (radial)

Number of targets

Update rate

Search time

min

2

22 444S

G

RR

GPt

min

3

224

4 S

GPR t

100μs

15km

Blue Transmitted

Green 15km Return

Red 60km Return

t

rSc

2

PW

D

kBW

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Improving Radar Performance

Desirable to

• Increase range

• Reduce power

• Improve resolution

• Update more often

• Search faster

Other considerations

• EP: how to protect against jammers?

– Prevent detection

– Agility

• Imaging

– Synthetic Aperture Radar (SAR)

Bursts

• Increased integration time

• Staggers

Intrapulse modulation

• Phase modulation

• Frequency modulation

min

3

224

4 S

GPR t

rSc

BW2

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Miscellaneous Radar Topics

Modes

• Multi-mode radars

• Track-while-scan

Operating Frequency

• Stealth

• Interference

Missile Guidance

CW Radar

Monopulse

Su

rve

illa

nce

Ra

da

rs

(Acq

uis

itio

n)

Counter Stealth

Surveillance Radars

(Early Warning and Acquisition)

B C D E F G H I J K L M

Older

FSU SAM FS

U S

AM

(G

uid

an

ce)

Air Defence

(Acquisition,

Tracking,

Guidance)

AI

FS

U A

I

SHOR-

ADS

Futu

re A

ir

De

fence

Future

Seekers

0.25 0.5 1 2 3 4 6 8 10 20 40 60 100 GHz

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ESM System

Aim?

• Detect

• Identify

• Locate

• Track Behaviour

Problems?

• Bandwidth

• Timing

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ESM Receivers

To Processing

Analogue

Front End

(Frequency

Selective)

Envelope

Extraction

Pulse Parameter

Measurement

Frequency

Measurement

Analogue

Front End

Analogue

Front End

(Frequency

Selective)

Envelope

Extraction

Pulse Parameter

Measurement

Frequency

Measurement

To Processing

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ESM Receivers

Envelope

Extraction

Pulse Parameter

Measurement

Frequency

Measurement

Analogue

Front End

Analogue

Front End

(Frequency

Selective)

Pulse Parameter

Measurement FFT Digitisation

To Processing

To Processing

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ESM Receivers

Analogue

Front End FFT

Pulse Parameter

Measurement Digitisation To Processing

To Processing

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ESM Receivers

Features

• Limited resolution in time and frequency

• Limited number of simultaneous pulse measurements

• Possibly limited bandwidth

“Dumb” systems

• Not capable of distinguishing between signal types

– e.g. GSM burst vs. radar pulse

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Deinterleaving

Resolving multiple signals

• Some easily separated

– By frequency

– By angle of arrival

• Some still mixed

– Must be separated

Gets harder

• Bursts – intermittent

• Multiple similar emitters

• Receiver has limited granularity

• Staggers, Jitters

• Agile emitters

AOA

RF Data Clusters

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Historic Technique

Identifies pulse trains from easiest to hardest

• Stable Trains are found first

• Staggered Trains next

• Jittered Trains next

• Finally, complex trains

Based on basic statistical techniques

• Histogramming

• Very opaque

• Tuned over time to each set of receiver hardware

Works well in low density pulse environments

Naïve – Only basic “models” included

No uncertainty measure exported

• Try to report only very certain conclusions

• Much data discarded

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Tracking and Classification

Identified pulse trains (Intercept Reports) checked against library of emitters

• Gathered from intelligence work

• Updated regularly

• Effectively these are pulse train templates

Intercept reports used to update previous tracks first

• If no match, a new track is formed

• Stable tracks will be presented to the pilot

Problems

• Ambiguity in models

• Difficult to assign confidence as the intercept reports have no uncertainty information

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Geolocation

Desire to locate threat systems

• To avoid missile engagements

Currently

• Use interferometry to get successive angles

– Use these to triangulate

• Use time difference and/or phase difference to get angle/range

Problem

• Many types of emitter are very similar

– e.g. maritime radar

• Hard to associate pulses correctly

• Some pulse types will not get complete information

Historic approach

• Kalman filter based

• Assumes that good track association has been done

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Changing World

Software defined radar becoming more widespread

• Pulse train characteristics can more easily be changed

• Intelligence information easily outdated

Merging of the radar and communications bands

• Current systems cannot distinguish between signals

• More detailed processing needed

More emitters with intrapulse modulation

• Low SNR

• Harder for older receivers to detect

Longer range threats

• Also Low SNR

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What Does This Mean?

Historic techniques likely to become even less effective over time

• Classification might no longer be possible

– Still want to know the behaviour, however

• All data likely to be uncertain to some extent

– Need rigorous methods of modelling uncertainty

New techniques and analyses needed for analysis of raw sample data

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Possible Solutions

More complex “generic” models

• Choose models and parameters

Multi-hypothesis analysis

• To deal with ambiguity

Thresholding still needed

• To reduce data presented to the pilot

• Should be done as late as possible

– Improved performance

Slow-time detailed analysis of raw sample data

• Look for very low SNR (likely negative SNR) signals

• Needs ways to inform standard processing

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Conclusions

Historic techniques are insufficient for future systems and current systems in the future

Due to receiver technology, only incomplete, noisy, coarsely measured data is available

More powerful mathematical tools must be brought to bear

• Particularly those based on sufficiently rich models

• Must deal with the noisiness of the data

There are many more such problems in EW

Close work between industry and academia needed to solve these