Hardware in the Loop Radar Clutter Simulationaardvarkaoc.co.za/wp-content/Proceedings/201211... ·...

Hardware in the Loop Radar Clutter

Simulation

Presenter: Jurgen Strydom

Systems Engineer & Signal Analyst

Experimental EW Systems, CSIR

Email: [email protected]

Co-authors: Jacques Cilliers, Andre McDonald, Klasie Olivier

7 November 2012

Outline

• Hardware in the Loop simulation

• Overview of radar system specifications

• Radar testing and evaluation

• Digital Radio Frequency memory

• Radar environment simulation

– Complex targets

– ECM

– Clutter

• Radar clutter simulation

– Ground clutter

– Sea clutter

– Sidelobe clutter for an airborne platform

• Conclusion

© CSIR 2012 Slide 2

Hardware in the Loop Simulation

• Hardware-in-the-loop simulation is a well established technique used in the

design and evaluation of hardware systems

• Traditional testing of systems relied solely on field trails

• Hardware in the loop replaces the actual environment with a simulated

environment

• The environment is simulated on a hardware platform, and connected to the

system under test

• Radar systems are connected by RF either through air coupling or by cable


Radar Environment


Radar Types


• Radars by Function

• Weather Avoidance

• Navigation & Tracking

• Search & Surveillance

• High resolution Imaging (mapping)

• Proximity Fuses

• Countermeasures

• Examples

• Search radar (High power, pulsed, low PRF, long pulse

lengths, lower frequency bands (L/S), low resolution)

• Airborne radar (Low power, pulsed, medium to high PRF,

higher frequency bands (X), high resolution)

Radar waveforms


• Continuous wave (CW)

• Frequency modulated continuous wave (FMCW)

• Pulsed

• Non-coherent

• Coherent

• Low PRF (1 - 3 KHz)

• Medium PRF (10 - 30 KHz)

• High PRF (100 - 300 KHz)

• Spread spectrum - Low Probability of Intercept (LPI)

• Multi mode (track while scan)

• Examples

• Missile fuse (FMCW)

• SAR / ISAR (pulsed Low / Medium PRF)

Environmental Factors


• Influence on radar performance

• Target Glint

• Multipath

• Signal propagation effects

• Noise

• Clutter

• Sea clutter

• Ground clutter (trees, grass, buildings, mountains)

• Countermeasure clutter (e.g. Chaff)

• Airborne clutter (birds, clouds, rain/snow, small aircraft)

• Intentional / Unintentional Jamming / System Blanking

• Line of sight

• Commercial Spectrum usage (cell-phones, etc., etc.)

Radar testing and evaluation

• Radar testing and evaluation is becoming difficult for modern radars

because of their adaptive nature

• Two possible approaches:

• Development teams can design and build their own test equipment

for each radar

• Generic test equipment can be designed to test a class of radars

• The second approach is well suited to:

• Organizations such as defence evaluation and research institutes

(DERI)

• Agencies that specialize in independent review acceptance testing

and optimisation of operational utilisation


Radar testing and evaluation

• Hardware in the loop testing allows for the evaluation of critical

functionality, before optimisation of weight and size

• Reduces number of measurement field trails, as well as cost of

developing system

• Continuous testing of repeatable scenarios during the development

of the radar


CSIR DRFM

The DRFM kernel

• Captures radar transmit pulse

with ADC

• FPGA stores data in digital

memory

• FPGA reads data at required

time delay

• Radar pulse transmitted through

DAC

• Local oscillator used in both RF

up and down conversion chain

to guarantee coherency

• This architecture is commonly

referred to as a DRFM


The HIL simulator

• Captures radar transmit pulse

with ADC

• FPGA stores data in digital

memory and a modulation is

applied

• FPGA reads data at required

time delay

• Radar pulse transmitted through

DAC

• Local oscillator used in both RF

up and down conversion chain

to guarantee coherency

• This architecture is commonly

referred to as a Radar

Environment simulator


The HIL simulator

• DRFM and Radar environment simulator processes match

• DRFM is a modular building block

• Different firmware on DRFM allows for different types of modulations

to signals which represents different radar environmental elements

• Complex targets

• ECM techniques

• Radar clutter

• The combination of each of these DRFM channels results in the

Hardware in the loop radar environment simulator


Radar Environment Simulation

Advances in DRFM technology allows for the real-time simulation of a

wide range of targets, ECM techniques, and environmental effects

Radar Environment Simulation

Target Simulation Electronic Attack Target Environment

Point targets

Multi scatterer targets

Jamming Clutter


Complex Targets

• A conventional DRFM simulates a target by simply re-transmitting the radar

pulse

• Approximate target as a single scatterer

• Assumption is made that radar target is only present in a single range cell of

the radar


Complex Targets

• Today's radars use High Range

Resolution (HRR) profile techniques

• Separation of scattering points on a

single target

• This creates a "complex" target return

• A conventional DRFM simulates a target by simply re-transmitting the radar

pulse

• Approximate target as a single scatterer

• Assumption is made that radar target is only present in a single range cell of

the radar

• Can be used for Non-cooperative target recognition (NCTR)


Complex Targets

• High Range Resolution target profile [1]

[1] J.C. Smit, J.E. Cilliers, E.H. Burger, "Comparison of MLFMM, PO and SBR

for RCS Investigations in Radar Applications," IET radar conference 2012.


ECM

• Deception techniques to interfere with target detection and tracking

• Non-coherent jamming (injection of high powered noise)

• Manipulation of transmitted radar pulse

– Range

– Doppler

– Amplitude information

• Goal is to have radar interpret false target as actual target

• Common ECM techniques for tracking radar

– Range gate pull off

– Velocity gate pull off


Radar Clutter


Clutter from literature


Considerations for high fidelity clutter generation

• Clutter Radar Cross Section (RCS)

• Number of discrete scatterers

• Spatial extent of clutter

• Velocity extent of clutter (Doppler spectrum)

• Wavelength dependence

• Amplitude distribution

• Spatial correlation

• Polarization properties

From [4]

Radar Clutter from literature


From [1]


Amplitude distribution for radar clutter

Grasslands measured with HH polarisation at X band with an incidence

angle of 20 degrees


From [2]



10dB attenuation

30dB attenuation

50dB attenuation

Clutter returns can be very large:

From [3]



Clutter becomes more spiky as range resolution increases

From [3]



Spatial behaviour of clutter

No real correlation in spatial dimension, but scenario depended

From [1]



Temporal behaviour of clutter

Temporal correlation can be large

From [1]

The Problem


• Clutter returns interfere with the object of interest (target)

• The performance in clutter is a critical aspect of the radar

• Real world testing of a radar against all types of clutter for all

possible types of scenarios is costly and difficult to repeat

• Software simulation cannot take all the finer details of the

complete design and implemented system into account

• Severely limited with software simulation if you are required to

verify a radar purchased from a 3rd party

The Solution


• Hardware in the loop simulation on DRFM based hardware

• Statistical modelling of clutter, NOT recorded data

• Playback of recorded data is radar and configuration dependent

Synthetic Clutter Simulation


Goal:

Simulate clutter in real time as realistic as possible

Reality:

Real time is pretty fast, and realistic is computationally complex, so these

requirements have to be interpreted somewhat...

Realistic land clutter varies drastically depending on the terrain

Land clutter includes:

Soil, rocks, trees, grass, shrubs, short vegetation, road surfaces, urban

areas, dry snow, wet snow, etc.

A single range line can contain any combination of these




• Wind direction does not change relative to radar because of the

constant look angle

• Statistics remain constant

• Range line can be divided up into range segments to re-create the

change in properties of the illuminated area with range



• Segments contain an arbitrary number of time correlated scatter points

• Each segment is set independently of the other segments

• Each segment has is own statistics

• Amplitude

• Spectrum

• Probability Density Function (PDF)

• All segments combine to produce a single range line



Record accurate radar

data

Figure on left:

Data captured with the

measurement radar of

the CSIR





What has been achieved thus far:

• Correlated ground clutter (Rayleigh, Weibull, Log-Normal)

• Mainbeam for stationary platform and moving platform

• Gaussian approximation to clutter bandwidth

• 2 million+ independent clutter scatterers in a range line

• 500 MHz instantaneous bandwidth

• Input pulse lengths from 50 ns up to 300 us

• PRF from 0.8 kHz to 300 kHz

• Synthetic Clutter Simulation System covers large number of radar

systems

Sea Clutter Simulation

Current research:

• Clutter simulation for a seaborne search radar

The Challenge:

• Sea clutter statistics not straightforward to connect to sea

environment, there are no good solutions in literature as of yet

• Clutter statistics are dependent on many variables (wind direction

and magnitude, wave direction and magnitude, water depth, etc.)

• Many different scattering mechanisms, complex models



• Scattering mechanisms for sea clutter have large and small scale

• Spiky nature of clutter caused by the small scale scattering

mechanisms: ripple, spray and foam.

• Large scale (swell) decorrelates slowly, small scale decorrelates quickly

• Breaking waves



Compound model for sea clutter:

• Texture (tau): non-negative random process; takes into account the

local mean power (large scale)

• Speckle (x): complex Gaussian process, takes into account the local

backscattering (small scale)

• For a Gamma texture the K distribution results (amplitude PDF):



• Sufficient for low resolution radars

and for large grazing angles

• Many scattering points in a range

cell cause Gaussian statistics

• Magnitude of a Gaussian signal is

Rayleigh

• K distribution represents sea

clutter statistics

• Sea clutter is more spiky

• Fewer scattering points in a

range cell causes more spikes

0 100 200 300 400 500 600 700 800 900 10000

1

2

3

4

5

6

7

8

0 100 200 300 400 500 600 700 800 900 10000

1

2

3

4

5

6

7

8

Range

Range

Am

plit

ud

e

Am

plit

ud

e

Rayleigh distributed clutter

K distributed clutter


Sea Clutter Measurements




Sea Clutter Measurements [2]

Low sea state High sea state

Rayleigh

Incre

asin

g S

pik

yn

ess (

K-D

ist)

A. McDonald, J.E. Cilliers, "Autoregressive-to-anything process model of

maritime clutter and targets," IET radar conference 2012.



• On-line database freely available of small boats in sea clutter

– Radar data database of this quality and size are very rare

– Very well instrumented targets in sea clutter

– Database is well documented in a user guide

– Aim is small boat detection in sea clutter

– Over 100 users from over 16 countries

– Did I mention it is free?

• Available at:

http://www.csir.co.za/small_boat_detection/


• Wind and wave direction changes relative to rotation angle of radar

• Therefore statistics change

• Segments & Sectors are used to re-create a scene

• Time multiplexing can be used to create a 360 degree scenario


Rotation angle

Am

plit

ud

e


• Time multiplexing can be used to create a 360 degree scenario

• Scenario divided into sectors based on desired statistics in that

direction

– Divisions based on regions of similar statistical properties

– Divided in a statistically meaningful way

– For example :1 sector of 80 degrees for an area looking at a

mountain and 35 sectors of 8 degrees to capture the sea surface

with the relative wind and wave direction changes







Current research:

• Clutter for a moving airborne platform

The Challenge:

• For stationary radar platforms the mainlobe is sufficient

• For moving airborne radar platforms the mainlobe is not the only

contributing factor to the radar range Doppler map

• Antenna sidelobes of the radar becomes a large contributing factor



From: N. Levanon, Radar Principles

Mainlobe only scenario



From: G. Morris and L. Harkness, Airborne Pulsed Doppler Radar



Data from:

Recorded Data Airborne Range Doppler map



Building the airborne radar Range Doppler map:

• It is relatively easy to adapt the mainbeam of the stationary ground

based platform to the moving airborne platform case

• Apply a Doppler offset that corresponds to the look direction and

range of the intersection with the surface

• Airborne platform (because of its movement) spends less time

looking at the same patch of ground, thus less correlation between

pulses, which results in a wider bandwidth



Spectrum of mainbeam Spectrum of sidelobes

- Gaussian Doppler power

spectrum

- Simple to implement

- Jakes Doppler power spectrum

- Computationally expensive to

implement



Range (k

m)

Doppler velocity (m/s)

Sim

ula

ted a

irborn

e ra

dar ra

nge D

opple

r map

00.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1-2

50

-200

-150

-100

-50 0

50

100

150

200

250

0 0.5 1 1.5 2 2.5

x 104

-10

-5

0

5

10

15

20

Doppler Frequency

Magnitude

Sidelobe simulation

Simulated range Doppler map with the DRFM sidelobe clutter algorithm


Challenges:

• Difficult to split a single scenario over multiple DRFMs

• "High" latency and "low" transfer rate between seperate DRFM

systems (relative to on chip)

• This bottleneck makes it difficult to sync clutter scatterers

Possible Solution:

• Mainlobe / Sidelobe is an ideal split for the use of multiple DRFMs

• Statistics of mainlobe is different and uncorrelated to that of the

sidelobe

• Thus one can get away with minimum sync of scatterers



Technological tradeoffs:

• DRFM is processing power limited

• Bandwidth spectral shaping quality and accuracy

• Fidelity of clutter (number / update frequency, of clutter samples )

• Complexity of statistical distribution shape

• Rayleigh (least complex)

• Weibull (medium complexity)

• Log-Normal (medium complexity)

• K-Distribution (most complex)

• Trade-off between a high fidelity model over a small area or a low

fidelity model over a large area


Future Synthetic Clutter Simulation


Sea Clutter Airborne Radar Ground Clutter Weather Clutter

K-Distributed PDF

Rayleigh PDF

Log-Normal & Weibull PDF

Mainbeam clutter

Sidelobe clutter

Clouds / Rain / Hail / Snow

Exponential Doppler Spectrum

Moving airborne platform

Stationary platform

Gaussian Doppler Spectrum

Moving platform

Completed:

In-Progress:

Future research: Arbitrary PDF

Counter Measure Clutter

Chaff

Multipath

Integrated capability

Scenario simulation & control

RCS / HRR prediction HIL simulation

System under test

Radar under test

International Conferences

• Past conferences (2010 / 2011)

• Conferences for 2012

The End


References

[1] J.B. Billingsley, "Low-Angle Radar Land Clutter,

Measurements and Empirical Models"

[2] F.T. Ulaby and M.C. Dobson, "Radar Scattering Statistics

for Terrain"

[3] F.E. Nathanson, Radar Design Principles, Signal

Processing and the Environment"

[4] D.K. Barton, "Modern Radar System Analysis"

[5] K.D. Ward, R.J.A. Tough and S. Watts, "Sea Clutter:

Scattering, the K Distribution and Radar Performance"

The End


Special thanks:

Airborne radar data provided by:

Contact information:

Jurgen Strydom

[email protected]

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