EITN90 Radar and Remote SensingLecture 4: Characteristics of Clutter

Daniel Sjoberg

Department of Electrical and Information Technology

Spring 2018

Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Learning outcomes of this lecture

In this lecture we willI Characterize the clutterI Observe orders of magnitude from different sourcesI Have an initial discussion on clutter suppressionI See a few empirical models

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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What is clutter?

I Backscattering from natural objects, such as precipitation,vegetation, soil and rocks, or the sea.

I When trying to detect man-made object, it is considered anunwanted interference, masking the signal.

I When surveying natural processes (thickness of ice caps,weather etc), it may be the main signal of interest.

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Examples of clutter

Images from http://www.radartutorial.eu/ (CC BY-SA 3.0).

PPI screen of an ATC-radarwith targets and clutter.

Sea-Clutter on a PPI-Scope.Wind from 310◦ or 130◦.

By observing how the image evolves with time gives furtherinformation. Clutter can fluctuate and move.

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Radar imaging

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target

clutter

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Clutter vs noise

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Scattering coefficients

The received electric field strength from the i-th scatterer isproportional to (k collects factors common to all scatterers)

|Ei| ∼[PtG

2λ2σi(4π)3LsR4

]1/2= k

√σi

d2i, arg{Ei} = −

(θi +

4π

λdi

)

E =∑i

Ei =∑i

k

√σi

d2iexp

[−j(4π

λdi + θi

)]=

k

d2√σejφ

The complex number√σejφ is the backscatter coefficient and d is

the nominal distance to the clutter.10 / 48

Clutter polarization scattering matrix

Taking polarization effects into account, the concept of thebackscatter coefficient can be extended to the polarizationscattering matrix (PSM).

S =

(√σHHe

jφHH√σHVe

jφHV

√σVHe

jφVH√σVVe

jφVV

)The PSM could also be expressed in circular polarization (righthand CP and left hand CP). Additional information on thescatterer can be obtained by considering, for instance,

I Parallel/cross polarization ratio:√σHH/

√σVH.

I Vertical/horizontal polarization ratio:√σVV/

√σHH.

I Polarimetric phase: φHH − φVV.

These measurements require a radar capable of transmitting andreceiving individually in all polarizations, which is expensive.

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Surface and volume reflectivity

The absolute square of the complex backscatter coefficient√σejφ

is the radar cross section σ of the clutter.

To characterize clutter originating from a surface, use the surfacereflectivity

σ0 =σ

A[σ0] =

m2

m2= unitless

where A is the illuminated clutter area.

For clutter scatterers in a volume, use the volume reflectivity

η =σ

V[η] =

m2

m3= m−1

where V is the illuminated clutter volume.

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Beam limitation vs pulse limitation

Depending on pulse length cτ , the illuminated clutter area islimited by the projected beam or the projected pulse (θ3 and φ3are the 3 dB azimuth and elevation beam widths, respectively):

δφ3

δφ3 cτ

A =πR2 tan

(θ32

)tan

(φ32

)sin δ ≈ πR2

4θ3φ3sin δ A =

cτR tan(θ32

)cos δ ≈ cτRθ3

2 cos δ

The illuminated clutter volume is restricted by the pulse length

cτ

V = πR2θ3φ34

cτ2

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Dependence on grazing angle

The surface reflectivity depends on the grazing angle.

Based on theory and measured data for land and sea. Thebehavior at low grazing angles is motivated by the surfacebecoming smoother (less backscattering). Rayleigh’s definition of asmooth surface is

σh sin δ <λ

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Random nature of clutter

The clutter response varies with time and space due to motion ofthe radar or the scatterers, for instance due to wind. A statisticalapproach is necessary, for instance using the Weibull distribution

pσ =

{bσb−1

α exp(−σb

α

)σ ≥ 0

0 σ < 0

where α = σbm/ ln 2 and σm is the median of the distribution.

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Spatial statistics for ground clutter

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Land reflectivity: grass and crops

Follows the general trend shown before. Depression angle is anangle relative to the radar system, same as grazing angle for levelsurface. This is easier to control in an experiment.

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Land reflectivity: trees

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Land reflectivity: frequency

Higher frequency implies higher reflectivity.

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Sea reflectivity: affecting factors

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Sea reflectivity: measurements

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Sea reflectivity: range dependence

Theoretically, sea clutter should decrease as R−3, but maydecrease faster. 24 / 48

Clutter suppression, decorrelation time

I The clutter decorrelation time τ0, is the time over which theclutter response is coherent (stable phase and amplitude).This is frequency dependent.

I If the target signal is stable over longer time than τ0, thesignal-to-clutter ratio can be improved by averaging.

I If PRI > τ0, each clutter sample is uncorrelated.

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Clutter frequency spectra

Theory predicts Gaussian-shaped spectra, but actual measurementsoften result in a slower roll-off with frequency. This may be due toimperfections in the systems, since a very well-controlledexperiment (Billingsley, ref [11]) was well modeled by a Gaussiandistribution.

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Atmospheric clutter

Most volumetric (atmospheric) clutter is due to rain or otherprecipitation. It depends on rain rate, and the drop-size (typically0.5–4 mm) in relation to the wavelength λ.

Strongest response around ka ≈ 1, radius a ≈ λ/(2π), or adiameter around λ/10.

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Rain data

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Snow data

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Rain decorrelation time

Decorrelation time in the order of milliseconds. This correspondsto a limit for maximum PRF in order to have uncorrelated clutterresponses in each pulse (PRFmax = 1/τ0).

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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General remarks

Clutter is notoriously difficult to model, due to the complexity ofthe real world phenomena it represents. But still, explicit modelsmay provide useful approximations when evaluating the radarscenario.

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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GTRI empirical model

The following model was developed in the late 1970’s

σ0 = A(δ + C)B exp

[−D

1 + 0.1σhλ

]

I δ is the grazing angle in radians

I σh is the rms surface roughness

I λ is the wavelength

I A, B, C, and D are empirically derived constants

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GTRI coefficients

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Comparison of GTRI model with measured data

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GTRI sea clutter model

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Rain clutter

The model parameters A and B below can be fitted to the raindata in Figure 5-33:

η = ARB [m−1]

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Land reflectivity

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Sea reflectivity

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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Conclusions

I Characterization of clutter: backscatter coefficient, surfacereflectivity σ0, volume reflectivity η.

I Illuminated area/volume determines the clutter RCS.

I Clutter decorrelation time needs to be considered for cluttersuppression.

I Some empirical models exist for estimating the reflectivity fordifferent contexts.

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Outline

1 Introduction and definitions

2 General characteristics of clutterSurface clutterAtmospheric clutter

3 Clutter modelingSurface clutterAtmospheric clutterSummary of clutter results

4 Conclusions

5 Lab on Friday

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About the lab

I The lab will take place in the same room as the exercises.Note the time: 8–12!

I The lab is based around a simple ultrasonic sensor placed on astepper motor, controlled by an Arduino unit.

I Read the lab instructions carefully before the lab! Theyare available on the course web site, under “Lectures”.

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A practical problem: interference!

Since several units will be operating at the same time, they mayinterfere with each other, meaning one unit may receive both itsown echo (intended) and the direct signal of another unit (notintended).

I We will use (at least) two rooms: 4118 and 4115, in order toreduce problems.

I In each room, make sure to spread out, and try not to pointyour radar in the direction of others (remember signals willalso reflect in walls, but the range is only a couple of meters).

The lab is done in pairs of two, meaning we will have 10 groups.Ask your lab leader Sebastian if you get strange results, or if thereare any other questions.

Before you leave the lab, demonstrate your findings to thelab leader in order to be approved on the lab!

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