Issues and challenges in Airborne Radars - Performance Analysis Lab

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Nov 14, 2013 (3 years and 10 months ago)

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Workshop on Mathematical Engineering

IISc
-
DRDO



ISSUES & CHALLENGES IN
AIRBORNE RADARS


Dr A VENGADARAJAN, Sc ‘F’, LRDE


09 JUNE 2007

Airborne Radars being developed by LRDE





SV 2000 Maritime Patrol Radar



Primary Radar for AEW&C



Synthetic Aperture Radar for UAV application with

road map to extend it to other aircrafts



Active Electronically Scanned Array (AESA) for Fire

Control Radar


Multi Mode Radar

Common requirements of various airborne radars


Look up mode (air
-
to
-
air operations


detection &
tracking)


Look down modes (air
-
to
-
air operations


detection &
tracking)


Look down mode (air
-
to
-
ground operations


detection
& tracking)


Look down mode (mapping operations)


Look down mode (Ground ranging)


Look down mode (Air to Sea operations


detection &
tracking)

Radar to operate in multiple modes using Low,
Medium & High PRF

Detection & Tracking Requirement



Clutter spreads in the Doppler domain due

to platform motion



Waveform optimization to maximize

detection of targets against background

clutter



For various modes of operation



For various height of operation



For various clutter regions

Synthetic Aperture Radar



Stripmap SAR



Spotlight SAR



Scan SAR



Ground Moving Target Imaging

SAR MODES

Scan


Stripmap


Spotlight


Challenges in Synthetic Aperture Radars



Platform Motion Compensation (PMC)



Transfer alignment of master
-
slave navigation

system



Data derived motion compensation


Auto
-
focus

techniques



Spotlight SAR



Compensation for motion Through Range Cells

(MTRC)



GMTI

Challenges in Synthetic Aperture Radars

(Ground Moving Target Imaging)



Detection of Ground moving Targets
-

low velocity

(relative) targets



Conventional MTI cannot serve the purpose as these

targets gets submerged in the Main Lobe Clutter

Different Ground Moving Target
Indication and Detection Methods



Prominent

point

identification

method




Block

Matching

Algorithm



Detection

and

parameter

estimation



(a)

Without

Time

Frequency

Analysis



(b)

With

Time

Frequency

Analysis



Displaced Phase Center Antenna



Space Time Adaptive Processing (STAP)


Challenges in SAR + GMTI Image Processing



Overlay of SAR & GMTI images



Automatic Target Detection and Target Classification

of SAR images



SAR image processing issues


SPACE TIME ADAPTIVE PROCESSING



Applicable for both conventional radars as well as for

GMTI operation in SAR



Possible to detect very low velocity targets through two

dimensional processing

Space Time Adaptive Processing


STAP refers to the adaptive processing algorithms that
simultaneously combine the signals from the elements of an array
antenna (
spatial
) and the multiple pulses of a coherent radar waveform
(
temporal
).



Possible and required whenever there exists a functional
dependency between the spatial and temporal variable.



Moving Pulse Doppler Radar : Dependency of the clutter Doppler
frequency on the Direction of arrival;





Where


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楳i瑨攠敬敶慴楯渠慮杬a




)
cos(
)
sin(
2



V
f
d

Space time spectrum for side looking array

Radar returns are projected in both angle and Doppler domain

Filter requirements to remove the clutter and jammer

Challenges in STAP



Reduced Data Processing towards easing the
computational complexity



Requirement of massively parallel processing for real
time processing



Requirement of new STAP algorithm to provide for
realistic (non
-
Gaussian, heterogeneous) clutter
cancellation



Generation of simulated/measured data



STAP for Medium and High PRF operation under non
-
side looking conditions.



Sub aperture based STAP

FUTURISTIC REQUIREMENTS



Knowledge Based airborne radar systems



Signal Processing, Data Processing and Radar
Controller & Scheduler



Cognitive Radar

Prominent Point Identification Method


This

method

is

applicable

only

to

Spotlight

SAR

mode
.



Compensates for translational and rotational motions between SAR
antenna phase center and the target.



In the first stage the relative translation between the radar and the
target is estimated and its effect eliminated.


In

the

second

stage,

the

rotation

rate

of

the

target

is

estimated

by

choosing

a

second

prominent

point,

compressing

its

signal

history

in

range,

tracking

the

motion

of

this

point

in

the

phase

history
.


These

two

stages

results

in

the

complete

focussing

of

the

target

Moving target not
at the scene center

Moving target at
the scene center

Initial Scene
Center

Moving Target

Block Matching Algorithm


Generates

images

at

different

times

of

the

same

location
.

Therefore

the

clutter

background

appears

static

whereas

the

positions

of

moving

target

changes

from

image

to

image
.



Detection

and

estimation

of

target

velocity

and

position

is

done



Candidates

for

moving

target

are

done

according

to

signal

amplitude



Then

a

maximum
-
likelihood

estimation

of

velocity

and

position

is

performed
.



The

velocity

of

a

candidate

is

obtained

by

estimating

displacement

vectors

in

pairs

of

two

successive

single

look

images

by

block

matching

algorithm
.

Position of tgt in image 1

Position of tgt in image 2

Shift of displacement vector
in two images

Optimal Detection and Parameter Estimation

Dechirping

Reference Signal

Moving Target

f
D

t

Fixed scene

Moving
Target

f
D

Fixed scene

t

Sine
Output

Doppler
filter bank

Fixed scene gives a sine

Moving target still gives a chirp

Estimate Doppler frequency


and Doppler frequency Rate

Displaced Phase Centre Antenna

(Two element, two pulse case)








PRF

is

chosen

such

that

aircraft

moves

by

one

inter

element

spacing

for

each

pulse


Clutter

cancellation

is

done

by

subtracting

the

second

echo

at

first

antenna

(c
21
)

from

first

echo

at

second

antenna

(c
12
)



This

approach

uses

processing

in

both

the

time

and

spatial

domain
.

Till

now

the

algorithms

were

based

upon

the

first

order

statistical

characteristics

of

the

echo
.

But

STAP

uses

the

second

order

statistics
.

This

is

because

the

determination

of

a

target

in

a

particular

cell

is

no

longer

confined

to

a

look

into

a

linear

array

of

cells,

rather

the

targets

are

determined

using

information

about

adjacent

cells

in

both

dimensions
.


Space Time Adaptive Processing