Workshop on Mathematical EngineeringIISc-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 is the azimuth angle• is the elevation angle

)cos()sin(2 V

fd

Space time spectrum for side looking arrayRadar 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 MethodProminent 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 AlgorithmBlock 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 2Shift of displacement vector in two images

Optimal Detection and Parameter EstimationOptimal Detection and Parameter EstimationDechirping

Reference Signal

Moving Target

fD

t

Fixed scene

Moving Target

fD 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 AntennaDisplaced 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 (c21) from first echo at second antenna (c12)

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 ProcessingSpace Time Adaptive Processing