The revolutionary active electronically scanned array (AESA) Radar provides huge gains in performance and all the front line fighters in the world from the Americans (F35, F22, F18, F15, F16) to the Europeans, Russians and Chinese already have one or soon will. This four day seminar, which took 10,000 man hours to produce, is a comprehensive treatment on the latest systems engineering technology required to design the modes for an AESA to capitalize on the systems inherent multi role, wide bandwidth, fast beam switching, and high power capabilities. Steve Jobs once said “You must provide the tools to let people become their best”, and this seminar will include two indispensable tools for the AESA engineer. 1) A newly written 400+ page electronic book with interactive calculations and simulations on the more complicated seminar subjects like STAP and Automatic Target Recognition. 2) A professionally designed spread sheet (with software) for designing, capturing and predicting the detection performance of the AESA modes including the challenging Alert-Confirm waveform.
AESA Airborne Radar Theory and Operations Technical Training Course Sampler
1. AESA Airborne Radar Theory and Operations
Instructor:
Bob Phillips
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3. Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and
Operations Course Sampler
Robert A Phillips
AnnapolisStar@gmail.com
AESA Airborne Radar Theory and Operations
Introduction Page 1
4. Copyright 2013 R.A. Phillips
Objective Number 1
1) Learn how to interleave modes, intercept targets using
advanced LPI techniques, and develop requirements for an AESA
radar from the pilots point of view.
AESA Radar engaging and launching
missiles on three targets
AESA Airborne Radar Theory and Operations
Introduction: Page 2
5. Copyright 2013 R.A. Phillips
Objective Number 2
2) Present the theory of an AESA Radar and learn how to design
the air-air and air-ground modes from the requirements up
Antenna Receive Pattern using a
diamond layout with true symmetric
Dolph Chebyschev sidelobe weighting
(from supplied Radar Theory eBook)
Clutter
Template
Pulse
Compress
FFT
Square
law
Detector
CFAR
M of N
Correlate
Block Diagram for Search mode
AESA Airborne Radar Theory and Operations
Introduction: Page 3
6. Copyright 2013 R.A. Phillips
Objective Number 3
3) Provide the simulations, tools, and references for “putting the
theory into practice”
500 page interactive
electronic book on
class material including
antennas, Space Time
Adaptive Processing,
Kalman filters, and
automatic target
recognition, with
simulations & examples
Win 7 Professional Radar mode
design spread sheet with software
AESA Radar Theory eBook
"You cannot mandate productivity, you must provide the tools to
let people become their best.“ Steve Jobs
AESA Airborne Radar Theory and Operations
Introduction: Page 4
7. Copyright 2013 R.A. Phillips
Some of the Questions to be Answered
1) How do you design and compute the performance
for the AESA search modes?
2) How do you design an AESA mode to track 50
targets ?
3) What is Space Time Adaptive Processing (STAP)
and how do you design for it?
4) How can you use an AESA antenna to detect slow
moving ground targets which are much smaller than
the background clutter.
5) How do you design an automatic target detection
and recognition mode?
This sampler presents the top level charts from the course on
how to answer these tough questions
AESA Airborne Radar Theory and Operations
Introduction: Page 5
8. Copyright 2013 R.A. Phillips
Sampler
1) Design of an AESA
Medium PRF Search
Mode
AESA Radar in Medium PRF search
AESA Airborne Radar Theory and Operations
Introduction: Page 6
9. Copyright 2013 R.A. Phillips
MED PRF Search Block Diagram
The Block Diagram for a MED PRF Search radar [Skolnick,fig 17.6]
Altitude
Speed
Clutter
Template
[12]
Sum Channel
Compress
FFT
Square
law
detect
CFAR
We will use an AESA antenna and receiver
with parameters like size, noise figure,
power and cooling appropriate for a fighter
type aircraft (from Stimson) to design the
modes and compute the performance
AESA Airborne Radar Theory and Operations
Smallest allowable
Target size m2
Skolnick Fig 17.12
Unfold
Detects
M of N
Range
Correlator
M of N
Doppler
Correlator
Target Reports
Range, Doppler,
Cross Section
Introduction: Page 7
10. Copyright 2013 R.A. Phillips
Clutter Template from Supplied Simulation
Tail aspect
Head
• In this region the template tells us
we should use a backend STC or
guard channel
• In this region we are competing
with altitude line – Use special
processing to blank returns
• In this region the template tells us
we are competing with noise only
and we can use the noise PFA
threshold.
• In this region we are competing
with Main Beam Clutter. Due to
its magnitude we will use a notch
filter
The template [12] guides us in choosing a CFAR design
AESA Airborne Radar Theory and Operations
Introduction: Page 8
11. Copyright 2013 R.A. Phillips
Baseline MED PRF Search Parameters
Parameter
Value
Comments
FFT Size
512
Controls S/N and scan rate
PRF
70KHz
For good tail aspect visibility
CHIP
0.5mics
For reduced clutter
PCR
4
Higher average power
M of N
3 of 7
Range correlation
TFA
30sec
Specification time between FA’s
Freq Agile
Look-Look
Good LPI design
Xmit Pulse
2mics
Duty
14%
Derived
Avg Power
parameters Pfa
471watts
5.8E-6
CFAR probability of false alarm
The course will show the student how to select the parameters and
enter them into the Mode Design spreadsheet
AESA Airborne Radar Theory and Operations
Introduction: Page 9
12. Copyright 2013 R.A. Phillips
MED PRF Single Scan Performance
Single Scan PD - Low PRF .VS. Medium PRF
Cross Section 5m2
Chart from the
mode design
spreadsheet
using VBA
software from the
eBook on
Detection Theory
(supplied with
course)
The Mode Design Spreadsheet 1) Guides the student in the
designing a mode, 2) Captures the designs and 3) Compares
the performance for different configurations
AESA Airborne Radar Theory and Operations
Introduction: Page 10
13. Copyright 2013 R.A. Phillips
Sampler
2) How to Track 50 Targets
with an AESA Radar
AESA Radar engaging and launching
missiles on six targets
AESA Airborne Radar Theory and Operations
Introduction Page 11
14. Copyright 2013 R.A. Phillips
Vector Tracking Loop
Error
∼∆kANT
Steering vector
k(α,β)
For monopulse vector processing see [9] Haupt
and eBook on antennas
Compute
Monopulse
Error ∆kANT
kT(θ,φ)
target
k(α,β)
antenna steering
General radar tracking loop
Transform
To NAV
Coords
Transform
to ANT
Coords
Kalman
Filter in
NAV
Target
Relative
Position
Ownship position vector
in NAV reference
The Σ and ∆ channels are used to compute the error vector ∆k in
ANT coordinates
AESA Airborne Radar Theory and Operations
Introduction: Page 12
15. Copyright 2013 R.A. Phillips
Three Channel (Az,El,Range) Kalman [1]
Gain Computation
For n in 1..3
= Pn H
Kn
T
( HP H
n
T
+ Rn )
Extrapolate
Where n is one of the
3 orthogonal channels
Rng, Az , El
XΦX
=
For n in 1..3
= ΦPn Q +
Pn P
n
n
−1
State Update
For n in 1..3
Nav
=
X X + K n En
P Update
For n in 1..3
Pn=
(1 − K n H ) Pn
The lectures will define each matrix in the design
AESA Airborne Radar Theory and Operations
Introduction: Page 13
16. Copyright 2013 R.A. Phillips
Typical Track Performance
RMS Velocity Error
Angle Error
Tracking a Steady 3G S Turn at 20nm. RMS velocity errors
typically approach 200+ft/sec and are entirely adequate to guide
missiles to intercept
AESA Airborne Radar Theory and Operations
Introduction: Page 14
17. Copyright 2013 R.A. Phillips
AESA Time Line
15 Target track interleaved with
search while displaying a SAR image.
Room for lots more!!
AESA Airborne Radar Theory and Operations
Introduction: Page 15
18. Copyright 2013 R.A. Phillips
Sampler
3) Space Time Adaptive Cancellers
AESA Airborne Radar Theory and Operations
Introduction: Page 16
19. Copyright 2013 R.A. Phillips
Space Time Adaptive Filters (Stimson, Haupt)
• The STAP canceller can remove multiple sidelobe jammer(s)
without prior knowledge of the jammer(s) location or antenna gains.
• STAP uses an Interferometric (space based) canceller.
• For each expected jammer we need one receiver and Auxilliary
antenna with a gain larger than the sidelobes of the main antenna.
Gain of AUX
Target
Adaptive Cancellers
Stimson [3,Ch 40],
Skolnick[4,Ch 9]
AESA Airborne Radar Theory and Operations
Standoff
sidelobe
jammer
STAP computes jammer phase
angles and antenna gains and
applies a spaced based adaptive
notch filter. By combining this
with an FFT to separate moving
targets we have a two
dimensional Space – Time
adaptive filter
Introduction: Page 17
20. Copyright 2013 R.A. Phillips
The Adaptive Canceller [7] Elbert
V
V
V
Main
AUX2
AUXn
Store samples from each
channel in the rows of the
H matrix
H=[m a2 a3…an]
X1
X2
∑
See also Stimson [3,Pg509]
The optimal weights X
are the 1st column of
the inverse of the
covariance matrix
(HTH)-1
Xn
Note the order of
the matrix inverse
is equal to the
number of
channels i.e. two
channels means
we have to invert
a 2x2 matrix
Sum the weighted outputs of the multiple
antennas to cancel the jammer.
The space filter is a direct application of linear estimation theory [7]
AESA Airborne Radar Theory and Operations
Introduction: Page 18
21. Copyright 2013 R.A. Phillips
Example of STAP With Multiple Jammers
Example of STAP with 4 Jammers. 4 Aux horns
Target
10deg
20deg
30deg
See eBook on
Antennas for
detailed
simulation of
multiple
jammers
40deg
Weighted
Sum
The optimal weights are:
x =1st Column of CovarianceMatrix −1
The cancelled jammer output equation is:
Output=Main+x1 Aux1 +x 2 Aux2 +x 3 Aux3 +x 4 Aux4
One 5th order Matrix Inversion and 25 dot products of length 10
AESA Airborne Radar Theory and Operations
Introduction: Page 19
22. Copyright 2013 R.A. Phillips
FFT Before and After Cancellation
The target cannot be seen in
the FFT with 4 Sidelobe
jammers. Notice the magnitude
of the noise at 100 Q or more!
Uncancelled Jammer + Target
After cancellation the target
is easily seen in the FFT
and the noise is down to 5
quanta
Cancelled Jammer + Target
Example from eBook on Antennas
AESA Airborne Radar Theory and Operations
Introduction: Page 20
23. Copyright 2013 R.A. Phillips
Sampler
4) Slow Ground moving target
indicator Main Beam
Clutter Canceller
AESA Airborne Radar Theory and Operations
Introduction: Page 21
24. Copyright 2013 R.A. Phillips
Slow Moving Target Detection
Combining the
Interferometer technique
(used in STAP) with
multiple antenna beams
we can implement a high
performance mode to
cancel main beam clutter
and detect small slow
moving targets in a
situation which
otherwise would be
completely hopeless
SAR display with outputs from the slow
moving target detector
One of the most impressive applications of an AESA canceller..
AESA Airborne Radar Theory and Operations
Introduction: Page 22
25. Copyright 2013 R.A. Phillips
Spatial vs Frequency Filtering
Tail aspect
Head
Frequency Filtering: With an FFT
we can separate targets with
different Doppler frequencies.
This fast moving target is separated
by frequency from main beam
clutter and is easily detected with
an FFT
FFT range/Doppler map
Spatial Filtering
This slow moving target,
overwhelmed in an FFT by main
beam clutter at the same frequency,
can only be detected by spatial
filtering with an interferometer
The course will describe this essential diagram in detail
AESA Airborne Radar Theory and Operations
Introduction: Page 23
26. Copyright 2013 R.A. Phillips
Slow Moving Targets and Clutter
Stationary
target at
angle θt
Large MBC
Clutter at
angle θc
In a space diagram the target and
clutter are separable
θt
θc Angle Space Map
Slow
moving
target at
angle θt
Whereas in a normal FFT frequency
diagram the target and clutter overlay
each other and the smaller target
cannot be detected
Doppler Frequency Space Map
A Spatial Notch with multiple antennas can remove the clutter
AESA Airborne Radar Theory and Operations
Introduction: Page 24
27. Copyright 2013 R.A. Phillips
Slow Mover - Canceller [Stimson Pg321]
k T (θ , φ )
k MBC (α , β )
The target at the
same frequency as
clutter
-d/2
The phase for clutter at angle α ,β :
d/2
Right
Left
Get α,β for
each FFT
Cell
Get Gain
for each
FFT Cell
MBC comes from a known
angle α,β
Rg x
Filter
matrix
Rg x
Filter
matrix
Cancel
Clutter
GLeft
πd
ϕc =R • k =
sin(α ) cos( β ), G rel =
GRight
λ
Using the canceller equation:
G
Output = Main - Aux M exp(− j 2ϕ )
GA
The cancelled clutter for each filter is:
Cancelled n = Leftn − Rightn exp(− j 2ϕc )
Recompute
FFT
CFAR
Slow moving
ground
targets
A little complicated but very powerful
AESA Airborne Radar Theory and Operations
Introduction: Page 25
28. Copyright 2013 R.A. Phillips
S
Sampler
ATR Finds 3 S-300
Surface – Air Missile
Launchers with
Pd>0.95 in 2 sec
S
5) Automatic Target Recognition
Target Detection
S
Bushehr nuclear power plant from Google Maps
AESA Airborne Radar Theory and Operations
Introduction: Page 26
29. Copyright 2013 R.A. Phillips
Automatic Target Detection Outline [13]
SAR Targets +
Clutter
Data from MSTARS public website, algorithms from
Lincoln labs and Mathcad image processing library
CFAR
Detector
Get
Enhanced
Tgt Chips
Binarize
Image
Detected targets
sans clutter
Clumped
Detects
Open/Close
Shapes
Edit Clutter
False Tgts
Compute
Moments
Statistics
Library
Clutter Shadow
Removal
Target
Recognition
Target List
Detector uses general target signatures to find “military like” targets
AESA Airborne Radar Theory and Operations
Introduction: Page 27
30. Copyright 2013 R.A. Phillips
Theory of Moments from [11] HU
Characterization of an image by statistical moments
like variance, and kurtosis, and invariant moments like
the eigenvalues is a common approach in ATR.
The Uniqueness theorem states that you can
completely reconstruct an image with knowledge of
the moments of the image.
If you use amplitude, translation, scale and rotation
invariant moments you increase the power of this
approach
E
All three E’s in this example are uniquely
identified by the same simple moments
which are independent of where they are
on the paper, their amplitude, scale or
rotation
We can also characterize tanks, trucks and guns by moments
AESA Airborne Radar Theory and Operations
Introduction: Page 28
31. Copyright 2013 R.A. Phillips
Example Automatic Target Recognition[13]
Enhanced M113 Chip
from ATD with feature
vector consisting of
moments, stats and
Pose=-30deg
pose
1) Use the pose to index the library
2) Compute Score for each target in
the library using feature vectors
3) The highest score is the ID
Library Chips
with same pose
as detected
target
BTR60
M113 BMP2
Correlation
0.81
1
Eigenvalues
0.62
1
Area
0.89
1
Combined
0.45
1
Good Match
Feature Vec
BTR70
T72
M109
M2
HMMW
M1
0.92
0.88
0.87
0.86
.91
.93
.85
0.71
0.61
0.73
0.54
.72
.70
.41
1
1
0.81
0.69
.91
.91
.67
0.66
0.54
0.51
0.32
.59
.59
.23
Comparison of feature vectors for each target in library
AESA Airborne Radar Theory and Operations
Introduction: Page 29
32. Copyright 2013 R.A. Phillips
References
1) Decoupled Kalman filters for phased array radar tracking: Automatic Control,
IEEE transactions on: Date of Publication: Mar 1983 Author(s):Daum F. Raytheon
Company, Wayland, MA, USA
2) Blinchikoff and Zverev, “Filtering in the Time and Frequency Domain” 1975
3) Rabiner and Gold, Theory and Application of Digital Signal Processing 1975
4) Stimson, “Introduction to Airborne radar” 1998
5) Skolnick “Introduction to Radar” 1995
6) William Skillman “Radar Calculations” Artech House ,1983
7) “Estimation and Control of Systems” Elbert 1984 – Contains all aspects of linear
estimation from least squares to the Kalman filter
9) Antenna Arrays - Randy Haupt IEEE Press
10) SDMS MSTARS Public Data Website https://www.sdms.afrl.af.mil/ Contains 1ft
SAR images of military targets
11) M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans.
Information Theory, vol. 8, no. 2, pp. 179–187, 1962.
12) Radar CFAR Thresholding in Clutter and MultipleTarget Situations Hermann
Rohling AEG-Telefunken, IEEE Transactions On Aerospace and Electronic
Systems VOL. AES-19, NO. 4 JULY 1983 Discusses clutter maps for describing
clutter regions of differing clutter type. Excellent analysis of CA, GO CFAR and
ordered statistic CFAR
AESA Airborne Radar Theory and Operations
Introduction: Page 30
33. Copyright 2013 R.A. Phillips
References
13) MIT Lincoln Lab Journal Archives
http://www.ll.mit.edu/publications/journal/journalarchives.html
Vol 10, Number 2 - 1997
Vol 8, Number 1 - 1995
Vol 6, Number 1 - 1993
Provides overview of the Automatic Target Recognition
and Detection including Super resolution SAR , CFAR’s
and effects of polarization and resolution on recognition
AESA Airborne Radar Theory and Operations
Introduction: Page 31