AESA Airborne Radar Theory and Operations Technical Training Course Sampler
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Transcript of AESA Airborne Radar Theory and Operations Technical Training Course Sampler
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Introduction Page 1
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
AESA Airborne Radar Theory and
Operations Course Sampler
Robert A Phillips [email protected]
Introduction: Page 2
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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
Introduction: Page 3
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Objective Number 2
Square law
Detector
Pulse Compress CFAR M of N
Correlate FFT
Clutter Template
Block Diagram for Search mode
Antenna Receive Pattern using a diamond layout with true symmetric Dolph Chebyschev sidelobe weighting (from supplied Radar Theory eBook)
2) Present the theory of an AESA Radar and learn how to design the air-air and air-ground modes from the requirements up
Introduction: Page 4
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Objective Number 3 3) Provide the simulations, tools, and references for “putting the theory into practice”
Win 7 Professional Radar mode design spread sheet with software
"You cannot mandate productivity, you must provide the tools to let people become their best.“ Steve Jobs
500 page interactive electronic book on class material including antennas, Space Time Adaptive Processing, Kalman filters, and automatic target recognition, with simulations & examples
AESA Radar Theory eBook
Introduction: Page 5
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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
Introduction: Page 6
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Sampler
1) Design of an AESA Medium PRF Search Mode
AESA Radar in Medium PRF search
Introduction: Page 7
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
MED PRF Search Block Diagram
Square law
detect CFAR Unfold
Detects
M of N Range
Correlator
M of N Doppler
Correlator
FFT Compress
Target Reports Range, Doppler, Cross Section
Sum Channel
The Block Diagram for a MED PRF Search radar [Skolnick,fig 17.6]
Smallest allowable Target size m2
Skolnick Fig 17.12
Altitude Speed
Clutter Template
[12]
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
Introduction: Page 8
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Clutter Template from Supplied Simulation
Tail aspect Head
The template [12] guides us in choosing a CFAR design
• 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
Introduction: Page 9
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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% Avg Power 471watts Pfa 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
Derived parameters
Introduction: Page 10
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
MED PRF Single Scan Performance
Cross Section 5m2
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
Single Scan PD - Low PRF .VS. Medium PRF
Chart from the mode design spreadsheet using VBA software from the eBook on Detection Theory (supplied with course)
Introduction Page 11
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Sampler
2) How to Track 50 Targets with an AESA Radar
AESA Radar engaging and launching missiles on six targets
Introduction: Page 12
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Vector Tracking Loop
General radar tracking loop
Compute Monopulse Error ∆kANT
Transform To NAV Coords
Transform to ANT Coords
Steering vector k(α,β)
k(α,β) antenna steering
kT(θ,φ) target
Kalman Filter in
NAV
Target Relative Position
Error ∼∆kANT
Ownship position vector in NAV reference
The Σ and ∆ channels are used to compute the error vector ∆k in ANT coordinates
For monopulse vector processing see [9] Haupt and eBook on antennas
Introduction: Page 13
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Three Channel (Az,El,Range) Kalman [1]
( ) 1
For n in 1..3
Gain Computatio
n
T Tn n n nR
−= +K P H HP H
For n in 1..3
State Update
n n= + NavX X K E
( )For n in 1..3
P Update
1n n n= −P K H P
For n in 1..3
Extrapolat
e
n n n n
=
= +
XΦX
P PΦP Q
Where n is one of the 3 orthogonal channels
Rng, Az , El
The lectures will define each matrix in the design
Introduction: Page 14
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Typical Track Performance
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
Angle Error
RMS Velocity Error
Introduction: Page 15
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
AESA Time Line
15 Target track interleaved with search while displaying a SAR image. Room for lots more!!
Introduction: Page 16
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Sampler
3) Space Time Adaptive Cancellers
Introduction: Page 17
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Space Time Adaptive Filters (Stimson, Haupt)
Standoff sidelobe jammer
• 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.
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
Target Gain of AUX
Adaptive Cancellers Stimson [3,Ch 40], Skolnick[4,Ch 9]
Introduction: Page 18
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
The Adaptive Canceller [7] Elbert See also Stimson [3,Pg509]
Sum the weighted outputs of the multiple antennas to cancel the jammer.
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
The space filter is a direct application of linear estimation theory [7]
Main
V
Store samples from each channel in the rows of the
H matrix H=[m a2 a3…an]
The optimal weights X are the 1st column of
the inverse of the covariance matrix
(HTH)-1
AUX2
V
AUXn
V
∑
X1 X2 Xn
Introduction: Page 19
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Example of STAP With Multiple Jammers Example of STAP with 4 Jammers. 4 Aux horns
1 1 2 2 3 3 4 4
The cancelled jammer output equation is:Output=Main+x +x +x +xAux Aux Aux Aux
One 5th order Matrix Inversion and 25 dot products of length 10
10deg 20deg
30deg 40deg
Weighted Sum
Target
1
The optimal weights are:=1st Column of CovarianceMatrix−x
See eBook on Antennas for detailed simulation of multiple jammers
Introduction: Page 20
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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!
After cancellation the target is easily seen in the FFT and the noise is down to 5 quanta
Example from eBook on Antennas
Uncancelled Jammer + Target Cancelled Jammer + Target
Introduction: Page 21
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Sampler
4) Slow Ground moving target indicator Main Beam Clutter Canceller
Introduction: Page 22
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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..
Introduction: Page 23
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Spatial vs Frequency Filtering 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
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
FFT range/Doppler map
Tail aspect Head
The course will describe this essential diagram in detail
Introduction: Page 24
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
θt
Slow Moving Targets and Clutter
θc Angle Space Map
Stationary target at angle θt
Large MBC Clutter at angle θc
Doppler Frequency Space Map
In a space diagram the target and clutter are separable
A Spatial Notch with multiple antennas can remove the clutter
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
Introduction: Page 25
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Slow Mover - Canceller [Stimson Pg321]
Rg x Filter matrix
Rg x Filter matrix
Left Right
A little complicated but very powerful
Get α,β for each FFT
Cell
Get Gain for each FFT Cell
Cancel Clutter
Recompute FFT
rel
The phase for clutter at angle , :
= = sin( )cos( ), G =
Using the canceller equation:
- exp( 2 )
The cancelled clutter for each filter is:e
Leftc
Ri
n
ght
n n
M
A
GdG
GOutput
Cancelled Left Rig
Main Aux jG
ht
α β
πϕ α βλ
ϕ
•
=
−
−
=
R k
xp( 2 )cj ϕ−
Slow moving ground targets
-d/2 d/2
( , )T θ φk ( , )MBC α βk MBC comes from a known angle α,β
The target at the same frequency as clutter
CFAR
Introduction: Page 26
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
S
S
S
ATR Finds 3 S-300 Surface – Air Missile
Launchers with Pd>0.95 in 2 sec Sampler
5) Automatic Target Recognition Target Detection
Bushehr nuclear power plant from Google Maps
Introduction: Page 27
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Automatic Target Detection Outline [13]
Detector uses general target signatures to find “military like” targets
CFAR Detector
Binarize Image
Open/Close Shapes
Compute Moments Statistics
Edit Clutter False Tgts
Get Enhanced Tgt Chips
SAR Targets + Clutter
Detected targets sans clutter
Clumped Detects
Target List
Target Recognition
Clutter Shadow Removal
Data from MSTARS public website, algorithms from Lincoln labs and Mathcad image processing library
Library
Introduction: Page 28
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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
Introduction: Page 29
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
Example Automatic Target Recognition[13] Enhanced M113 Chip from ATD with feature vector consisting of moments, stats and
pose
Feature Vec BTR60 M113 BMP2 BTR70 T72 M109 M2 HMMW M1
Correlation 0.81 1 0.92 0.88 0.87 0.86 .91 .93 .85 Eigenvalues 0.62 1 0.71 0.61 0.73 0.54 .72 .70 .41 Area 0.89 1 1 1 0.81 0.69 .91 .91 .67 Combined 0.45 1 0.66 0.54 0.51 0.32 .59 .59 .23
Comparison of feature vectors for each target in library
3) The highest score is the ID
1) Use the pose to index the library
2) Compute Score for each target in the library using feature vectors
Pose=-30deg
Goo
d M
atch
Library Chips with same pose as detected target
Introduction: Page 30
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
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
Introduction: Page 31
Copyright 2013 R.A. Phillips
AESA Airborne Radar Theory and Operations
References
Provides overview of the Automatic Target Recognition and Detection including Super resolution SAR , CFAR’s and effects of polarization and resolution on recognition
13) MIT Lincoln Lab Journal Archives http://www.ll.mit.edu/publications/journal/journalarchives.html
Vol 10, Number 2 - 1997 Vol 6, Number 1 - 1993 Vol 8, Number 1 - 1995