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Title: Sequential%20Adaptive%20Multi-Modality%20Target%20Detec-tion%20and%20Classification%20using%20Physics-Based%20Models


1
Sequential Adaptive Multi-Modality Target
Detec-tion and Classification using Physics-Based
Models
  • Professor Andrew E. Yagle (PI) (EECS)
  • Mine detection, channel identification
  • Professor Alfred O. Hero III (EECS)
  • Sensor scheduling, nonparametric statistical
    models
  • Professor Kamal Sarabandi (Director, RadLab)
  • Vehicle and foliage physics-based modeling

2
SEQUENTIAL ADAPTIVE MULTI-MODALITY TARGET
DETEC- TION AND CLASSIFICATION USING
PHYSICS-BASED MODELS
  • APPROACH
  • Develop realistic physics-based models
  • Perform statistical simulations to obtain
    distributions of measured scattered fields
  • Develop sensor scheduling and detection
    algorithms using these statistical models
  • Evaluate algorithms using statistical measures
  • Apply algorithms to real multi-modal data.
  • ARMY COLLABORATIONS
  • Army Night Vision Lab (GPR IR mine field data).

(PICTURE)
  • ACCOMPLISHMENTS
  • Phenomenological studies of radar clutter and
    targetclutter using realistic physics-based
    models
  • Developed non-parametric MRF models for these
  • Developed myopic sequential adaptive sensor
    management algorithm for tracking problems
  • Developed migration (time-reversal) algorithm
    for imaging land mines and evaluated on real GPR
    data.
  • TRANSITION TO ARMY/ INDUSTRY
  • In progress.
  • OBJECTIVES
  • Develop algorithms for detection of landmines
    and tanks under trees using radar and IR
    sensors
  • Develop data-adaptive algorithms for sensor
    scheduling and multi-modal sequential detection
  • Evaluate the algorithms using Monte Carlo type
    simulations on realistic models, and on real
    data.
  • ARMY RELEVANCE
  • Detection of landmines and tanks under trees
    has obvious Army relevance


3
(No Transcript)
4
SM
Reduced Models
Reduced Models
Scenarios/Sensor models
Scenarios/Sensor models
Actual Data
Simulated Data
Performance Matrix
Hybrid simulated/real data
TUT
UXO
Environment Modification
5
(No Transcript)
6
Research Project Objectives
  • Develop overall algorithm for detection of
    Tanks under trees landmines (not structures).
  • Initial focus TUT (can hit the ground running).
  • Algorithm Features sequential detection, sensor
    management selection, physics-based models
  • Evaluate the resulting procedure on realistic
    models (statistical simulations) and real data.

7
Issues Overall Algorithm
  • How to select which sensing modalities to use?
  • What is the value-added for combining other
    modalities? Is it worth the additional cost?
  • How to implement data-adaptive configurations,
    e.g., selection of sources/receivers, based on
    scattering of targets and propagation in medium?
  • How to select decision thresholds for detection?
  • What are the figures of merit for evaluation?

8
Issues Overall Algorithm II
  • ALL of these issues require that we develop
  • statistical models for scattered fields from
    vehicles under foliage and from mines.
  • Development of multimodal target detection
    algorithms take time we need to perform Monte
    Carlo simulations using realistic physics-based
    models.

9
Sequential Adaptive Multi-Modality Target
Detection and Classification using Physics-Based
Models
  • Professor Andrew E. Yagle
  • RAs Jay Marble, Siddharth Shah
  • Professor Alfred O. Hero III
  • RAs Doron Blatt, Chris Kreucher, Raghuram
    Rangarajan
  • Postdoc Cyrille Hory
  • Professor Kamal Sarabandi
  • RAs Mojtaba Dehmollaian, Feinian Wang
  • Research Scientists Leland Pierce, Il-Suek
    Koh

10
Sequential Adaptive Multi-Modality Target
Detection and Classification using Physics-Based
Models
  • Mine detection Yagle, J. Marble
  • Blind Channel Deconvolution Yagle, S. Shah
  • Vehicle modeling Sarabandi, M. Dehmollaian
  • Foliage modeling Sarabandi, I. Koh, F. Wang
  • Sensor scheduling Hero, Kreucher, Blatt
  • Nonparametric statistics Hero, Blatt, Ragarajan

11
HERO 1st-Year Accomplishments I
  • Developed non-parametric statistical modelling of
    scattered fields using Markov random fields.
  • Significance Can model scattered fields from
    a few
  • observations, extrapolating the rest. This
    saves much time in Monte Carlo statistical model
    development.
  • Developed target model reduction technique.
  • Significance Can model vehicles using a
    lower dimensional manifold, simplifying detection.

12
HERO 1st-Year Accomplishments II
  • Developed myopic distributed multi-sensor
    multi-look detection and tracking sensor
    management algorithms using Renyi divergence.
  • Significance Optimal sensor scheduling too
  • hard when many targets and sensors present.
  • Particle filtering Renyi-divergence-based
    scheduling reduces complexity. Tracking of
  • dozens of actual targets was demonstrated.

13
HERO Progress since August
  • Developing non-myopic distributed multi-sensor
    multi-look detection and tracking sensor
    management algorithms using Renyi divergence and
    Q-learning.
  • Significance Can develop useful optimal SM
    approximations and quantify performance-vs-complex
    ity tradeoffs.
  • Developing aggregation strategies for distributed
    sensors and quantifying performance tradeoffs.
  • Significance Allows fast and reliable
    computation of maxima of objective functions
    these dictate strategies.

14
Sarabandi 1st-Year Accomplishments I
  • Performed phenomenological studies of
  • (a) physics-based clutter models
  • (b) physics-based target models
  • Significance Basic understanding of
    effects is vital for interpreting results.
  • These proved very useful in developing the
    statistical models of scattered fields.

15
Sarabandi 1st-Year Accomplishments II
  • New results Target-clutter interaction Multiple
    scattering from needle clusters
  • Closed-form solution for scattering from a
    disk of arbitrary shape.
  • Developed time-reversal method for foliage
    camouflaged target detection.
  • Significance This is one of the
    physics-based models for detection (project
    title).

16
Sarabandi Progress since August
  • Developing iterative frequency-correlation based
    forest radar channel identification
  • New approach for attenuation estimation.
  • Significance Procedure for deconvolving
    effects of propagation through foliage.
  • Developing iterative physical optics approach to
    account for foliage shadowing.
  • Significance Greatly reduces computation

17
YAGLE 1st-Year Accomplishments
  • Developed mine detection algorithm from SAR GPR
    using range migration imaging (with Jay Marble).
  • Significance Physics-based algorithm for
    imaging mines from ground-penetrating radar
    (project title).
  • Developed 2D and 3D blind deconvolution
    algorithms for radar channel identification (with
    Siddharth Shah).
  • Significance Apply to blind deconvolution of
    channel propagation effects for mines, and
    perhaps for foliage.

18
YAGLE Progress since August
  • Developed hyperbola-flattening transform
    algorithm for feature detection in GPR data.
  • Significance Preliminary detection stage
    using less computation than range-migration
    imaging
  • Working on material discrimination using decay
    rates from magnetometer (metal detector) data.
  • Significance Multi-modal mine detection.

19
Synergistic Activities Hero
  • General Dynamics (formerly Veridian, ERIM)
  • C. Kreucher sensor management scheduling
  • K. Kastella sensor management
  • J. Ackenhusen mine detection
  • ARL NAS-SED review panel member
  • ARL N. Patwari (student) summer internship
  • ERIM Int C. Shih (student) summer internship

20
Synergistic Activities Sarabandi
General Dynamics John Ackenhusen BAE Norm
Byer FCS COMMUNICATIONS Jim Freibersiser
(DARPA PM) Barry Perlman (CECOM) ARL Ed Burke
(mm wave), Brian Sadler, Bruce Wallace
21
Synergistic Activities Yagle
  • General Dynamics (formerly Veridian, ERIM)
  • Jay Marble, student (ARO mine research)
  • Brian Fischer, student (Low RCS material design)
  • Chris Wackerman, former Ph.D. student

22
Summary of Results Hero
Quick Overview
  • Statistical distributions and realizations of
    backscatter from a plate in a pine forest, from
    Sarabandis physics-based models.
  • Aggregation (centralization) of sensors data for
    detection and estimation
  • 3. Sequential adaptive sensor management using
    non-myopic strategies.

23
Research Loci(2003)
  • Statistical modeling of forward- and back-
    scatter fields
  • Polarimetric Field Modeling and Reconstruction
    (Hory/Blatt)
  • Adaptive multicomponent Pearson model
  • Markov random field (MRF) model for
    extrapolation/reconstruction
  • Adaptive decentralized detection and
    classification
  • Aggregation strategies for distributed sensors
    (Blatt)
  • Optimal estimator aggregation method developed
  • Quantified and exploited tradeoff between local
    on-board processing and centralized aggregation
  • Sequential adaptive sensor management
  • Non-myopic multi-modality sensor
    scheduling(Blatt/Kreucher)
  • Information-driven non-linear target tracking
    algorithms
  • Markov decision process (MDP) for detecting smart
    targets

24
Detection Target or Clutter Alone?
25
Experiment Plate in Forest of Pine Trees
Randomized tree positions
Trees
Plate
meters
meters
  • 15cm x 15cm x 1cm plate at 1m from ground
  • Plate under forest canopy (10 pine trees)

26
Multi-Static Radar Platform
60
Statistical analysis
60
Illumination/detection radar array
27
Backscatter realizations
Forest Alone Target in Forest
28
Forest Alone and Target plus Forest Histograms
SNR0dB
SNR6dB
Additive and Gaussian
Not Additive or Gaussian
  • 2GHz Spotlight SAR illumination
  • Aggregate of three look angles (azimuth35,45,55,
    elev180)

29
Target and Clutter Return Spatial Distribution
  • I.i.d. forest-alone return over array
  • Target introduces local spatial dependency
  • Spatial dependency decays exponentially fast
  • Dependency model required to capture presence of
    target

30
Q-Q Plot for Gaussianity Testing
Return from forest alone Return from
plate in forest
KS goodness-of-fit P value0.0303
KS goodness-of-fit P value0.9985
31
Target in Forest Marginal Backscatter Density
Non-parametric estimates via multi-component
Gaussian mixtures
  • Results for four corner sub-arrays
  • Gaussian mixture components estimated by ML-EM
    algorithm
  • Number of components adaptively estimated via
    (MML) penalty

32
Mixture models Goodness of Fit
33
Joint Finite Mixture Models For Spatial Dependency
Non-parametric joint density estimation using
Gaussian mixtures - neighboring detector cells
  • Advantages of Adaptive Mixture Modeling
  • Better fit to the empirical statistics than
    previous models
  • Simple ML estimator
  • Well suited for GLRT
  • Power of parametric and flexibility of
    non-parametric approaches

34
Centralized Approach to Estimation and Detection
Processing Unit
35
Decentralized Approach to Estimation and Detection
Processing Unit
36
A slice of ambiguity/likelihood function
Estimator Realizations
x xx x xx xxxx x xx
37
Optimal Aggregation Algorithm
Sample Covariance Analysis
Estimation of Gaussian Mixture Parameters (EM
)
Aggregation To Final Estimate
38
Distributed Sensing Performance Comparisons
39
Implications
  • 50 of the local estimates were aberrant
  • With gt 10 sensors centralized estimates attain
    the CRB
  • Naïve aggregation is 10dB worse than CRB
  • Smart (clairvoyant) aggregation comes within 3dB
    of CRB
  • New method is within 3.5dB of CRB

40
Sequential Adaptive Sensor Management
  • Sequential only one sensor deployed at a time
  • Adaptive next sensor selection based on present
    and past measurements
  • Multi-modality sensor modes can be switched at
    each time
  • Detection/Classification/Tracking task is to
    minimize decision error
  • Centralized decision making sensor has access to
    entire set of previous measurements
  • Smart targets may hide from active sensor

Single-target state vector
41
Sequential Adaptive Sensor Management
  • Progress made on two fronts
  • Non-myopic information-gain strategies for target
    tracking
  • Value function approximation using visibility
    constraints
  • Renyi-Divergence approximation
  • Established link between Renyi info and decision
    error exponents
  • Mitigated computational bottleneck by adaptive PF
  • Coupled vs independent particle partitions for
    tracking multiple targets
  • Exploitation of permutation symmetry
  • Real-time operation demonstrated for tracking gt
    10 real target motions
  • Partially Observed Markov Decision Process
    strategies
  • Developed Q-learning approach to sensor
    management
  • Applied to detecting smart targets

42
Sensor scheduling objective function
  • Action a deploy a sensor, probe a cell at time t
  • Value of taking action a at time t after
    observing

Sensor agility
Prediction
Retrospective value of taking action a
Available measurements at time t-1
43
In Retrospect Posterior Density
x

x
Best action is a2 since its posterior update is
most concentrated ? induces highest information
gain
44
Information-based Value Function
  • Incremental information gained from taking action
    a at time t can be measured by divergence
  • Requires updating posterior distributions of
    future target state given future Z and given
    present Z, resp.,
  • Main issues for evaluation of ED(a,t)Z
  • Computation complexity
  • Robustness to model mismatch
  • Decision making relevance

45
Information Value FunctionAlpha Divergence
  • Properties of Renyi divergence
  • Simpler and more stably implementable than KL
    (a1) (KreucheretalTSP04, SPIE03)
  • Parameter alpha can be adapted to non-Gaussian
    posteriors
  • More robust to mis-specified models than KL
    (KreucheretalTSP04, SPIE03)
  • Related directly to decision error probability
    via Sanov (HeroetalSPM02)
  • Information theoretic interpretation

46
Myopic Target Tracking Application
  • Possible actions point radar at cell c and take
    measurement, c1, , L
  • We illustrate the benefit of info-gain SM with AP
    implementation of JMPD tracking 10 actual moving
    target positions (2001 NTC exercise).
  • GMTI radar simulated Rayleigh target/clutter
    statistics
  • Contrast to a periodic (non-managed) scan same
    statistics
  • Coverage of managed and non-managed50 dwells per
    second

47
Comparison with Other Myopic Managed Strategies
  • Renyi-Divergence method of sensor management
    outperforms others
  • Periodic scan sweeps through all cells and then
    repeats
  • Methods A and B point the sensor where
    targets are estimated to be
  • Method A chooses cells randomly from cells
    predicted to have targets and cells surrounding
    those predicted to have targets
  • Method B chooses cells probabilistically based
    on their estimated target count

48
Multimode Radar Mode and Dwell Point
Selection(Myopic Sensor Management)
  • The information based SM algorithm applies to a
    sensor with multiple modalities.
  • Sensors make a total of L sensing actions each
    (here L16)
  • For each mode SM determines which action
    generates max. expected gain in information.
  • The mode corresponding to the largest expected
    information gain is chosen and used
  • To accommodate switching times (between modes)
    and different time scales required for the modes
    expected Information / time (information rate).

MTI 100mx100m cells (GMTI) measures 49x1
strips Pd.5, Pf 1e-4 Detects moving targets
only FTI 100mx100m cells (SAR) measures 7x7
blocks Pd.5, Pf 1e-4 Detects stopped targets
only ID 100mx100m cells (HRR) measures 3x3
blocks Confusion matrix
True1 True2 True3 Empty Meas 1 0.600
0.200 0.200 0.333 Meas 2 0.200
0.600 0.200 0.333 Meas 3 0.200
0.200 0.600 0.333
49
Toward Real-Time Operation
  • Incorporation of advanced adaptive multitarget
    sampling schemes allows tracking with small
    numbers (a few hundred to a thousand) of
    particles for tens of targets.
  • Modules for particle proposal, weighting, and
    divergence expectations written natively
  • Simulation
  • Multitarget particle filter tracker using
    a-divergence sensor management (a.5).
  • Real targets taken from battle simulations at
    NTC.
  • Number of sensor dwells per time scaled up with
    number of targets.

50
There is a Performance Loss Associated with
Myopic SM
  • Myopic SM computes only one-step ahead
  • Does not incorporate any information past one
    step ahead, even information that may be known
    perfectly
  • Vulnerable to situations which require planning
    ahead
  • Sensor-to-target visibility changing due to
    platform or target motion
  • Detection characteristics of target changing over
    time
  • Non-myopic SM looks ahead multiple steps
  • Computationally difficult to implement exactly
    approximate methods necessary
  • Even two step look-ahead can be of value

51
Non-myopic sensor management Relevant Situations
Sensor Position
Sensor Position
Visible Target
Region of Interest
Region of Interest
Shadowed Target
Extra dwells useful at time 1 not made by myopic
strategy
Time 1
Time 3
Time 4
Time 5
Time 6
52
Non-myopic scheme makes use of this information
Myopic scheme uses only this information
Left Target Measured
One Realization of p(X2Z1) when left target
measured
Right Target Measured
Posterior at t0, P(X0Z0)
Prediction at t1, P(X1Z0)
  • Simple illustration with Non-myopic information
    gain criterion
  • Two targets in two cells
  • At even time instants only one cell is visible

One Realization of p(X2Z1) when right target
measured
53
2-step Lookahead Non-Myopic Search Tree
54
Comparison of Greedy and Non-Myopic (2 step)
decision making
Myopic Target lost 22 of the time
Non-Myopic Target lost 11 of the time
55
General Non-Myopic Strategies
  • Reward at time t for action sequence
  • is
    information state
  • Optimal action sequence
  • Optimal action sequence satisfies Bellmans
    equation
  • Value function

56
Optimal Action Determined by Partition of
information state space
1
1
0
Special case of 3 state target
57
Application to Optimal Sensor Management
  • For discrete measurements and finite horizon (T),
    solution to value equation is linear program
  • Krishnamurthy (2002) exploited this property for
    SM
  • Problems with Krishnamurtys approach
  • Complexity of linear program is geometric in T
  • when number of states is large computations
    become intractable
  • when measurements are continuous value equation
    is non-linear

58
? time t-1 ?
time t ? time t1 ?
  • Impose simple form on scheduling function
    infinite horizon
  • Time invariant function of information state
  • Value Function Approximation

Approaches
  • Exploring depth with particle proposals
  • Optimal allocation of N particles

59
Exploration with Particle Proposals
Model Update
Realized Information Gain k1 to k2


Model Update
Expected Information Gain, k2
. . .
ltDagt
ltDagt
60
Non-Myopic Value Approximation
The Bellman equation describes the value of an
action in terms of the immediate (myopic) benefit
and the long-term (non-myopic) benefit.
Bellman equation
Non-myopic correction under a
Myopic part of V under action a
Value of state
For computational tractability approximate
non-myopic term Where Na(s) is an easily
computed measure of the future benefit of action
a (i.e. an approximate long-term value term).
61
Target Tracking Application Visibility
Constraints
  • Define visibility of cell c at time k as
    Visk(c)
  • Visk(c)0 implies cell not visible and Visk(c)1
    implies cell is perfectly visible.
  • A non-myopic strategy will place extra priority
    on measuring a visible cell that will soon become
    obscured to the sensor.
  • A candidate non-myopic approximation is to
    optimize

Nc( )
Myopic Scheduling (.31s) Brute-force
non-myopic (102.5s) Non-myopic approx.
(.32s)
62
Target Tracking Application Information
Divergence
  • Let denote the expected myopic gain
    when taking action c at time k
  • denote the distribution of
    myopic gain when taking action c at time k
  • Approximate long-term value of taking action c
  • Optimization becomes
  • Gaussian approximation to

63
Model Problem using Value function approximation
  • At initialization, target is localized to a 300m
    x 500m region.
  • GMTI Sensor must search the region for the
    target.
  • Sensor visibility region changes with time.
  • Non-myopic strategy scans regions that will be
    obscured in the future while defering regions
    that will be visible in the future.

64
Q-learning Approach to SM
  • Our results extend Krishnamurthys work
  • Handles continuous measurement space
  • Computational complexity is linear in T
  • Applicable to infinite horizon, e.g. quickest
    detection
  • Smart Targets state transition matrix affected
    by action a
  • Two principal ingredients
  • Using Monte Carlo simulation to approximate
    expectation integrals
  • Performing dimension reduction of information
    state s via function approximation

65
Q-learning Background
  • Main idea (Watkins89)
  • simulate actions and the induced information
    states (measurements)
  • Find the optimal schedules by stochastic
    averaging
  • Q-function defined as indexed value function
  • Algorithm For n1,2,
  • Using
    simulate trajectory
  • Update Q functions according to recursion
  • Repeat until variance of Q-function is below
    tolerance

66
Example SM for Smart Target Detection
  • Three possible target states
  • No target present (static)
  • Target present and exposed at time t
  • Target present and hidden at time t
  • Four possible actions at time t
  • Stop and declare target present or absent
    (stopping time is tT)
  • Defer decision and deploy strong active sensor
  • Defer decision and deploy weak active sensor
  • Defer decision and deploy passive sensor
  • If deploy active sensor target may go into hide
    mode.
  • Goal Deploy sensors so as to minimize time to
    correctly decide target present or absence.

67
Estimated Q-functions
  • Q(s,a) measures value of taking action a at
    information state s.
  • Three sensors available
  • A1 strong active
  • A2 weak active
  • Pa passive
  • Qs learned from 1M simulated trajectories of
    sensor deployments.

68
The resulting policy defined on information space
P(Exposed targetY)
P(Target absentY)
69
Gain over myopic strategy drop passive
Detection gain relative to myopic policy never
use passive
70
Foci for 2004
  • Backscatter models for adaptive detection and
    classification refining sensor performance
    metrics (Pf, Pd, Pid).
  • Adaptive non-myopic sensor scheduling and
    management combining Q-learning and particle
    filtering
  • Time reversal 3D imaging with uncalibrated sensor
    arrays

71
Summary of Results Yagle
Quick Overview
  • Mine detection using ground-penetrating radar
    (GPR) and range-migration imaging.
  • Hyperbola-flattening transform for feature
    detection from GPR mine field data.
  • Active magnetometer (metal detector) for
    multimodal mine detection.
  • OMITTED 3-D blind deconvolution
  • Basis-function-based inverse scattering

72
Quick Overview
The Mine Hunter / Killer
Metal Detector Coils
GPR Antenna 19
GPS Antenna
IR Camera
GPR Antenna 0
73
Quick Overview
Wavenumber Migration Applied to GPR Data
Applied SAR imaging algorithm to GPR data. Able
to estimate size and depth of landmine.
Depth 6 6.4
Height 6 8
Width 13 14
TM-62M Russian Landmine
Actual Estimated
Thresholded
Imaged Data
Original Data
6
14
8
74
Material Discrimination Using Decay Rates
Aluminum and Iron objects can be separated by
their different decay rates.
Pulsed Metal Detector
Iron Sphere
Aluminum Plate
Double Click to Run Movie
DARPA Backgrounds Dataset (1995)
75
Hyperbola Flattening Transform Algorithm
A novel feature for detecting hyperbolic
signatures in GPR data. The Hyperbola Flattening
Transform converts the entire signature into a
point.
Original Hyperbola
Remapping y -gt 1/y
45 Rotation
Radon Transform
Simulation
Simulation
Simulation
Final Location
45 Rotation
Original Hyperbola
Remapping y -gt 1/y
Radon Transform
Final Location
76
1st Year Accomplishments
Wavenumber Migration Applied to GPR Data A SAR
image formation algorithm was applied to GPR
data. The end result was a repeatable estimate
of the size and depth of the landmine. This
info is very useful in eliminating false-alarms
from GPR data based on the known size and
typical burial depths of landmines. Small
clutter objects near the surface should be
especially easy to eliminate given an estimate
of the size and depth. Decay Rates of Metal
Detector Exploited for Object Discrimination Usin
g a pulsed metal detector, swirling currents can
be induced in metal objects. Theses induced
currents will die away in an exponential
manner. By measuring the rate of decay, certain
metals can be identified. Specifically, iron
and aluminum objects can be easily separated as
currents in iron objects decay more rapidly than
aluminum. Typically, metal landmines are high in
aluminum and low in iron content. Novel
Feature Developed for Hyperbola Detection in GPR
Data It has been shown that the hyperbolic
nature of landmine signatures provides great
discrimination capable over false-alarms from
soil layers and surface returns. A feature can
be computed for discriminating hyperbolic
signatures from non- hyperbolic signatures using
a new transformation called the Hyperbolic
Flattening Transform. This technique transforms
the data from a hyperbola into a single point.
The energy contained in this point becomes the
feature that can be utilized in discrimination.
Winter 03
Fall 03
Summer 03
77
Quick Overview
OBJECTIVE
ILLUSTRATION
Determine size and depth of landmines using GPR
as part of a multimodal detection algorithm
APPROACH
ACCOMPLISHMENTS
Range Migration and phase compensation Stoltz
interpolation
Successful detection of Russian mines buried in
field from NVESD MH/K
78
RANGE MIGRATION ALGORITHM EXPERIMENT
Quick Overview
USSR TM-62 LAND MINE
Army NVESD MH/K
Point-spread response
Imaging a single point
79
RANGE MIGRATION ALGORITHM RESULTS
Quick Overview
TM-62 measured (6 depth)
TM-62 binary reconstructed
80
Battlefield Vehicle Prototype
  • Army Night Vision Electronic Science
  • GPR, metal detector, infrared camera
  • Robot arm will mark mine locations with ceramic
    disks (arm is not shown at right)

81
Ground-Penetrating Radar (GPR)
  • Mine Hunter/Killer Designed by BAE
  • Army Night Vision Lab (Fort Belvoir VA)
  • 20 transmit/receive antenna pairs in front
  • 256 frequencies 500 Mhz to about 2 MHz stepped
    by 5 MHz

82
Significance of Hyperbola
  • Avoids false alarms due to clutter and noise
  • Stratified ground appears as straight line
  • Hyperbola indicates real, localized target
  • Hyperbola indicates its depth, as well

83
Active Magnetometer Data
Quick Overview
  • Work in progress at present time
  • Mostly comparing GPR magnetometer
  • Multi-modal data GPR magnetometer
  • Using previously-developed (Jay Marble)
    electromagnetic induction model (1995)
  • Idea Distinguish aluminum from iron using
    induction decay rate (like MRI)

84
Quick Overview
Direction Recap The Mine Hunter/ Killer
utilizes 2 up close sensors
(1) array of ground penetration radar
(GPR) antennae (2)
array of metal detector coils, which are also
called
elecromagnetic induction (EMI) coils.
85
Quick Overview
Ft. AP Hill - TEST and CAL Lanes
86
EMI (6 sensors)
2
3
1
GPR (20 sensors)
1
2
4
3
This is a sample of the data produced by the
system. This is a test lane in Virginia. The
vehicle is moving to the right and the sensor
outputs are vertical. The GPR depths have
been summed into a plan view. The EMI is
showing 3 definite metal objects. The GPR
detects these objects plus a fourth (likely a
shallow, low-metal land mine).
87
Physical Model
By creating simple physical models, the hope
is to generate signatures for cross channel
fusion algorithm development, when actual data
from all sensors does not exist for the same
objects.
Expected GPR Signature
This model is the main progress this period.
This FORTRAN model has been compiled
and integrated into MATLAB using mex.
Predicted Vertical Magnetic Field
Depth inches
Target Info Radius 0.07m (3)
Depth 0.1m(6) Conductivity
1000 Coil Height 0.3m(12)
Along Track m
88
Measured Data
GPR Measured Signature
Depth
Along Track inches
Along Track samples
These signatures were extracted from the
actual MH/K data. They correspond to Target 1
in the upper left corner of the mine lane.
However, a great deal is not known about the
sensors Gain EMI dipole moment How much
current is exciting the coils? How many loops
does each coil have?
89
Raw GPR Signature
Imaged GPR Signature
Depth
Depth
We need a way to measure this value for
the imaging subsection.
Mask For Estimating Size
er 9 (A guess that worked.)
Height 6 Width 13 Depth 6 (to top) Metal
Case
Depth inches
We can get size info by using SAR imaging on
the GPR data.
Russian TM-62M Landmine-
Along Track inches
90
TM-62M Russian Landmine

Firing Pen (Always Metal)
91
Electromagnetic Induction (EMI)
92
Electromagnetic Induction (EMI)
Upper Coil
Lower Coil
  • Upper Coil Receive only
  • Lower Coil Transmit receive

93
EMI Modeling
1
2
  • (1) Current driven through coil generates a
    primary magnetic field.
  • (2) Primary field induces magnetic source in
    metal object. Induced source can be decomposed
    into horizontal and vertical components.
  • (3) Secondary field produced by horizontal and
    vertical induced magnetic sources can be sensed
    at surface.

3a
3b
94
EMI Spatial Simulation
95
3D Metal Detector Data Set DARPA
Backgrounds (1995) Operator Parsons
Engineering System by Geonics
Receivers
Z Coil
Y Coil
X Coil
96
Coil Current
Sampled Decay Rate
97
Location SB (Ft. Carson, CO) Transmit
Z Receive Y Target Registration Targets
5
Iron Sphere
Aluminum Plate
98
  • The decay rates of all iron and
  • aluminum test objects are shown here.
  • The blue objects are aluminum.
  • The red objects are iron.
  • Decay rates here are for the vertical
  • transmit and vertical receive pair.

99
  • Iron objects decay much faster
  • than Aluminum Objects.

100
The Hyperbola Flattening Transform
Quick Overview
  • Feature detection in GPR data
  • Map hyperbolas into spots in feature space.
  • Perform 45 degree coordinate rotation.
  • Perform reciprocal coordinate transform Maps
    rotated hyperbola to a straight line.
  • Use Radon transform to look for lines

101
Hyperbola Flattening Transformation
102
Hyperbola Flattening Transformation
Sampled Form
103
Hyperbola Flattening Transformation
104
Hyperbola Flattening Transformation
105
(No Transcript)
106
Try this on actual GPR data from mine field
107
Try this on actual GPR data from mine field
108
Present Work on Landmines
  • Issue detection performance post-migration
    (easier to look for parallel straight lines) vs.
    detection performance w/pre-migration data
    (harder to look for hyperbolae, but apply to raw
    data before migration processing)
  • Issue develop statistical physics-based model
  • Issue how to combine with other modalities

109
Summary of Results Sarabandi
Quick Overview
  1. Iterative physical optics for shadowing.
  2. Attenuation estimation in forest canopies.
  3. Frequency correlation for estimating forest
    canopy parameters (trunk thickness, etc.)

110
Sequential Adaptive Multi-Modality Target
Detection and Classification Using Physics-Based
Models
K. Sarabandi, M. Dehmolaian, F. Wang, T. Benjamin
Radiation Laboratory The University of Michigan,
Ann Arbor, MI 48109-2122 saraband_at_eecs.umich.edu
111
Phenomenological Study
Physics-Based Scattering and Propagation Modeling
of Forest and Embedded Targets
  • Forest is a complex random medium composed of
    lossy scatterers arranged a semi-deterministic
  • Foliage cause significant attenuation,
    scattering, field fluctuation
  • Target is in the close proximity of many
    scatterers (strong field fluctuations and phase
    front distortion)
  • Signal level, fluctuations, polarization state,
    impulse response, spatial coherence etc. depend
    on Tree density, type, height, and structure
  • Military targets are usually large and
    structurally complex
  • Significant multiple scattering and shadowing

112
Electromagnetic Scattering Simulation of Hard
Targets Embedded in Foliage
  • Objectives
  • To develop an accurate EM model for forest stands
    to allow performance assessment of radar sensors
    and target detection algorithms.
  • Determination of foliage channel, RCS of clutter,
    target signature in foliage
  • Examination of different modalities (f, p, q) on
    target/foliage signature.
  • Challenges
  • Hard target and clutter constitute a
    computationally very large problem.
  • Target and clutter are structurally complex
    (features vary from small to very large objects).

113
  • Forest Model
  • Arbitrary fractal tree structures
  • Discrete coherent scattering model
  • First-order uniform near-field/far-field
    calculation inside and outside forest
  • Target Models
  • Full-wave (MoM, FDTD) computationally
    inefficient, good for flt300 MHz
  • Approximate solution (GO ray tracing, PO)
  • Target-Foliage Model
  • Low frequencies (flt100 MHz) Full-wave methods,
    Scattering from foliage can be ignored
  • Mid-frequency range (flt1 GHz) Hybrid FDTD and
    the forest code
  • High frequency (fgt1GHZ) Hybrid PO and improved
    forest code

114
Tasks Under Phenomenological Studies
Forest Model
Target Model
Hybrid Forest/Target Model
Forest parameter estimation
  • Enhance model accuracy
  • Improve computational efficiency
  • Improve range of validity of models
  • Provide simulated data to SM team
  • Work with reduced models to improve computation
    time.

115
Progress
  • Forest Model
  • Accurate estimation of attenuation rate for
    near-grazing incidence (long distance
    propagation)
  • Efficient method for inclusion of multiple
    scattering
  • Forest Parameter Estimation
  • Application of frequency correlation function
    (FCF)
  • Hybrid Target/Foliage Model
  • Direct computation of forest scattered magnetic
    field.
  • Implementation of iterative PO to efficiently
    account for target shadowing and double bounce
    effects on the target.

116
Accurate Estimation Long-Distance Signal
Attenuation in Foliage
Forest Model Improvement
  • Issues related to direct wave propagation over
    long distances in foliage
  • A novel model for accurate predication of signal
    attenuation based on a renormalization approach
  • Estimation of forest block statistical parameters
    using a numerical approach
  • Overall signal estimation using a network theory

117
Estimation of Path-lossin dense random media
  • Experimental data indicates signal attenuation
    with distance shows a nonlinear behavior with
    distance
  • Path loss is usually computed from Foldys
    approximation (single scattering, far-field
    approximation)
  • Overestimation of attenuation rate
  • Significant error over long distances
  • Signal attenuation
  • a - absorption
  • b - scattering loss
  • c scattering gain (multiple scattering)

118
Statistical WAve Propagation (SWAP) Model
A Hybrid Statistical and Wave Theory Approach 1-
Statistically homogeneous forest properties can
be used to localize the field computation.
  • A forest environment can be divided into
    statistically identical blocks along the
    direction of wave propagation.
  • Each block of the forest can be considered as an
    N-port network with similar statistical
    properties.
  • Once the input-output relation is determined, it
    can be used in a network approach to find the
    forest channel path-loss.

119
SWAP Model
2- Break received power into coherent and
incoherent components.
jth block
Rx
  • Received field contains mean and fluctuation
    components, received power contains coherent and
    incoherent components.
  • Coherent power comes from the mean field which is
    the incident wave attenuated by the effective
    forest medium (Foldys approximation).
  • Incoherent power comes from the fluctuation
    field, which contains the contribution from
    scatterers within each block of forest (assuming
    the blocks are statistically independent).

120
SWAP Model
3- Determine the input-output relationship of a
typical block.
Input
Output
Elementary currents computed from fluctuating
fields
Field components computed from the coherent
forest scattering model for each pixel
A forest block made up of many statistical
fractal trees with random location
  • Assuming spatially uncorrelated input for
    fluctuating fields and using Monte Carlo
    simulation find the output mean-field and
    standard deviation (fluctuating field)
  • Repeat the same procedure for a plane wave
    illumination (mean-field incident)

121
One Block Simulation
z
y
  • Single scattering theory
  • plus Foldys approximation
  • Coherent mean field incident at each individual
    scatterer generates scattered field at the
    observation point which is then coherently added.
  • Monte-Carlo simulation
  • Randomly distribute the tree locations to
    simulate the statistical properties of forest.

x
Note Considering the statistical homogeneity
along y-dimension, only a line of observation
points along z-dimension are selected. The
spacing is half-wavelength for accurate
estimation of statistical parameters.
122
Desired Statistical Parameters for Estimation
  • Variation of fluctuation field
  • Spatial Correlation function
  • Foldys attenuation coefficient
  • Input-output relationship transmission matrix

Assumption statistical properties of forest
depend on the forest itself, not of the
excitation, therefore planewave incidence is
chosen for simplicity.
Note the estimation is conducted within one
representative block of forest and the results
are reused for any blocks.
123
Spatial Correlation Function
  • C1(?y), C2(?y) are the spatial correlation
    functions along a horizontal line at two vertical
    points.
  • C3(?z), C4(?z) are the spatial correlation
    functions along a vertical line at two horizontal
    points.
  • C1(?y) and C2(?y) are very similar due to the
    statistical homogeneity of forest along
    horizontal dimension.
  • C3(?z) and C4(?z) are much different since the
    vertical structure of the forest is not
    homogeneous.

Along vertical direction
Along horizontal direction
124
Algorithm Flowchart
125
Computation of Incoherent Power
  • Radiation from the output surface of the jth
    block is computed using the field equivalence
    principle. Only the fluctuating component is
    considered.
  • Ground effect is taken into account by using
    image theory.
  • Surface fluctuation field beyond the forest
    dimensions (i.e. the broadening effect) can be
    neglected.

126
Computation of Incoherent Power, ctd.
  • Incoherent power radiated from the jth block of
    forest to the receiver.
  • Stationary phase technique can be applied for the
    integration along y-direction due to the
    statistical homogeneity along that direction.

where,
127
SWAP Model Validation
Fractal pine trees generated Tree height 8m,
Trunk height 1.2m
  • Three sets of simulations are performed
  • Model validation (comparison between numerical
    foliage model and SWAP model)
  • SWAP model simulation of signal attenuation at
    different frequencies
  • SWAP model simulation of signal attenuation for
    different tree densities at 500 MHz.

128
Model Verification
  • Comparison between numerical foliage model and
    SWAP model
  • Frequency 0.5 GHz, Tree density 0.05/m2
  • Observation point height 1.5m, distance from
    forest edge 1m
  • SWAP model is reasonably accurate compared to the
    single scattering model.
  • Dual-slope phenomenon is clearly observed from
    the SWAP model simulation result.

129
Simulation Results
  • SWAP model applied to same forest at different
    frequencies
  • Tree density 0.05/m2, Forest range up to 500m
  • Observation point height 1.5m, distance from
    forest edge 10 m
  • Dual-slope phenomena are observed at all
    frequencies.
  • The knee point occurs at shorter distance as f
    increases due to higher incoherent power.
  • Attenuation rate of the mean field is increasing
    with f.
  • Scattering power is increasing with f. Incoherent
    power tends to dominate the field after the knee
    point.

130
Simulation Results (III)
  • Different Tree Densities
  • Frequency 0.5GHz
  • Observation point height 0.75m, distance from
    forest edge 10 m
  • Dual-slope phenomena are observed at all tree
    densities.
  • The knee point occurs at shorter distances as
    tree density increases.
  • Higher tree density causes more attenuation
    effect on the coherent power but gains more
    incoherent power which dominates after the
    slope-turning point.

131
Conclusions
  • The SWAP model efficiently includes effects of
    scattering in foliage attenuation.
  • The model for all single scattering effects.
  • The model accurately predicts the change in
    attenuation rate as a function of distance as
    observed in measurements.
  • Future improvements
  • Including multiple scattering among scatterers
    within one block
  • Improve the calculation of mean field at the
    output surface of each block by considering the
    scattered field from scatterers within adjacent
    blocks (both forward and backward)

132
Forest Model Enhancement
  • At High frequencies the effect of multiple
    scattering among tree components become important
  • To account for all multiple scatterings the
    simulation becomes computationally intractable,
    however the interaction up to the second order
    seems to be sufficient .
  • Efficient methods for inclusion of multiple
    scattering
  • far-field method
  • Near-field method

133
Second Order Scattering
Objects are in the near field of each other
Apply Reaction Theorem
The incident field induces a current density
on the particle 1 in the absence of particle 2.
is the near-field scattered field from
particle 2 when it is excited by an
infinitesimal current source along at the
observation point.
is the first plus second order scattered
field from particle 1.
134
Complete Second-order for two broad leaves
Using the VIPO approximation the far field
expression for the scattered field from a
circular disk is,
As the leaves get near to each other the exact
near field expression for the scattered field is
used,
135
Back Scattered RCS versus tilt angle of the
second leaf
Validation using MoM for d2l (Far-field Method)
Vertical Polarization
Horizontal Polarization
136
Validation using MoM for dl (two leaves are in
the near field zone of each other)
Vertical Polarization
Horizontal Polarization
137
Phase of back scattered field versus tilt angle
of the second leaf
dl
Vertical Polarization
Horizontal Polarization
138
Estimation of Forest Channel Parameters From The
Frequency Correlation Function of Radar
Backscatter
  • Goal Need to remove the effects of foliage from
    the target signature for target detection and
    identification (Electromagnetic defoliation in a
    statistical sense)
  • Require Parameters
  • Tree height
  • Foliage attenuation rate
  • Volume scattering
  • Ground reflectivity

139
Theoretical Formulation
FCF of a Homogeneous Foliage Layer above a Ground
Plane
  • Consider a uniform distribution of scatterers
    above a dielectric ground causing attenuation
    and volume scattering.
  • Effective propagation constant , scattering per
    unit volume

Indirect term
Clutter-ground term
Ground-clutter term
Direct term
Radar
z
Volume Scattering
d
Ground
140
Backscattering Decomposition Using Fourier
Transform of FCF
.
141
Simulated Frequency Response of a Tree Stand
Magnitude
Phase
Trunk-ground
FT
Canopy
142
Frequency Correlation Function
Frequency spacing 2 MHz Number of
realizations 50
Canopy
Ground-trunk
27m2H
143
Simulation Data
Tree trunk
Simulation of 50 trees over 500 MHz
f (GHz)
Tree canopy
Realization
Realization
Simulated data contains tree trunk, tree
canopy, and noise floor
144
Choose SAR Data Similar to Simulated Data
X-band SAR image (B500 MHz)
? Range
Tree canopy, ground, and noise floor (this
resembles the simulated data)
Azimuth ?
Ground only
Perform similar FCF analysis on these two
SAR patches
145
Preparing SAR Image for FCF
Bandwidth to get high resolution Can we extract
FCF from high resultion SARS Sacrifice resolution
for achieving tree structures.
146
Analyzing SAR FCF Using Small Correlation Windows
High attenuation at X-band does not allow
extraction of tree height and structure
Homogeneous Tree Area
147
Overview of High-frequency Model
Hybrid Target/Foliage Model
  • Calculate scattering from the target inside a
    forest using PO approximation
  • Valid for targets large compared to l and in
    specular directions
  • Forest scattering at high f is significant,
  • hence the target is illuminated from all
    directions.
  • Independent of observation point there will be
  • many specular contributions.
  • Process
  • Calculation of field distribution on the
    scatterer using the coherent forest model.
  • Based on these calculated fields derive PO
    currents on the target.
  • Apply the reciprocity theorem to calculate
    scattered field from the target that includes the
    effects of trees.

148
Hybrid Target/Foliage Model
Calculation of PO currents requires
Z
Y
Complex near to far-field expressions of forest
code provides
X
The code is modified to calculate directly
speed upgt2
Example Tree trunk near-field
Frequency 2 GHz X 50 l7.5m Observation
Height 1 m Trunk Height 5.64 m Trunk diameter
20 cm
Trunk Height
Observation Height
149
Magnetic Near-Field using the old and new methods
V-Pol. Incidence
H-Pol. Incidence
H
H
Distance 4.2 m
Distance 4.2 m
Time to Run a simulation for lXl plate behind a
10 trees is approximately 3.125 times faster.
150
Hybrid Target/Foliage Model
Dimension of Computational Domain
80lX100lX100l Number of scatterers excuding
needles gt 50,000
Sensitivity analysis
Frequency 2GHz Number of Trees 10
Simulation Scenario
Incident direction
Z
Y
q
3 l
3l
f
X
Height 1m
151
Sensitivity of the electric current on the plate
to the forest realization
  • 37
  • f 177

Realization 2
Realization 1
Realization 3
The electric current induced on the plate is
highly sensitive to the arrangement of
trees. Scattering from nearby trees is very
significant.
152
Sensitivity analysis
  • Enhanced SAR Target Detection Methods
  • Multi-incidence angle data
  • Spotlight SAR
  • SAR tomography

153
Sensitivity to elevation angle
For fixed f 177 the induced current on the
plate is plotted for 3 close q.
154
Calculation of backscattering Using Reciprocity
Elementary source at the excitation point
Field computation inside forest
Induced current calculation on the scatterer
Scattered field at the excitation point
Apply reaction theorem
No need for computation of scattering from forest
155
Backscattering sensitivity to Azimuthal and
elevation angles
Clutter
Plate
  • Backscatter
  • from plate
  • fluctuates more
  • along the elevation angle than the
    azimuthal angle.
  • Backscattering is sensitive to
  • forest realization, elevation and azimuthal
    angles.

Clutter
Plate
156
Backscattering for different elevation
azimuthal angles for 2 different realizations
f
f
q
q
f
f
q
q
Note Fluctuation along the elevation angle is
more than that along azimuthal angle.
157
Cross pol Comparison
Level of the xpol from the forest is about 10dB
more than that of the plate.
158
Backscattering sensitivity to forest realization
Back scattering from forest and plate are highly
sensitive to the forest Realization.
159
Another Example 3D Box
For 3-D objects the lit and shadow area from all
scatterers in forest must be identified
POGO Approach PO current estimation GO
shadowing
Direct Wave
Shadow for reflected
Lit for direct
Reflected Wave
Shadow for direct
Lit for reflected
Direct is shadowed if
Ground Plane
160
Simulation Results
Z
Freq 2 GHz 10 Pine trees
  • 30 Degrees
  • 0 Degrees

Y
3l
Two view of the box
3l
3l
Height 1m
X
Ground Plane
Note Direct Incident field has strong effect on
the level of current.
161
Backscattering plots versus elevation angle
  • Freq 2 GHz
  • Pine Trees
  • Target Metallic Box
  • f 0 Degrees

svv
Note Level of backscattering from the box Is
comparable to that of the forest.
shh
shv
162
Complex Objects
  • For complex objects GO-PO Solution becomes
    intractable
  • Estimation of shadowing is difficult, the
    algorithm is very complex and becomes the
    bottleneck in the scattering computation
  • For each forest scattere and for each observation
    point, shadowing should be estimated.

Incident wave
Shadow
Lit
Very complicated algorithm for an arbitrary
object.
163
Iterative PO Approach
Iterative near-field PO approach
Incident field
Plate 2
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