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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

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

(No Transcript)

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

(No Transcript)

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.

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?

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.

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

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

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.

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.

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.

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.

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).

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

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.

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.

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

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

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

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.

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

Detection Target or Clutter Alone?

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)

Multi-Static Radar Platform

60

Statistical analysis

60

Illumination/detection radar array

Backscatter realizations

Forest Alone Target in Forest

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)

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

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

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

Mixture models Goodness of Fit

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

Centralized Approach to Estimation and Detection

Processing Unit

Decentralized Approach to Estimation and Detection

Processing Unit

A slice of ambiguity/likelihood function

Estimator Realizations

x xx x xx xxxx x xx

Optimal Aggregation Algorithm

Sample Covariance Analysis

Estimation of Gaussian Mixture Parameters (EM

)

Aggregation To Final Estimate

Distributed Sensing Performance Comparisons

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

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

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

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

In Retrospect Posterior Density

x

x

Best action is a2 since its posterior update is

most concentrated ? induces highest information

gain

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

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

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

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

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

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.

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

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

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

2-step Lookahead Non-Myopic Search Tree

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

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

Optimal Action Determined by Partition of

information state space

1

1

0

Special case of 3 state target

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

? 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

Exploration with Particle Proposals

Model Update

Realized Information Gain k1 to k2

Model Update

Expected Information Gain, k2

. . .

ltDagt

ltDagt

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).

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)

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

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.

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

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

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.

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.

The resulting policy defined on information space

P(Exposed targetY)

P(Target absentY)

Gain over myopic strategy drop passive

Detection gain relative to myopic policy never

use passive

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

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

Quick Overview

The Mine Hunter / Killer

Metal Detector Coils

GPR Antenna 19

GPS Antenna

IR Camera

GPR Antenna 0

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

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)

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

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

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

RANGE MIGRATION ALGORITHM EXPERIMENT

Quick Overview

USSR TM-62 LAND MINE

Army NVESD MH/K

Point-spread response

Imaging a single point

RANGE MIGRATION ALGORITHM RESULTS

Quick Overview

TM-62 measured (6 depth)

TM-62 binary reconstructed

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)

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

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

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)

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.

Quick Overview

Ft. AP Hill - TEST and CAL Lanes

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).

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

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?

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

TM-62M Russian Landmine

Firing Pen (Always Metal)

Electromagnetic Induction (EMI)

Electromagnetic Induction (EMI)

Upper Coil

Lower Coil

- Upper Coil Receive only
- Lower Coil Transmit receive

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

EMI Spatial Simulation

3D Metal Detector Data Set DARPA

Backgrounds (1995) Operator Parsons

Engineering System by Geonics

Receivers

Z Coil

Y Coil

X Coil

Coil Current

Sampled Decay Rate

Location SB (Ft. Carson, CO) Transmit

Z Receive Y Target Registration Targets

5

Iron Sphere

Aluminum Plate

- 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.

- Iron objects decay much faster
- than Aluminum Objects.

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

Hyperbola Flattening Transformation

Hyperbola Flattening Transformation

Sampled Form

Hyperbola Flattening Transformation

Hyperbola Flattening Transformation

(No Transcript)

Try this on actual GPR data from mine field

Try this on actual GPR data from mine field

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

Summary of Results Sarabandi

Quick Overview

- Iterative physical optics for shadowing.
- Attenuation estimation in forest canopies.
- Frequency correlation for estimating forest

canopy parameters (trunk thickness, etc.)

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

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

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).

- 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

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.

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.

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

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)

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.

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).

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)

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.

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.

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

Algorithm Flowchart

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.

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,

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.

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.

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.

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.

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)

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

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.

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,

Back Scattered RCS versus tilt angle of the

second leaf

Validation using MoM for d2l (Far-field Method)

Vertical Polarization

Horizontal Polarization

Validation using MoM for dl (two leaves are in

the near field zone of each other)

Vertical Polarization

Horizontal Polarization

Phase of back scattered field versus tilt angle

of the second leaf

dl

Vertical Polarization

Horizontal Polarization

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

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

Backscattering Decomposition Using Fourier

Transform of FCF

.

Simulated Frequency Response of a Tree Stand

Magnitude

Phase

Trunk-ground

FT

Canopy

Frequency Correlation Function

Frequency spacing 2 MHz Number of

realizations 50

Canopy

Ground-trunk

27m2H

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

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

Preparing SAR Image for FCF

Bandwidth to get high resolution Can we extract

FCF from high resultion SARS Sacrifice resolution

for achieving tree structures.

Analyzing SAR FCF Using Small Correlation Windows

High attenuation at X-band does not allow

extraction of tree height and structure

Homogeneous Tree Area

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.

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

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.

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

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.

Sensitivity analysis

- Enhanced SAR Target Detection Methods
- Multi-incidence angle data
- Spotlight SAR
- SAR tomography

Sensitivity to elevation angle

For fixed f 177 the induced current on the

plate is plotted for 3 close q.

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

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

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.

Cross pol Comparison

Level of the xpol from the forest is about 10dB

more than that of the plate.

Backscattering sensitivity to forest realization

Back scattering from forest and plate are highly

sensitive to the forest Realization.

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

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.

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

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.

Iterative PO Approach

Iterative near-field PO approach

Incident field

Plate 2

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