Utilization of advanced clutter suppression algorithms for ... · Physical Sciences Inc. Motivation Detection of threat R/N sources in moving cargo is difficult due to the need to

Physical Sciences Inc. 20 New England Business Center Andover, MA 01810

Physical

Sciences Inc.

Bogdan R. Cosofret, Kirill Shokhirev and Phil Mulhall

Physical Sciences Inc., Andover MA

[email protected]

David Payne, Bernard Harris and Richard Moro Raytheon Integrated Defense Systems, Tewksbury MA

2013 IEEE Conference on Technologies for Homeland

Security (HST ’13)

12-14 November 2013

Utilization of advanced clutter suppression

algorithms for improved spectroscopic

portal capability against radionuclide threats

ACKNOWLEDGEMENT:

This work has been supported by the US Department of Homeland Security, Domestic Nuclear Detection

Office, under competitively awarded contract/IAA ‎HSHQDC‎-‎10‎-‎C‎-‎00171 and HSHQDC-11-C-00117. This

support does not constitute an express or implied endorsement on the part of the Government.

VG13-158

Approved for Public Release

Physical Sciences Inc.

Agenda

Motivation and General Objectives

Overview of PCS Algorithm and

Optimization for ASP

Experimental Setup

Results

Conclusions

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Motivation

Detection of threat R/N sources in moving

cargo is difficult due to the need to

acquire spectra at short integration time

– Low SNR regime

– Poisson noise and clutter mask weak threat

signals

Current systems: PVT-based RPM and

NaI-based ASP

Advanced Spectroscopic Portal

Capability gaps:

– RPMs are sensitive, cost effective, but lack energy resolution necessary for

threat ID high false warning rates that require secondary screenings

– PVTs have discrimination capability, but are expensive reduced sensitivity

imposes limits on how fast traffic moves through portal

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General Objectives

Improve overall performance of current ASP systems using

advanced algorithms for noise and clutter suppression

Demonstrate the achievement of ASP Key Performance Parameters

under improved and cost effective operational capability:

– Utilization of only 4 out of 12 NaI detectors‎currently‎integrated‎with‎RTN’s‎

ASP units

– Vehicle speeds through the portal in excess of 20 mph (> 6x improvement

over current throughput)

ASP Key Performance Parameters targeted:

– False alarm rate of 1 in 1000 occupancies

– Pd,ID > 90% for weak activity sources (< 10 µCi)

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Poisson/Clutter Split Model (PCS): Conceptual Approach and ASP Optimization



GLRT Methodology for Threat Detection

PCS algorithm is based on the GLRT framework, where the

background is estimated within the Poisson/Clutter model

Background Estimation Data Set:

Background-only Spectra

Test Spectra:

Background + (Threat Signal)

GL

RT

Fra

me

wo

rk

Algorithm

Statistical

Model

Likelihood of H0

(no threat)

Likelihood of H1

(threat present)

Likelihood

ratio

Detection

and ID

CFAR

threshold


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PCS Model: Separation of Poisson and Clutter Noise

The variability among background radiological spectra can be attributed to

two mechanisms:

– Background clutter, i.e. the changes of the energy-dependent count rate due to

variations in isotopic composition depending on particular environments, weather

conditions, etc.

– The random process of radioactive decay, described by Poisson statistics

The key innovations behind the Poisson Clutter Split (PCS) algorithm are:

– The use of a novel probabilistic representation of radiological backgrounds

– Accurate modeling of gamma counts based on Poisson statistics

– The use of the Generalized Likelihood Ratio Test (GLRT) to simultaneously perform

detection and identification of sources.

PCS algorithm’s non-linear probabilistic model provides a better

characterization of the radiological environment than traditional linear

methods

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PCS Model: Separation of Poisson and Clutter Noise

Observed counts x, obey Poisson statistics corresponding to the local background

rate, b, and the integration time, Δt.

PCS calculates the mean rate as a function of energy and the dominant modes of

spectral variations, zk, observed across sampled environments:

The underlying rate, b, can be accurately parameterized with a limited number of

coefficients which determine the spectral variability of the rate:

– Clutter is reflected in the varying parameters, w

– zk capture the spectral features of the environment

In the presence of a radioactive source, the background rate, b , is elevated by an

energy-dependent contribution from the source:

)(~ tbPxb

1 x D vector, D is the number of channels

)},,..,({ 1 wzzbb k

),..,( 1 Kwww

),()( wswbsb

)()( wfwbpbp

f(w) is the probability distribution

of the clutter parameters

)(~ tPxTest

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PCS-based Background Estimation and

Threat Detection and Identification

Estimate the background (train) in single or multiple environments

N background spectra, { xi }, i = 1,..,N,

Estimate the modes of spectral variability and find parameter combinations, for each spectrum

Fit a probabilistic model to the distribution of w’s

Kkzk ,..,1,

distribution‎of‎w’s

Detection and ID: analyze new spectrum x

Given new spectrum x, maximize likelihood under two hypotheses:

H0: x is generated from a rate consistent with the background model

H1: x is generated from the rate consistent with the estimated background

spectrally perturbed by a threat isotope

Alarm and ID if likelihood ratio exceeds threshold

iw

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PCS Optimization for ASP

PCS background model developed using

previous ASP field data

– Background model developed for two operational modes:

1 sec and 0.5 sec integration time

– Spectra from 60 empty occupancies were used to create

background model

PCS CFAR threshold determination (Objective

1 in 1000 occupancies):

– 1000 available no-source occupancies contain ~200,000

1/10 sec live time spectra

– Processed spectra through PCS and analyzed results

– Set isotope specific CFAR threshold to be the highest PCS

signal value recorded under the 1000 observed occupancies

16 isotopes included in the PCS spectral library

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PCS Integration with ASP

Real-time PCS software installed on

Windows laptop

Laptop connected to ASP database

server which resides on an

Ethernet backbone

32-bit API allows calls across the

Ethernet backbone to pull spectra

and packet sequence number (PSN)

from database

Groups of 5 PSN were accumulated

to generate 0.5 sec integration time

spectra for ingestion into PCS

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Experimental Setup



Low Cost Identifier Portal (LCIP): Only 4 out 12 ASP detectors used: Aa3, Ba3, Ca1, Da1

96”

69” Aa1

Aa2

Aa3

Ba1

Ba2

Ba3

Da2

Da3

Ca1

Ca2

Ca3

Da1

NaI

Detectors

(4”x2”x16”)

Neutron

Detector

2.30m

2.36m

Speed (MPH)

Observation time (sec)

5 2.6

10 1.3

20 0.65

30 0.43

Spectra acquired at 0.5 second integration time

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Experimental Parameters

Vehicle: PSI Truck

Check sources emplaced inside truck:

– (1x) Cs-137 (8 µCi)

– (1x) Ba-133 (7 µCi)

– (1x) Co-57 (4 µCi)

Interferant: three 50 lb salt bags (~ 40 µCi of

K-40 signal)

Shielding:

¼” steel cap

– 30% reduction in peak

count for Cs-137


count for Ba-133


count for Co-57

Salt bags

Steel Cap

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LCIP Evaluation: Check Source Locations Inside Vehicle

12'

Salt Bags

Shielding (0.25” steel cap)

(when used)

29” 49”

40” 3x50 lbs

Salt bags

Source Locations

Positions for Cs-137 and Ba-133 used

during multi-source runs. Co-57 was

located on second stand

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Truck Through Portal at 30 mph

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Background Only (30 mph) 150 lbs of Salt Inside the Truck

Several runs through the portal were conducted without sources present

– Weak PCS responses observed for all 16 isotopes in the library

– No PCS responses exceeded the CFAR (1 in 1000 occ) isotope specific thresholds

– No false alarms were reported

Continuous acquisition of spectra over ~ 2 hrs also yielded no false alarms

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Truck with Unshielded Cs-137 (8 µCi) + Salt Vehicle Speed: 30 mph

PCS results with 4/12 NaI detectors: Pd,ID = 95% against unshielded Cs-137

at 30 mph (detected/ID in 18 out of 19 runs), CFAR = 1 in 1000 occ.

No false alarms or mis-identifications were reported

Note: Standard ASP software using with all 12/12 NaI detectors yielded

Pd,ID = 10% (2 out of 19 runs)

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Truck with Shielded Cs-137 (attenuated 8 µCi) + Salt Vehicle Speeds: 20 and 30 mph

PCS results with 4/12 detectors: Pd,ID = 93% at CFAR of 1 in 1000 occ.

against shielded Cs-137 at 20 - 30 mph (detected in 14 out of 15 runs)


Note: Standard ASP software with all 12/12 ASP detectors yielded Pd,ID = 0%

20 mph 30 mph

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Truck with Unshielded Ba-133 (7 µCi) + Salt Vehicle Speed: 30 mph

PCS results with 4/12 NaI detectors: Pd,ID = 93% against unshielded Ba-133 at 30

mph (detected/ID in 14 out of 15 runs), CFAR 1 in 1000 occ.

No false alarms or mis-identifications reported

Ba-133 presence leads to correlated I-131 PCS responses, but not strong enough to

exceed the I-131 isotope specific threshold. Cross-talk also addressed using

Dominant PCS

Note: Standard ASP software using all 12/12 NaI detectors yielded Pd,ID = 0%

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Truck with Shielded Ba-133 (attenuated 7 µCi) + Salt Vehicle Speed: 20 mph

PCS results with 4/12 NaI detectors: Pd,ID = 86% against shielded Ba-133 at

20 mph (detected/ID in 12 out of 14 runs), CFAR 1 in 1000 occ.


Note: Standard ASP software using all 12/12 NaI detectors yielded Pd,ID = 0%

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Truck with Unshielded Multiple Sources

(Co-57, Ba-133, Cs-137)

Vehicle Speed: 20 mph

PCS Results with 4/12 NaI detectors: Pd,ID(Cs-137) = 100%, Pd,ID(Ba-133) =

100%, Pd,ID(Co-57, 4 µCi) = 85% at 20 mph when all sources inside the truck

Note: Standard ASP software using all 12/12 NaI detectors yielded Pd,ID

(Cs-137) = 30%, Pd,ID (Ba-133) = 0%, Pd,ID (Co-57) = 0%

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Conclusions



Conclusions and Next Steps

Demonstrated Pd,ID = 100%, CFAR = 1 in 1000 occupancies at

20 mph

Successfully demonstrated Pd,ID > 90%, CFAR = 1 in 1000

occupancies at 30 mph

Successfully demonstrated isotope identification/discrimination

capability with no reported false alarms or mis-identifications

Demonstrated the ability to detect shielded check sources

Next Steps: Integrate C version of PCS (demonstrated

< 100 msec/spectrum processing time with 28-isotope library)

VG13-158 -23


Documents

Utilization of advanced clutter suppression algorithms for ... · Physical Sciences Inc. Motivation Detection of threat R/N sources in moving cargo is difficult due to the need to