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Adaptive Multi-sensor Integrated Security System
(AMISS)
August 1998
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Los Alamos National Laboratory 2
AMISS Agenda
• AMISS Overview• Source Detection and Location• Path Tracking Anomaly Detection• Multi-sensor Data Fusion• Shape Recognition and Identification• Reasoning/Data Mining• Remote Operation• AMISS Integrated Exercise
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Los Alamos National Laboratory 3
AMISS Vision
Develop and demonstrate state-of-science adaptive technology to determine potential threats and anomalous situations and ascertain appropriate action to increase facility security, safeguards, and safety.
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Los Alamos National Laboratory 4
AMISS Strategic Objectives
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Los Alamos National Laboratory 5
AMISS Strategic Objectives
• Provide continuous, adaptive real-time detection and categorization of all activity• Assist security personnel• Enhance material movement monitoring• Reconstruct threatening events• Ensure compliance
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Los Alamos National Laboratory 6
AMISS Tactics
• Develop test bed• Learn correct/acceptable facility operating
conditions and detect unusual behavior• Build techniques for active facility• Build portable capabilities • Provide feedback to security personnel
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Los Alamos National Laboratory 7
AMISS Challenge
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Los Alamos National Laboratory 8
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Los Alamos National Laboratory 9
AMISS Architecture
Whole Is Greater Than The Sum Of The Parts
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Los Alamos National Laboratory 10
AMISS Schematic
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Los Alamos National Laboratory 11
AMISS Architecture
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Los Alamos National Laboratory 12
Source Detection And Location
• Long-term vision– Detect, identify, and locate multiple sources
and track movement and activity
• Initial goal– Detect and locate single moving source in a
room
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Los Alamos National Laboratory 13
Source Location Problem
• Detector gives imperfect information• Source moves around room• Inconsistent signals (candle flickers)• Background changes • Detectors are non-directional• Shielding or occlusions
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Los Alamos National Laboratory 14
Source Location At A Glance
• Isocounts as single bands with known source strength
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Los Alamos National Laboratory 15
Simplistic Source Location
• Overlapping isocount Bands gives X,Y,Z and strength
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Los Alamos National Laboratory 16
Source Location Solution• Estimate X, Y, Z background levels• Radiation detection model for rectangular detector
and spherical source• Optimization algorithm
– Kalman Filter– Constrained optimization
• Elements of uncertainty– Counting statistics– Occlusion, shielding– Varying background– Directional detectors
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Los Alamos National Laboratory 17
Source Location Future
• Improve Location Accuracy with known source strength• Locate Multiple Sources • Deduce multiple source strength• Deduce Nuclear Signatures• Decrease uncertainties– Location– Source Strength– Source Material
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Los Alamos National Laboratory 18
AMISS Architecture
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Los Alamos National Laboratory 19
Path Tracking Anomaly Detection
• Automated process using data collection to locate individual movement to detect and determine anomalous behavior patterns
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Los Alamos National Laboratory 20
Path Tracking Anomaly Detection Current Technology
• Unsupervised neural network• Clusters spatial-temporal patterns• Learns individual behavior• Defines normal behavior – X, Y locations in sequence– Broader patterns
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Los Alamos National Laboratory 21
Path Tracking Anomaly Detection
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Los Alamos National Laboratory 22
Path Tracking Anomaly Detection
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Los Alamos National Laboratory 23
Path Tracking Anomaly Detection AMISS Innovative Technology
• Determine anomalies from examples of normal behavior - more secure• Real-time• Provide explanation facility• Learns quickly• Customized by changing sensitivity
parameters
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Los Alamos National Laboratory 24
Path Tracking Anomaly Detection Future• Facility Security
– Identify number of people in a room– Identify open doors– Examine time– Determine expected behavior– Expand to larger areas
• Other domains– Verification of dismantlement activities– Assist IAEA inspections– Ensure unattended monitoring
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Los Alamos National Laboratory 25
AMISS Architecture
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Los Alamos National Laboratory 26
Multi-sensor Data Fusion
• Combining multi-sensor data to provide more accurate, descriptive, and useful information for higher-level reasoning and display
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Los Alamos National Laboratory 27
Multi-sensor Data Fusion Challenges
• Different sensor characteristics– Different data collected – Various accuracy– Various reliability– Different resolutions– Varying speed
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Los Alamos National Laboratory 28
Multi-sensor Data Fusion Current Research• Uses proven graph theory technique,
including edge trimming• Robust sensor suite – Handle sensor failure– Redundancy for level of assurance
• Fuse active and passive infrared and video to identify and locate personnel• Experimental code complete• Evaluation phase starting
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Los Alamos National Laboratory 29
Multi-sensor Data Fusion Future
• Integrate into AMISS • Research other methodologies• Add new sensor types• Framework for future development
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Los Alamos National Laboratory 30
AMISS Architecture
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Los Alamos National Laboratory 31
Shape Recognition and Identification - Initial Concept• Profile Evaluator for image identification
within controlled environment for entry control
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Los Alamos National Laboratory 32
Shape Recognition and Identification - Current Technology
moment p,q = x p y q f(x,y)x,y
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Los Alamos National Laboratory 33
Shape Recognition and Identification - Current Technology
y = W i X i z = tanh (y)
• Neural Net
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Los Alamos National Laboratory 34
Shape Recognition and Identification - Current Results
ERROR RATESActual1 Camera
Estimated3 Cameras
Tailgating .1700 .0049Crouching .0000 .0000Person +Object .0625 .0002Normal .0870 .0007
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Los Alamos National Laboratory 35
Shape Recognition and Identification
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Los Alamos National Laboratory 36
Shape Recognition and Identification Future
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Los Alamos National Laboratory 37
Shape Recognition and Identification - Future
• Detect and identify a complete inventory of objects in a scene– New Feature Extraction - Image Understanding– Better hardware – Post processing classification
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Los Alamos National Laboratory 38
AMISS Architecture
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Los Alamos National Laboratory 39
Reasoning/Data Mining
• ADaptive Virtual Integrating senSOR (ADVISOR)
• Provide continuous, integrated facility status reasoned from real-time and historical data and human input.
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Los Alamos National Laboratory 40
ADVISOR Expertise
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Los Alamos National Laboratory 41
ADVISOR Benefits
• Provides continuous real-time detection of procedure violations and anomalies• Reduce information overload and tedious
data analysis• Consistent rule interpretation and application• Continuous information integration• Continuity of knowledge • Provides detailed explanation capability• Active role, advisory role or combination
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Los Alamos National Laboratory 42
ADVISOR Objectives
• Knowledge engineering to obtain human expert experience • Integrate policies and procedures• Dynamic adaptation• Learn normal facility status
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Los Alamos National Laboratory 43
ADVISOR Components
• Real-time reasoning and control (current)• Data mining • Real-time integrated with data mining
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Los Alamos National Laboratory 44
ADVISOR Decisions
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Los Alamos National Laboratory 45
ADVISOR Decision
Is Joe supposed to move that material in that location?
YES
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Los Alamos National Laboratory 46
ADVISOR Future
• Develop System Health reasoning diagnostics• Expand to multiple buildings and facilities• Countermeasures (e.g. know when being
fooled)• New domain applications• Develop and integrate data mining
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Los Alamos National Laboratory 47
AMISS Architecture
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Los Alamos National Laboratory 48
Remote Operation
• World Wide Web (www)– Remote (e.g. global)– Secure (SSL)
• Remote alarm (e.g. notify guard through pager)• Potential to take action immediately• Operate several facilities
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Los Alamos National Laboratory 49
Remote Operation Capabilities
Shut Door Dispatch Guard
All OK Human hint to confirm ADVISOR Thought
ACTION
ADVICE/STATUS QUERY HUMAN EXPERT
CONFIRM ACTION
• Bringing it all together - remotely
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Los Alamos National Laboratory 50
AMISS
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Los Alamos National Laboratory 51
Potential Application Areas
•MC&A at DOE Facilities•Treaty Verification•IAEA– Remote (unattended) monitoring – Environmental monitoring – Covert and/or underground facilities
•Other – Critical infrastructure– Recent national and international incidents
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Los Alamos National Laboratory 52
AMISS Future
• Confidence versus redundancy• Aging facilities• Robust and hardened• Adaptable, portable, scalable• Address emerging requirements• Define measurable performance measures• Further reasoning development• Integrate to entire facility
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Los Alamos National Laboratory 53
AMISS Experimental Site — TA-18
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Los Alamos National Laboratory 54
AMISS Exercise - Conceptual Demo
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Los Alamos National Laboratory 55
AMISS Exercise - A Day In The Life
• Identify and locate personnel and material• Alert unauthorized activities, material
shielding, and unauthorized movements• Track paths of interest and detect anomalous
behaviors• Provide real-time facility status decisions
based upon all available data sources• Provide security personnel with effortless
facility status view
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Los Alamos National Laboratory 56
AMISS Exercise - A Day In The Life
1. Unlock High Bay– Alarm unauthorized entry
2. Security Sweep High Bay– Alert attempted sweep against protocol pattern
3. Experiment Entry Procedures– Alert improper approvals, personnel, materials,
or schedule
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Los Alamos National Laboratory 57
AMISS Exercise - A Day In The Life
4. Perform Experiment– Alert improper procedures with material– Alarm invalid exit while material shielded
5. Experiment Exit Procedures– Alarm unauthorized material removal
6. Secure High Bay
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Los Alamos National Laboratory 58
AMISS Integrated Exercise
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Los Alamos National Laboratory 59
Partnering To Solve Problems
NN-20Advanced TechnologyPush State-Of-The-ScienceIdentify Gaps & Fill ThemProvide Vision/Path ForwardLeverage OpportunitiesAnticipate Customer NeedsProvide Alternate Methods To Address Current & Emerging Challenges
NN-40Define NeedsIdentify Challenges
NN-50Define NeedsIdentify Challenges
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Los Alamos National Laboratory 60
Differences from Radiation Instrumentation Problems
• Instrumentation imperfections– Moving source within integration interval– Sensor sensitivity decreases with angle and
distance– Minimal detector technology
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Los Alamos National Laboratory 61
Radiation Detection
• Identify “Unusual” background changes• Factors in developing “Decision Rules”– Recognizable source strength– Recognition time– Time between events– Acceptable error rates• False positive• False negative
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Los Alamos National Laboratory 62
Radiation Detectors
• Gamma Detector– Gamma hits cause Fluorescence– Light flashes counted
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Los Alamos National Laboratory 63
THE AMISS TOOLBOX
SensorsVirtualSensors
ExpertSystems
AnomalyDetection
DataMining Brain Interface
videocamera
PIRs
palmreader
AIRs
sourcelocation
pathtracking
objectrecognition
multiplesensorfusion
DOErules
LANLrules
site specificrules
safetyrules
PTAD
multiplepeople
source
eventdriven
database
personelprofiles
SNMprofiles
sensorprofiles
expertsystem
neural network
mixture ofexperts
case-basedreasoning
demo
sitespecific
dialogue
security
Component Communication
each component has equal status and every component can communicate withevery other component
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Los Alamos National Laboratory 64
PTAD
multiplepeople
source
eventdriven
C_API dataformat
PTAD Specific Code
Communication Code
tracker hot_spot
C_API dataformat
source Specific Code
Communication Code
source variance
AMISS COMPONENT COMMUNICATION
Anomaly Detection
each component contains the same communication software
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Los Alamos National Laboratory 65
PIR/AIR
Video
Portal Monitor
Rad Detectors
Bar Code
Palm Reader
Database
PTAD
Face ItRad Tracker Sentry
Expert System
Web Interface
Blob Tracking
DEMO FLOW DIAGRAM
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Los Alamos National Laboratory 66
Expert System
ROOM STATE
Video Tracking
MOTION DETECTION
Sentry
Data Base
PTADFace It
PEOPLE STATE
Rad Tracker
SOURCE STATEACCESS CONTROL
Palm Reader
AIR
Video
PIR
RadDetectors
Bar CodeReader
Web Browser
INTERFACE