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Physical Sciences Inc. 20 New England Business Center Andover, MA 01810 Physical Sciences Inc. Bogdan R. Cosofret, Daisei Konno, Dave Rossi, William J. Marinelli and Pete Seem Physical Sciences Inc., Andover MA [email protected] 2014 SPIE DSS 6-8 May 2014 Dynamic 3-D chemical agent cloud mapping using a sensor constellation deployed on mobile platforms ACKNOWLEDGEMENT: This work has been supported by the US Army RDECOM, under competitively awarded contract W911SR-11-C-0085. This support does not constitute an expressed or implied endorsement on the part of the Government. VG14-051

Dynamic 3-D chemical agent cloud mapping using a sensor

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PowerPoint PresentationPhysical Sciences Inc. 20 New England Business Center Andover, MA 01810
Physical
William J. Marinelli and Pete Seem
Physical Sciences Inc., Andover MA
[email protected]
using a sensor constellation deployed
on mobile platforms
ACKNOWLEDGEMENT:
This work has been supported by the US Army RDECOM, under competitively awarded contract
W911SR-11-C-0085. This support does not constitute an expressed or implied endorsement on the part
of the Government.
– Performance characteristics
Field Measurements and Results
– BioWeek 2013
value to the mission of chemical early
warning contamination avoidance:
identification from multiple sensors
duration of the cloud life
– Provide real-time 3-D threat concentration
profiles yield in-depth knowledge of the threat’s
absolute geo-location, 3-D extent and
composition
tactics and CONOPs
Operational approach is based on current squad NBCRV CONOPs: first
and last vehicle traveling in convoy, 500 m – 1 km separation between
vehicles
– Ability to extract information about the chemical threat is limited by 2-sensor
geometries and to a lesser extent by the system’s pointing and positioning
accuracy
– Accurate information about total mass, COM location/track, and threat bounding
can still be extracted when pointing uncertainty is less than 1 degree
– Accurate (< 20% error) 3-D threat reconstruction profiles generate with at least
3 sensor and pointing uncertainty < 0.4 degrees
Each sensor platform operates independently, controlled by local user
Tomography Station (co-located with one sensor platform) passively
collects data from sensors
tomography station performs threat reconstruction
VG14-051
Sensor NESR µW/cm2srµm 2 – 3 (wavelength dependent)
Spatial Resolution meters 10 m @ 5 km
Temperature uncertainty in radiometric calibration degree K 0.3
Sensor positioning uncertainty meters 2.5
Sensor pointing/bearing uncertainty degrees 0.5
Band to band image registration capability pixel Image registration for on-the-move operation
was not incorporated in MTMS prototype
Ambient temperature measurement uncertainty Degree Kelvin ~ 1
Full Tomographic Reconstruction Capability n/a yes
Advanced triangulation capability n/a yes
Data products n/a
Cloud propagation direction
Total mass estimate
User selected option for detailed 3-D
concentration distribution
Visualization Platform n/a Map with Embedded Threat Info and offline
Google Earth playback
cellular communication network
Physical Sciences Inc.
System Information Output
Threat detect X X
Threat ID X X
Threat Azimuth X X
Mass Estimate X
LWIR Hyperspectral Imager
10 meter spatial resolution at 5 km range
for smaller release detection
Detections overlaid on thermal
time processor
11 m
Detection of nerve and blister
agents vapors and aerosols (at
JSLSCAD vapor threshold levels)
and static platforms:
via RJ-45
Communication between local devices in sensor platform: 1-Wire, RS485, USB
Cellular communication between sensor platforms:
– Virtual private network (VPN) was created for all mobile platforms and the tomography
station
– The cellular data link provides connectivity between units, even without line of sight and
in environments or at distances where 802.11 networking is problematic
Panasonic Toughbook
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– Proven ACE/MVE algorithm to detect and identify chemicals within cluttered
backgrounds based on per-pixel radiance spectra
Column density/Mass estimates
using algorithms validated for RTVS system
Triangulation algorithm:
– Coordinate transform: Transform data w.r.t AIRIS pointing to data w.r.t world
coordinates, incorporating pan/tilt setting, vehicle attitude, alignment
calibration values
– Overlap: Determine when threat detections represent multiple views of the
same cloud within a fixed time period
– Triangulation: Calculate absolute location (from geometric intersections) of
8 bounding vertices and COM for each pair of overlapping detections
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1) Quantitative threat detection
fall within the calculated extent
– Calculates metric for each neighborhood
– Iteratively selects most likely cloud,
recalculates using remaining threat
products:
integrated with real-time system
2D Vectors to threat extent in absolute coordinates
Cloud COM
Cloud mass
Projection Matrix Calculator (PMC)
– On-the-fly calculation of projection matrices for current area of interest and sensor
positions
Reconstruction
– Two available algorithms: 1) Improved PSI-MLEM and 2) UC-Davis SLEM and STR
– PSI-MLEM executes faster and was the algorithm of choice at BioWeek 2013
– SLEM and STR will be evaluated in the future
(a)Truth concentration map;
2-sensor NBCRV-TMC geometry,
Threshold 2-sensor NBCRV-TMC geometry.
~ 5 seconds to generate
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Threat ID
Bearing, Elevation
Size of Cloud
– (R134a)/nitrogen flow: column densities of 100-500 mg/m2
– ΔT~ 3 degrees with respect to the ambient temperature
R134a-filled cell was accurately mapped in 3D with the COM location
estimated with ~5% error
Mass estimates were generated via two methods:
– Single sensor column density data in the 2-D image and the estimated range to cloud
to derive the size of the projected pixel at the cloud position mass estimates with
RMS error of ~ 22%
– Using the reconstructed concentration profile and integrating over the 3-D volumetric
output from the CT algorithm mass estimates with RMS error of ~ 8%
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7/30/13 – 8/2/13
Center lines of sight of the sensors to the release point formed a shallow angle
of ~ 45 degrees
AIRIS 1 sensor was positioned 1.42 km from release point. Setup for a 3-sector
scan for a total coverage of 90° with an absolute bearing from 157° to 247°
AIRIS 2 was 1.16 km away. Setup for 120° (4 sectors) with an absolute bearing
from 207° to 327°
Overview of Detected Releases
8 total chemical releases
– 4 TEP (Triethyl Phosphate): Spray releases, 1 (1L/min) and 3 (2L/min)
– 4 MeS (Methyl Salicylate): Spray releases, 1 (1L/min) and 3 (2L/min)
TEP detection overview:
– Good detections for 3/4 releases, 1 release only single sensor detection
– Cloud triangulation, reconstruction and tracking achieved for 3/4 releases
MES detection overview:
– Did not detect/ID MES with the available basis set (absorption based) MES likely
present as aerosol
– Change detection (relative to pre-release cube) shows very weak differential signal
downstream of the release point
Bio-releases: in the process of analyzing findings in comparison with
LIDAR data
TEP Release (BW14) was used for MTMS detailed performance evaluation
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2.5 m/s wind speed, slightly
unstable atmosphere, 3 m release
height with no reflection at the
ground
Conclusions:
downwind of the release point were
expected at these flow rates
– Low thermal contrast and low CDs
yielded cloud detection conditions that
approached the sensitivity limit of the
AIRIS-WAD sensors
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TEP BW14: Real-Time Outputs
TEP BW14 run was a 2 L/min release for a duration of ten minutes
Shown: detection of the TEP cloud as generated by each of the sensor
units in the MTMS constellation at 1:23 minutes after initial dissemination
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release
release
~ 56 m from the release location registered in
the Test Director’s log
The tip of the cloud distribution generated by
MTMS system is within 20 m of the LIDAR
output
along the AIRIS 1 viewing axis and is
significantly different from the LIDAR result
Similar differences were observed in the other
contours generated throughout the duration of
the BW14 release
Physical Sciences Inc.
2-Sensor Reconstruction Errors
The MTMS/cloud geometry was not favorable to 2-sensor reconstruction with a
limited angle (~45°)
The low column density and thermal contrast conditions resulted in low pixel
column density (CD) estimates, as well as missed detections
AIRIS 1 CD values are significantly lower than those generated by AIRIS 2 – in
contrast to what would be expected:
– The cloud vector has a strong propagation component towards AIRIS 1 longer integrating
pathlength should yields higher CD observed by AIRIS 1
– An analysis of the available thermal contrast in both AIRIS 1 and AIRIS 2 showed that AIRIS 1
was subjected to a lower ΔT
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Low mass recovery is typically due to additional factors:
– Inefficient mass dispersion coupled to low vapor pressure of TEP
– Significant mass entrainment into a very low layer where passive standoff detection is
least effective
– Material falls to the ground and undergoes slow evaporative process resulting in low
observable concentrations
TEP amount released (~ 21 kg)
The integrated total mass
release was 4 kg
Physical Sciences Inc.
BW14: Cloud Track
MTMS predicted the TEP track at 144° which is ~4° off from the LIDAR result and
~11° off from the measured wind direction
Despite 3-D reconstruction errors, MTMS provides accurate cloud COM geo-location
estimates and propagation track
Conclusions
We developed, integrated and tested a mobile 2-sensor passive system for
chemical cloud characterization and tracking
The MTMS prototype was evaluated in both laboratory conditions and at
Dugway Proving Ground during BioWeek 2013
Under lab conditions, MTMS demonstrated the ability to estimate the total
plume mass with ~ 8% RMS error and COM location and plume size with
errors of ~ 5%
During BioWeek 2013, MTMS was able to detect, track and reconstruct
3 out of 4 observed TEP releases
– MTMS predicted the cloud location to within 20 m of the LIDAR result and provided a
cloud track that was 4° off from the LIDAR output
– The MTSM 2-sensor tomographic reconstruction process generated cloud contours
with errors attributed to the 2-sensor geometry and difficult detection conditions
The performance of the MTMS system demonstrated at BioWeek 2013 was
consistent with the system requirements defined in collaboration with the
US Army
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