AMBUSH VULNERABILITY MODEL DEVELOPMENT
JOHN WILLIAM SHINSKY
i
CONTENTS
Page
1.
2.
3.
4.
5.
6.
7.
8.
9.
ACKNOWLEDGEMENTS ............................................................................................... iii
LIST OF FIGURES ........................................................................................................... iv
LIST OF TABLES .............................................................................................................. v
LIST OF ACRONYMS ..................................................................................................... vi
LIST OF TERMS .............................................................................................................. vii
ABSTRACT ........................................................................................................................ 1
INTRODUCTION .............................................................................................................. 2
2.1
Background ............................................................................................................. 2
2.2
Problem Statement .................................................................................................. 2
LITERATURE REVIEW ................................................................................................... 4
3.1
Relevant Studies and Papers ................................................................................... 4
3.1.1 MICRODEM and the Weapons Fan Algorithm .......................................... 4
3.1.2 Uncertainty in Viewshed Analysis of IED Ambush Sites .......................... 4
3.2
Leveraging Previous Works .................................................................................... 5
OVERHEAD ANGLE OF ATTACK MODEL ................................................................. 6
4.1
Overhead Angle of Attack Model Inputs ................................................................ 6
4.2
Overhead Angle of Attack Model Methodology .................................................... 6
4.3
Overhead Angle of Attack Model Assumptions ..................................................... 7
4.4
Overhead Angle of Attack Model Limitations ....................................................... 7
AMBUSH VULNERABILITY MODEL ........................................................................... 8
5.1
Ambush Vulnerability Model Inputs and Variables ............................................... 8
5.1
Ambush Vulnerability Model Assumptions ........................................................... 9
5.2
Ambush Vulnerability Model Methodology........................................................... 9
5.3
Ambush Vulnerability Model Outputs.................................................................. 10
5.4
Ambush Vulnerability Model Limitations ............................................................ 11
CUMULATIVE VIEWSHED MODEL ........................................................................... 12
RESULTS ......................................................................................................................... 13
7.1
Common Inputs ..................................................................................................... 13
7.2
Overhead Angle of Attack Model Results ............................................................ 14
7.2.1 Inputs ......................................................................................................... 14
7.2.2 Outputs ...................................................................................................... 14
7.3
Ambush Vulnerability Model Results .................................................................. 15
7.3.1 Inputs ......................................................................................................... 15
7.3.2 Outputs ...................................................................................................... 16
DISCUSSION OF RESULTS .......................................................................................... 26
8.1
Comparison of Results .......................................................................................... 26
8.2
Comparison of Methodologies .............................................................................. 26
8.3
Advantages of the Ambush Vulnerability Model ................................................. 27
8.4
Disadvantages of the Ambush Vulnerability Model ............................................. 28
8.5
Summary of Results .............................................................................................. 28
CONCLUSION ................................................................................................................. 29
APPENDIX A: BASIC INSTRUCTIONS ....................................................................... 30
REFERENCES CITED ..................................................................................................... 31
ii
ACKNOWLEDGEMENTS
The US Army Materiel Systems Analysis Activity (AMSAA) recognizes the following
individuals for their contributions to this report.
The author is:
John William Shinsky, Combat Systems Analysis Division, CSAD
The author wishes to acknowledge the contributions of the following individuals for their
assistance in the creation of this report:
Dr. Peter L. Guth, Department of Oceanography, United States Naval Academy
iii
LIST OF FIGURES
Figure 1: Flowchart depicting the OAAM methodology.................................................................6
Figure 2: Firing position point shapefile created for each route point .............................................7
Figure 3: Route centerline and the vehicle length are used to create the route points .....................9
Figure 4: Overview of the iterative viewshed analysis process for each point ..............................10
Figure 5: All viable threat rasters are summed to build the final threat map.................................10
Figure 6: CVM: Number of route points visible at each terrain position ......................................12
Figure 7: DSM for a portion of Baltimore, MD with route centerline ..........................................13
Figure 8: Shapefile containing the evenly spaced route points required by the Model .................14
Figure 9: Translation of speed and reaction time into required visible distance ...........................16
Figure 10: Effect of vehicle speed and reaction time on number of threat points .........................17
Figure 11: 32 KPH Viable threats map using 5s, 10s, and 15s reaction times ..............................18
Figure 12: 48 KPH Viable threats map using 5s, 10s, and 15s reaction times ..............................19
Figure 13: 64 KPH Viable threats map using 5s, 10s, and 15s reaction times ..............................20
Figure 14: 5s reaction time viable threats map using 32, 48, and 64 KPH vehicle speeds ...........21
Figure 15: 10s reaction time viable threats map using 32, 48, and 64 KPH vehicle speeds .........22
Figure 16: 15s reaction time viable threats map using 32, 48, and 64 KPH vehicle speeds .........23
Figure 17: AVM viable threats for each route point using a 133m reaction distance ...................24
Figure 18: AVM firing positions that can see each route point .....................................................25
Figure 19: Viable attack positions from the AVM and CVM methodologies ...............................27
iv
LIST OF TABLES
Table 1: Ambush Vulnerability Model user selected inputs ............................................................8
Table 2: Ambush Vulnerability Model intermediate computations ................................................8
Table 3: Ambush Vulnerability Model output files .......................................................................11
Table 4: Final statistical output table for the OAAM ....................................................................15
Table 5: User defined parameters used for the analysis ................................................................15
Table 6: Typical Model Run Times ...............................................................................................27
Table 7: Visualization Recommendations .....................................................................................30
v
LIST OF ACRONYMS
AoA
AMSAA
AVM
CDD
CSAD
CVM
DA
DEM
DOD
DSM
DTM
GIA
GIS
OAAM
MPE
SF
TR
USGS
- Analysis of Alternatives
- US Army Materiel Systems Analysis Activity
- Ambush Vulnerability Model
- Capability Development Document
- Combat Systems Analysis Division
- Cumulative Vulnerability Model
- Department of the Army
- Digital Elevation Model
- Department of Defense
- Digital Surface Model
- Digital Terrain Model
- Geospatial Information Analysis
- Geographic Information System
- Overhead Angle of Attack Model
- Mobility, Power, and Energy
- Standard Form
- Technical Report
- United States Geological Survey
vi
LIST OF TERMS
ArcGIS
- Commercial software application primarily used for Geospatial analysis
Feature Class - Dataset containing vector data stored within a geodatabase
Geodatabase - Spatial data repository used by ArcGIS to store geospatial data in a proprietary
format
Polyline
- A geospatial feature that is a line created by linking several points
Shapefile
- A dataset containing vector data in an open format
Vector Data - Any geospatial data that is made up of points, lines, or polygons
vii
1.
ABSTRACT
The Ambush Vulnerability Model (AVM) was developed to better analyze a moving vehicle’s
vulnerability to an attack. The model uses several user defined parameters to provide a custom
analysis that can determine the location of every viable threat position the vehicle could
encounter while traversing the route. The model uses a LIDAR DSM dataset, route centerline,
vehicle speed, vehicle length, targeting ranges, and a reaction time to accurately determine the
positions of every point on the terrain that poses a threat to the moving vehicle based on the
vehicle speed and how long they need to maintain line of sight in order to attack.
1
2.
INTRODUCTION
2.1
Background
In 2012, a question was asked regarding the leveraging of viewshed analysis in order to
determine the vulnerability of military vehicles to an overhead attack in operational
environments. Although AMSAA is one of the Army’s leading analysis organizations,
covering everything from logistics analysis to combat systems analysis, no AMSAA
models incorporated line of sight. Since AMSAA had little experience in geospatial
analysis, a new model had to be developed. The resulting Overhead Angle of Attack
Model (OAAM) was designed for the sole purpose of providing a statistical
representation of the percentage of the area that is suitable for attacking the route. This
refers to the number of suitable locations out of the total possible firing positions across
the entire DEM.
The model provides statistical output that could be used for survivability and lethality
analysis from an overhead attack. The model also had to be developed within two weeks.
The prototype model was completed on time and the statistical results were provided for
the survivability and lethality analyses.
The methodology utilizes geospatial elevation data to locate and characterize all overhead
firing opportunities within a user defined radius circle of a vehicle traveling along a
route. The model used a new engagement geometry methodology to capture the
engagement range, attack angle, and other critical factors such as line of sight that are
needed to assess vehicle vulnerability to overhead attacks.
The AMSAA teams responsible for survivability and lethality analysis requested that the
model be improved to incorporate a moving target, as well as to easily locate all viable
threats instead of just the percentage of the area suitable for an attack. The goal of
identifying viable threats would require the model to be completely revamped in order to
incorporate both vehicle speed and reaction time, defined as the amount of time it would
take for an attacker to acquire, aim, fire, and hit the target. The Ambush Vulnerability
Model (AVM) was developed as a result of this overhaul of the OAAM.
In addition to the AVM, the Cumulative Viewshed Model (CVM) was also developed to
provide a quicker result than the AVM by ignoring whether or not the route points are
consecutively visible to the attacker. The CVM determines how many route points a
firing position can see, but it does not determine if these visible points are consecutive or
split up along the route.
2.2
Problem Statement
The new methodology will analyze a vehicle’s vulnerability to an attack by looking not
only at line of sight, but also how long there is continuous line of sight from a shooter to
a target. It builds upon the multiple viewshed analysis methods that exist in the OAAM
methodology to consider the speed the vehicle will be traveling and how long the weapon
2
system will need to have line of sight in order to be considered a viable threat. The final
product will be a toolbox that contains two models that can be used to answer different
questions regarding a vehicle’s vulnerability to an ambush. The AVM and the CVM
models provide the user with various analytical options to address ambush vulnerability.
3
3.
LITERATURE REVIEW
A review of past studies was done to determine if any previously used principals could be
applied to this new methodology. The focus of the literature review was on viewshed and line of
sight analysis methods, as well as any information on utilizing a multiple viewshed analysis.
Several studies have focused on these principals and one was very similar to the Overhead Angle
of Attack methodology, but nothing had previously been done with respect to analyzing the
potential of an attack on a moving ground vehicle.
3.1
Relevant Studies and Papers
The literature review found four papers that detail the use of multiple or cumulative
viewshed analysis that is similar to the analysis methodology in the development of the
AVM. Two papers by Guth (2003, 2004) are directly related to the work that was done in
the OAAM, and can be directly applied to the AVM.
3.1.1
MICRODEM and the Weapons Fan Algorithm
Guth (2003) described the MICRODEM program, which allows the user to define
a route and the program determines where it can be seen by ambushing forces.
This is done by computing a weapons fan at each point along the route which is
then stored so that it can be used in the final ambush movie. The ambush movie
uses a base layer and overlays the results from the weapons fan algorithm at each
point to create a movie that shows the amount of the terrain that can see each
point along the route. The model also creates a final image that displays the
percentage of the route that can be seen from each observation point.
The AVM project builds on Guth (2003). The MICRODEM model takes a route
and computes a viewshed at each point along the route to determine which points
on the terrain have line-of-sight to the route. When the analysis is completed there
is a final output that includes a movie of the viewsheds strung together, as well as
a visualization of what portions of the route are visible. In addition, Guth showed
outputs identical to what is produced by the CVM. By expanding on this
technology, vehicle speed and reaction times can be incorporated, bringing a
higher level of complexity and accuracy to the model.
Guth (2004) details how the line of sight, viewsheds, and accurate 3D perspective
view models are computed from intervisibility algorithms and DEMs. The author
discusses how the geometric model has the largest impact on these results. The
author also discusses what methods and algorithms produce the highest quality
weapons fans while minimizing error. (Guth, 2004)
3.1.2
Uncertainty in Viewshed Analysis of IED Ambush Sites
Not all of the applicable studies pertain to the use of a standard multiple viewshed
analysis approach. (Raehtz, 2011) covers in detail the uncertainty in viewshed
4
analysis, which can come from both the differences in algorithms and the error in
elevation models. The paper also discusses a viewshed analysis approach that uses
the highest resolution DEMs available in Afghanistan to analyze IED explosion
sites. By considering uncertainty Raehtz, (2011) was able to extrapolate the error
models to ultimately develop a more informative viewshed using a Monte-Carlo
simulation. His detailed documentation on error in viewshed analysis and how to
properly implement viewsheds contributed to properly implemented code in
AVM.
3.2
Leveraging Previous Works
The previous works above all deal with various types of cumulative and multiple
viewshed analysis methodologies. The AVM relies heavily on this methodology to
compute a viewshed at every point along a specified route. These time synchronized
viewsheds are then used for a complex analysis to determine what points on the terrain
have continuous line of sight to the vehicle.
5
4.
OVERHEAD ANGLE OF ATTACK MODEL
The OAAM was developed to show the locations of suitable attack positon in an urban
environment. The model provides an overall statistical representation of the threat in the study
area, while also providing statistical information on the distribution of attack angles and attack
distances. The model uses python scripting and runs in Esri ArcGIS10.x for Desktop Advanced.
4.1
Overhead Angle of Attack Model Inputs
The only two inputs required by the OAAM are a DSM and a route point shapefile to
represent the vehicle waypoints to be analyzed. Depending on the type of analysis that is
being conducted, the required resolution of the DSM varies. The other input ingested into
the model is the route points for the vehicle. While any spacing can be used for these
points, the vehicle length was used as the point spacing under the assumption that it
provided for a continuous analysis is conducted and that no vehicle positions are
unaccounted for. By using a larger route point spacing, the runtime of the model can be
greatly reduced at the at the expense of introducing some uncertainty in the results.
4.2
Overhead Angle of Attack Model Methodology
The current OAAM locates and characterize all overhead firing opportunities within a
user defined radius circle of a vehicle traveling along a route (Figure 1). The model
computes a viewshed for each point on the route, and characterizes all possible firing
opportunities in terms of distance and attack angle. The OAAM produces a statistical
output table called tablePercentages showing the percentage of the area that is suitable for
an attack originating from certain distances and attack angles. The model also creates a
shapefile for each route point that contains the locations of every possible firing position
along with angle and distance (Figure 2).
Figure 1: Flowchart depicting the OAAM methodology
6
Figure 2: Firing position point shapefile created for each route point
4.3
Overhead Angle of Attack Model Assumptions
The main assumption, which has been hard coded into the model, is that the target and
the firer are 2 meters tall. The model also assumes the vehicle is not in motion, and that
line-of-sight provides enough time for a firing opportunity.
4.4
Overhead Angle of Attack Model Limitations
The major limitations of the OAAM include static inputs and the assumption of a
stationary target. The model requires the route centerline to be split into route points prior
to running the model, but this functionality could easily be added to the model for
convenience and is a simple procedure for an experienced GIS analyst. The OAAM
produces a statistical result that only provides the user with an overall percentage of the
area that is suitable for attacking the route. The model produces shapefiles that allow the
user to visualize all firing positions at each route point, but there is no easy way to
visualize the results for the entire route.
7
5.
AMBUSH VULNERABILITY MODEL
The AVM was developed to analyze a moving vehicle’s vulnerability to any attack while also
addressing the limitations of the OAAM. The model allows the users to customize the most of
the inputs so that the model will better fit their analysis. The model also allows the user to
visualize the actual locations of the firing positions that are classified as viable threats. A viable
threat exists if the firing position has continuous visibility of the target for the amount of time
that was specified by the user. The end result is a highly versatile model that can simulate a
moving vehicle and analyze the vulnerability to an attack from any angle or distance.
5.1
Ambush Vulnerability Model Inputs and Variables
To consider a moving target, the AVM requires a few additional inputs (Table 1) beyond
those used in the OAAM. These parameters allow the model the versatility to handle a
wide range of applications. Table 2 shows the three variables that are computed within
the model based on the user defined parameters. These variables are used throughout the
model for viable threat classification, as well as output messages that the user can see as
the model runs in order to keep track of the model’s progress.
Table 1: Ambush Vulnerability Model user selected inputs
INPUTS
LIDAR DSM
Route Centerline
Vehicle Length (meters)
Vehicle Speed (kph)
Minimum Range (meters)
Maximum Range (meters)
Target Height (meters)
Firer Height (meters)
Reaction Time (sec)
DESCRIPTION
Elevation raster used for the viewshed analysis
Polyline shapefile that will be split into evenly spaced points
Used for route point spacing and the calculation of the number
of route points required for a viable threat to exist
Used to calculate the number of route points required for a
viable threat
Minimum targeting range for an attack
Maximum targeting range for an attack
Height of the target above the ground in meters
Height of the threat above the ground in meters
Used to calculate the number of route points required for a
viable threat
Table 2: Ambush Vulnerability Model intermediate computations
VARIABLES
pointsForKill
totalPoints
Reaction Distance (meters)
DESCRIPTION
Number of consecutive points that must be continuously visible
for a viable threat
Total number of route points analyzed
Distance that must be under continuous observation for a viable
threat
8
5.1
Ambush Vulnerability Model Assumptions
The model assumes that one raster cell centroid equals one potential firing position.
Another assumption is that analyzing the vehicle positions bumper to bumper is sufficient
to assess every possible vehicle location since there would be no position on the route
that the attacker could not see some portion of the vehicle. The last assumption is that a
viable threat firing position requires line of sight to the vehicle for at least the amount of
time specified by the user defined reaction time.
5.2
Ambush Vulnerability Model Methodology
After the user sets all of the parameters and starts the model, the first process involves
splitting the route into evenly spaced route points (Figure 3). These points correspond to
every possible vehicle location along the route, assuming that using the vehicle length as
the spacing results in a line of vehicles bumper to bumper. If a firing position can see two
points in a row, it is not possible to miss anything since some portion of the vehicle
would still be visible when its centroid is between the two visible locations.
Figure 3: Route centerline and the vehicle length are used to create the route points
Once the route points shapefile has been created, the model goes calculates and stores a
variable called pointsForKill, which refers to the number of route points that must be
continuously visible by a potential firing position in order to classify it as a viable threat.
This is the distance traveled by the vehicle during the user defined reaction time, divided
by the point spacing. The outputs of these two steps are fed into an iterative multiple
viewshed analysis that loops through one point at a time for the entire route.
The multiple viewshed analysis loops through every point in the route points shapefile
and determines whether or not continuous line of sight exists at a firing position. This
iterative analysis process (Figure 4) completes three steps for each route point.
9
Figure 4: Overview of the iterative viewshed analysis process for each point
The model computes a viewshed at each route point. Once the route point number is
equal to pointsForKill, the model sums the previous pointsForKill viewsheds into a single
raster and then reclassifies that raster to retain only those attack points that had
intervisibility for all the points.
The goal of process is to compute a raster for every route point that shows the locations
of firing position that had continuous line of sight to the target for the correct number of
route points. These viable threats rasters will then be used to determine the location of
every threat along the entire route. After looping through every point in the routepoints
shapefile, the model sums every viable threat raster to form one final viable threat output
raster showing the location of every viable threat that was encountered along the entire
route. This final process is visualized in by the flowchart in Figure 5. Once this final
output raster is created and saved, the analysis is complete.
Figure 5: All viable threat rasters are summed to build the final threat map
5.3
Ambush Vulnerability Model Outputs
The AVM produces several output files that can be used by the user in many ways (Table
3). The final raster layer showing the location of every threat along the route provides
the final result of the model. This raster dataset that shows the location of every point on
the map that has the capability of attacking the vehicle based on the vehicle’s speed and
the user defined reaction time, as well as how many route points each viable threat could
attack. This output map could be used to make the driver aware of the possibility of an
attack from those locations, as well as to set up observation points in areas with line-ofsight to the locations that have the most visibility to the route. This would ultimately
provide protection to the driver and occupants of the vehicle. This could also be used to
10
reroute a convoy by avoiding the sections of the route determined to be the most
dangerous.
Table 3: Ambush Vulnerability Model output files
OUTPUTS
DESCRIPTION
allThreats raster
Final raster layer showing the
viable threats for the entire route
routepoints shapefile
viableThreats# raster
viewshed# raster
5.4
Point Shapefile containing the
evenly spaced route points and
information on the number of
viable threats and firing positions
at each point
Intermediate raster layer showing
the viable threats at each point
Intermediate output raster layer
showing the viewshed computed
at each point
USES
Main output of the AVM that
allows the user to visualize
every viable threat along the
route
Allows the user to visualize
the number of viable threats
and visible firing positions at
each route point
Used by the model to compute
the final all threats raster
Used by the model to compute
the viable threat rasters
Ambush Vulnerability Model Limitations
Although the AVM addresses most of the limitations in the OAAM, it still only assesses
possible threats on the rooftop level of buildings or on the ground. Since the model uses a
DSM for the elevation values, it does not consider a threat positioned on a lower level
balcony or shooting through a window.
The model also assumes that the vehicle remains at a constant speed throughout the entire
route. In doing this, every viable threat position is based on the assumption that the
vehicle has not encountered a previous attack which would have caused them to drive
faster and use evasive maneuvers. This is also an issue if the vehicle is forced to slow
down or stop because a vehicle is attacked in front of it. The model is not capable of
assessing how a drastic change in speed would affect the outcome of the remaining
portion of the route. Vehicle speed has a direct effect on the distance that a vehicle must
be visible in order to be attacked. If the target speeds up, the vehicle would be able to
travel a larger distance within the attacker’s reaction time, which would make it more
difficult for a viable threat to exist. If the vehicle slows down the opposite would occur,
making the existence of a viable threat more probable.
The most significant limitation of the model is runtime. The AVM can take up to 10
seconds per route point to run depending on the user’s input parameters and computer
specifications. This limitation can be mitigated by either using fewer route points to
improve runtime or using the CVM which was developed in response to the AVM’s
runtime issue. Both of these mitigation options require the user to sacrifice precision for
performance.
11
6.
CUMULATIVE VIEWSHED MODEL
The CVM was developed as a quicker alternative to the AVM. The model uses the same inputs
as the AVM and many of the same processes. The only difference between the two models is that
the CVM ignores continuous line of sight and does not determine whether or not route points are
consecutively visible to a firing position. The model allows the user to quickly visualize the
number of route points that are visible at each location on the terrain (Figure 6). If maintaining
continuous line of sight is not important to the user and can be neglected, the CVM out performs
the AVM and provides a similar result that still meets the user’s analytical requirements.
Figure 6: CVM: Number of route points visible at each terrain position
12
7.
RESULTS
The goal of this analysis was to compare the results using the existing OAAM model and the
new AVM and CVM models.
7.1
Common Inputs
To conduct the analysis using both models, a LAS dataset for a portion of downtown
Baltimore was downloaded from the USGS’ Earth Explorer website
(http://earthexplorer.usgs.gov) and converted to a DSM elevation raster with a resolution
of three meters that could be easily ingested into the model. This conversion was done
within ArcGIS using the Create LAS Dataset and LAS Dataset to Raster conversion
tools. A 2 kilometer route was digitized through the study area in order to ensure enough
opportunities for a viable threat to exist. Figure 7 shows this DSM raster layer with the
route overlaid.
Figure 7: DSM for a portion of Baltimore, MD with route centerline
13
7.2
Overhead Angle of Attack Model Results
7.2.1
Inputs
The OAAM requires the route centerline to already be split into evenly spaced
points prior to running the model. To ensure the same data was being used for
both models, the routepoints shapefile that is generated automatically by the
AVM (Figure 8), using only the vehicle length for the point spacing, was used as
the input for the OAAM.
Figure 8: Shapefile containing the evenly spaced route points required by the Model
7.2.2
Outputs
The final output from the OAAM is a statistical table showing the summarized
results of the analysis broken down by angle ranges and distance bins. The
percentage of the area suitable for an attack is averaged for all 406 route points
that make up the vehicle route that was analyzed (Table 4). The table shows that
there are very few firing positions from an angle greater than 30 degrees. At an
angle of 30 degrees the firer’s height would need to be half the distance from the
target. As the angle increases from this point, the firer’s height must drastically
rise as well. Unless the firer is almost directly above the target firing straight
14
down, these higher angle ranges will never be achieved and should not be
included in most analyses.
Table 4: Final statistical output table for the OAAM
7.3
Ambush Vulnerability Model Results
7.3.1
Inputs
The AVM has a great deal of versatility, allowing the user to fully customize the
parameters of the model to best represent the desired analysis. Table 5 shows the
additional parameters for this analysis.
Table 5: User defined parameters used for the analysis
PARAMETER
Vehicle Speed (kph)
Vehicle Length (meters)
Minimum Targeting Range (meters)
Maximum Targeting Range (meters)
Target Height (meters)
Firer Height (meters)
Reaction Time (seconds)
VALUES
32, 48, 64
5
30
600
2
2
5 , 10 , 15
The vehicle length of 5 meters corresponds with the approximate dimensions of a
standard SUV, which is often used by military, law enforcement, and many
15
civilians. Varying reaction times were used to show how the results of the model
vary depending on the parameters that have been set by the user, and to illustrate
how the analysis could easily be changed to reflect a specific weapon or targeting
system. The analysis was conducted nine times using three different vehicle
speeds and three different reaction times. Figure 9 shows how the varying vehicle
speeds and the reaction times correspond to the distance over which a firer needs
continuous visibility to the target.
Figure 9: Translation of speed and reaction time into required visible distance
7.3.2
Outputs
The AVM creates a raster map that shows the location of every viable attack
position along the route. For this 2 kilometer route through downtown Baltimore,
the majority of the viable threats are close to the route at ground level and on top
of nearby buildings. The results also clearly show how changing the vehicle speed
and reaction time impact the final results of the model.
Figure 10 shows the total number of viable threats for the nine runs. It shows an
inverse correlation between vehicle speed and viable threats, so if the vehicle is
traveling faster, there will be fewer viable threats with the capability of hitting the
target. This also goes for the reaction times, the longer the attacker needs to hit
16
the target, the less likely they are to being classified as a viable threat. The graph
shows how critical the reaction time and vehicle speed parameters are to the
model’s results. It also shows that the relationship between vehicle speed, reaction
time, and viable threats is exponential. This is visualized by the bend in the graph.
If each additional 5 seconds reduces the number of viable threats by 60%, the
reductions will not be linear since the first 60% reduction could be thousands of
threats and the second 60% reduction could be hundreds of threats. In addition, an
increase in reaction time creates more short stretches of road that cannot be
attacked.
Figure 10: Effect of vehicle speed and reaction time on number of threat points
A final viable threats map overlay was created for each of the nine runs. Figure 11
shows the results for the three runs conducted at 32 kph, Figure 12 shows the
results at 48 kph, and Figure 13 shows the results at 64 kph. The maps show that
as the reaction time is increased the number of threat locations decreases. These
maps were also created for each reaction time to show how changing the vehicle
speed affects the results. Figure 14-16 show the results for 5, 10, and 15 second
reaction times. To help visualize the most dangerous areas of the routepoints
output file provides the capability to visualize the number of viable threats and the
number of visible firing positions at each route point. Figure 17 shows the number
of viable threat locations that can engage each route point and Figure 18 shows
the number of firing positons that can see each route point. The map showing the
number of viable threats at each route point clearly shows the most dangerous
places along the route.
17
Figure 11: 32 KPH Viable threats map using 5s, 10s, and 15s reaction times
18
Figure 12: 48 KPH Viable threats map using 5s, 10s, and 15s reaction times
19
Figure 13: 64 KPH Viable threats map using 5s, 10s, and 15s reaction times
20
Figure 14: 5s reaction time viable threats map using 32, 48, and 64 KPH vehicle speeds
21
Figure 15: 10s reaction time viable threats map using 32, 48, and 64 KPH vehicle speeds
22
Figure 16: 15s reaction time viable threats map using 32, 48, and 64 KPH vehicle speeds
23
Figure 17: AVM viable threats for each route point using a 133m reaction distance
24
Figure 18: AVM firing positions that can see each route point
25
8.
DISCUSSION OF RESULTS
8.1
Comparison of Results
The results of the OAAM show that the majority of the suitable attack locations exist in
the 150 meter to 450 meter distance ranges and come from an attack angles between 1
degree and 15 degrees (Table 4). The AVM provided results that range from 616 viable
threats (0.22% of total possible firing positions) to 15,099 viable threats (5.42% of total
possible firing positions) over the entire route depending on the parameters that were
used. Both models have the capability of providing a number of firing positions and a
total percentage, but the OAAM is limited to a stationary target where the AVM allows
the user to determine vehicle speed and the firer’s reaction time.
The OAAM determines the percentage of the area suitable for an attack on a stationary
vehicle from various attack angles and ranges. Most of the attack positons will occur at
angles below 30 degrees, since it becomes much harder to find an elevated attack position
with a large angle as you get farther away from the target. At 30 degrees the firer’s
elevation must be half the distance to the target. This is only possible at very close
ranges.
The AVM uses the vehicle speed and a reaction time to find all possible attack positions
for a moving target. A faster moving target is more difficult to hit than a slower one.
Also, a weapon system that requires a longer target acquisition time will also have more
difficulty hitting the target. The bends and curves in the route prove to be the safest
portions of the vehicle’s route. The majority of the firing positions that can see the
vehicle on one street cannot see the vehicle on another. This suggests that a route that
constantly turns and changes streets may be safer than a direct route that stays on the
same street for long distances. Frequent turns would cause the vehicle to slow down,
which would increase the risk of being attacked.
8.2
Comparison of Methodologies
The comparison of the results using AVM and CVM (Figure 19) shows red areas
corresponding to viable threats using both models. The yellow areas correspond to the
viable threats classified only by the CVM. The comparison was done using a vehicle
speed of 48 kilometers per hour and a reaction time of 10 seconds. The AVM provides a
final result of 5,409 viable threats, while the CVM results in 7,264 viable threats. This
equates to almost a 35% increase in the number of viable threat locations by considering
continuous line of sight. The AVM is twice as time consuming as the CVM (Table 6), but
also provides an increase in accuracy. While the AVM does occasionally omit a viable
threat because there is a small break in visibility, it also rules out the possible threat
locations that can see half of the required number of route points on one road and the
other half on another. This firer would not be able to attack the target based on the user
defined parameters, but it would be classified as a viable threat using the CVM
methodology. This also applies to unmanned targeting systems that must have continuous
visibility to the target to lock on, fire, and hit the target. A small gap in visibility may be
26
enough to restart the acquisition process. The AVM takes longer to run, but provides a
higher level of confidence in the results. This needs to be taken into consideration when
the user determines which methodology they plan to use. If faster run times and better
model performance is more important than absolute accuracy, then the CVM is sufficient.
If the user is comparing routes and is only looking to determine which route is safer, the
differences between the AVM and CVM do not matter. If the user is interested in the
exact locations of viable threats on a specific route that have continuous line of sight to
the target, the differences in results between the two models is more significant.
Table 6: Typical Model Run Times
Model
OAAM
AVM
CVM
Run Time
15 Seconds Per Route Point
10 Seconds Per Route Point
5 Seconds Per Route Point
Figure 19: Viable attack positions from the AVM and CVM methodologies
8.3
Advantages of the Ambush Vulnerability Model
The new model has many advantages over the OAAM. These advantages range from the
ability to model a moving target to the ability for the user to quickly generate a map of
27
the locations of every viable threat. The OAAM provides individual shapefiles that can
be visualized for every route point, but no simple synthesis. The AVM systematically
generates a summary visualization. In addition, the AVM also allows the user the
capability to tune the inputs to meet the specific requirements of the analysis. This is also
possible using the CVM, but the model does not consider continuous line of sight which
can lead to gaps in visibility that may not be negligible.
8.4
Disadvantages of the Ambush Vulnerability Model
The AVM’s runtime ranges from a few seconds per point to over 10 seconds per point
depending on the input parameters and the user’s computer specifications. The excessive
runtime can be mitigated depending on the user’s analysis requirements by increasing the
route point spacing so the model has to process fewer points. Future ArcGIS capabilities,
such as multiple core processing, can be built into new versions of the model to allow for
much faster runtime by taking advantage of multiple core processing. This could allow
the model to run different tasks simultaneously resulting in significant improvements in
runtime.
8.5
Summary of Results
The results of the three models clearly represent the limitations of the OAAM, as well as
the advantages of the AVM and the CVM. The results of the OAAM provide detailed
statistical probabilities, but do not provide useful information to the user other than the
ability to compare different routes to see which is safe. The CVM enables the user to see
how many route points can be seen by each firing positon on the terrain, which can be
used to determine which firing positions pose the greatest threat to the chosen route. The
AVM, which adds to the methodology of the CVM, computes the number of threat points
and shows their locations. The model also provides additional outputs that make it
possible for the user to determine the most dangerous portions of a route and ultimately
what roads should be avoided completely.
Both the CVM and the AVM provide the user with the flexibility to customize the model
to meet the requirements of their analysis. Both models provide useful results that can be
used easily by the user. The major difference between these two methodologies is how
the viable threats are classified. The CVM simply determines the total number of route
points that each position can see and ignores whether or not they are consecutively
visible. This assumes that either all the visible points are consecutive or that small gaps in
visibility do not matter enough to justify a longer run time. The AVM considers
continuous line of sight and ensures that there are no gaps in visibility. This leave the
user with a higher level of confidence that the viable threats have continuous line of sight
to the target for the specified amount of time with no gaps or breaks in visibility at the
expense of a much longer runtime. The user must determine whether faster runtimes is as
important as a high level of confidence in the results.
28
9.
CONCLUSION
To improve upon the OAAM, the AVM and CVM methodologies analyze a vehicle’s
vulnerability to an attack by looking at both line of sight and target acquisition timing, which
depends on vehicle speed and the required weapon system engagement time. The AVM and
CVM models also allow the user to specify a minimum and maximum targeting distance in order
to analyze the viable threats associated with specific weapons systems with individual arming
distances and range limitations. The addition of these additional parameters allow the models to
more accurately simulate a moving vehicle ambush scenario. The models enable the user to
conduct a much more thorough and complete analysis than the OAAM. In addition to these
improvements, the new models run faster and more efficiently. The AVM runs 33% faster and
the CVM runs 66% faster. The development of these new models provides the user with the
capability to effectively model the ambush vulnerability of a moving vehicle, as well as
determine the locations of all possible threats along a specified route.
29
APPENDIX A: BASIC INSTRUCTIONS
Installation Instructions:
Before the models can be used, they must be downloaded and installed. This process is not
difficult and can be easily completed within ArcGIS. The installation process is as follows:
Download the zipped tools folder
Unzip folder
Open ArcMap
Open ArcToolbox
Right click on Toolbox icon at the top of the ArcToolbox window
Pick “Add toolbox”
Navigate to the directory, and Select the “AMSAA tools.tbx” file
Open the toolbox, and pick one of the two models
Sample data for Baltimore is included in the “Input” directory
Output Visualization Recommendations
The AVM produces two output files that are useful to visualize. The most important is the
allThreats.tif raster that can be found in the Threats folder within the selected output folder. The
other useful output is the routepoints.shp shapefile that can be found directly within output
folder. These two files can be used to produce three visualizations that make it very easy to
understand the final results. (Table 7)
The CVM produces one main output file that is useful to visualize. This output is the
viewshedSum.tif raster that can be found directly in the output folder that was used to run the
model. Using this output, it is possible to produce two visualizations that provide a detailed look
at the model’s results. (Table 7)
Table 7: Visualization Recommendations
MODEL
OUTPUTS
allThreats.tif
AVM
routepoints.shp
routepoints.shp
viewshedSum.tif
CVM
viewshedSum.tif
VISUALIZATION
Location of all viable threats that are
capable of attacking the vehicle
Number of viable threats that exist at each
route point by changing the symbology to
visualize the numthreat field
Number of visible firing positions at each
route point by changing the symbology to
visualize the numvis field
Firing positions that can see a specific
number of route points
Number of route points that can be seen by
each firing position
30
EXAMPLE
Figure 19
Figure 17
Figure 18
Figure 23
Figure 19
REFERENCES CITED
Guth, P.L. (2003). Ambush Movies and the Weapons Fan Algorithm: Military GIS Operations
and Theory, in Proceedings of the International Conference on Military Geology and Geography,
June 15-18, 2003, West Point NY.
Guth, P.L. (2004). The Geometry of Line-of-Sight and Weapons Fan Algorithms: in Caldwell,
D.R., Ehlen, J., and Harmon, R.S., eds., Studies in Military Geography and Geology, Dordrecht,
The Netherlands, Kluwer Academic Publishers, chapter 21, p.271-285.
Raehtz, S.M. (2011). Accounting for Uncertainty in Viewshed Analysis of IED Ambush Sites in
Afghanistan. Michigan State University. Retrieved August 20, 2014, from
http://etd.lib.msu.edu/islandora/object/etd%3A1141
31