Understanding environmental complexity: a simple testbed model and analysis

JJ Gillard, NHJ Stanbridge*, PR Syms

Dstl LBSD Fort Halstead

26 ISMOR

1st – 4th September 2009

DSTL/CP37864

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Introduction• Dstl seek to understand complex environments containing both

natural and artificial clutter objects. A clutter object:

• Is a transient object not represented on mapping or aerial photography*;

• Blocks lines of sight or competes for observer’s attention.

• Incorrect representation of clutter leads to potentially misleading results from combat simulations:

• Shorter search times;• Longer engagement ranges;• Higher surveillance and target acquisition probabilities;• Shielding from system effects.

• Red can and does use clutter to his advantage:

• Hiding personnel and devices behind clutter;• Hiding amongst clutter, both visually and behaviourally – being

‘clutter-like’.* Unless, in some circumstances, very high resolution and immediate.

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Urban and rural clutter examples*

• Urban clutter, eg:

• Cars, lorries

• Street furniture, civilians

• Rural clutter, eg:

• Sheds, rubbish, animals;

• Abandoned machinery

* Urban and rural clutter categories not exclusive

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

2D Testbed Clutter Model• A scene was modelled as follows:

• An observer views a scene of width and depth 10 metres - the field• Within the field are a number of objects which obstruct the

observer's view - these objects constitute the clutter.• The vista coverage is the percentage of the observer's view of the

perimeter which is obstructed, measured in metres.• The area coverage is the percentage of the field area which is

hidden from the observer's view.

10m

y-axis

x-axisObserver

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Simple data entry and results presentation

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

LoS interruption: buildings and vegetation

• P(LoS) – random target, observer 27°: ≈ 50% of azimuths P(LoS) = 0 ≈ 50% of azimuths P(LoS) = 0.1

• P(LoS)max for terrain: ca. 0.80

• azimuth change needed to alter P(LoS) significantly: ca. 30°

45 90 135 180 225 270 315 360Rotation Angle

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

egatnecrePfo

niarreTneeS

0 45 90 135 180 225 270 315

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

9 °

17 °

27 °

37 °

56 °

63 °

72 °

0

0.25

0.5

0.75

1

0 20 40 60 80

P(LoS)max vs graze angle

• Mapping only an approximation:

• vegetation underestimated

• buildings overestimated

MapMap

Reality

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

LoS interruption: clutterNumbers of objects per property

0

2

4

6

8

10

12

14

16

<=10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90

Number of objects

Fre

qu

ency

• Far more clutter on the ground than revealed by aerial photography

• Objects per property conforms closely to negative exponential distribution

• Clutter is ‘clumped’ around properties: result of human activity

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Number of Objects

Co

vera

ge

(%)

0.1m

0.2m

Random

0.3m

0.4m

0.5m

Relationship between number of objects and P(LoS) derived

Coverage for randomly chosen widths within a certain range is almost identical to that of the midpoint of the range:

number more important than explicit size

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Conspicuity: search performance• Visual search performance is influenced by a combination of many

factors

• E.g. colour, motion, shape, flicker, lustre of the target

• CONSPICUITY: how conspicuous an object is relative to its surroundings

Observer

Target

Point of fixation at which observer first detects target

in his peripheral vision

Conspicuity = Angular distancefrom target to the point of detection

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Conspicuity: search performance• Two refinements of the metric:

– Detection conspicuity: angular distance between target and fixation point at which target is detected

– Identification conspicuity: angular distance between target and fixation point at which target is identified

• Literature search yielded rural trials data relating conspicuity to search time, and provided relationships:

log Ts = 0.81 – 0.67 log Cd

log Ts = 0.61 – 0.49 log Ci

where Ts is search time, Cd and Ci are detection/identification conspicuities

• Dstl extending Dutch research in urban environment.

See ‘Visual Conspicuity’

A. Toet, P. Bijl

© Dstl 2009

Dstl is part of the Ministry of Defence

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Recommendations for improvement• Highly detailed combat models need to have greater

amounts of clutter incorporated into their environments:• This work provides validated values.

• Aggregated / abstract models need to modify statistical terrain / terrain algorithms and data:

• P(LoS) from static observer; mean distance between non-exposure; distance moved before re-assessment;

• Testbed model provide rapid method to derive statistics

• Both types of model should consider interaction with clutter:

• Visual competition, tracking of targets, physical shielding;• Conspicuity provides viable method.

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Conclusions • LoS is blocked to a greater extent than would be appreciated.

• Low density clutter can have a highly detrimental effect on STA.

• Clutter is more prevalent in the environment than expected:• Maps / aerial photos misrepresent the ‘ground truth’;• Clutter distribution is non-homogeneous rather than random;• Photographic analysis provides useful insight.

• Rapid, flexible model developed to analyse impact of clutter: • Statistics for coverage and line of sight can be generated;• Relationships between clutter metrics can be derived.

• Study findings need to be incorporated into low-level combat analysis and considered in studies involving STA

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Acknowledgements and references

• Joe Gillard developed the testbed model and conspicuity understanding.

• Paul Syms provided data and analysis on rural clutter densities and expertise on STA processes.

• This project is sponsored by D Scrutiny, UK MoD.

• This project is reported in:• Representation of Clutter in Combat Models, Gillard,

Stanbridge and Syms, Dstl/CR34839, March 2008• Best practice for modelling STA in complex environments,

Gillard, Stanbridge and Syms, in preparation.

© Dstl 2009

Dstl is part of the Ministry of Defence

UK UNCLASSIFIED

Abstract

•Abstract•Surveillance and target acquisition (STA) are fundamental to all operations involving dismounted personnel. Increasing complexity, in terms both of the physical and human environments, adds additional burdens to the STA processes. In order to model STA processes appropriately, it is necessary to understand complex environments containing many objects, including natural and artificial 'clutter', and objects that can be deliberately added as part of Red denial and deception (D&D) tactics. •This paper presents a simple model for the rapid quantification of the impact of complex environments on STA metrics. The paper demonstrates how models of complex environments can be validated against real world data in a rapid and efficient manner and outlines how Dstl has used the simple model to gain insights on the relationship between environment complexity and performance on combat operations. Finally, the paper identifies areas of combat modelling which are sensitive to complex environments, and proposes improvements to combat modelling processes.

Understanding environmental complexity: a simple testbed model and analysis

JJ Gillard, NHJ Stanbridge*, PR Syms

Dstl LBSD Fort Halstead

26 ISMOR

1st – 4th September 2009

DSTL/CP37864