Understanding environmental complexity: a simple testbed model and analysis

1. 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

2. 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.

3. Urban and rural clutter examples* • Rural clutter, eg: • Sheds, rubbish, animals; • Abandoned machinery • Urban clutter, eg: • Cars, lorries • Street furniture, civilians * Urban and rural clutter categories not exclusive

4. 10m 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. y-axis x-axis Observer

5. Simple data entry and results presentation

6. LoS interruption: buildings and vegetation Reality Map Map • 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° • Mapping only an approximation: • vegetation underestimated • buildings overestimated P(LoS)max vs graze angle

7. LoS interruption: clutter 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 • 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

8. Target Point of fixation at which observer first detects target in his peripheral vision Conspicuity = Angular distancefrom target to the point of detection Observer 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

9. 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

10. 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.

11. 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

12. 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.

13. 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.

14. 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