Camouflage fabrics

Camouflage fabrics R.J. Denning CSIRO Manufacturing, Geelong, VIC, Australia

15.1

15

Introduction

Identification of an object (including living) involves mainly our vision system but may also involve our sense of hearing, smell, and touch. Identification by vision involves two processes, detection followed by identification. Detection involves two processes: (1) Scanning an environment looking for something that appears of interest or out of place. (2) Once detected, focusing on the object to identify it as either of interest or a “false positive.”

Camouflage aims to disrupt both processes. Camouflage, the art of concealment by disguise, is important in the natural environment and for hunters, game watchers, and military applications. The art, which is usually aimed at disrupting the visual shape of the user, has developed significantly since the Second World War where infrared (IR) or low-light surveillance technology was first developed. Concealment of objects or personnel from daylight observation is now a well-established practice where different hues of green, olive, khaki, brown, black, and sometimes other colors are used to reduce the contrast between the camouflaged object and typical bushland surroundings. Other color combinations are used for different environments such as desert or snow. Textiles such as nets, covers, or uniforms are often used to camouflage objects and personnel. Known examples of the use of camouflage date back to the mid-18th century. However, military camouflage was first introduced on a large scale by the British Army in 1902 in the form of a khaki uniform, which has been adopted by all armies by the end of the First World War. Prior to this, most battle dress was brightly colored (Letowski, 2012; Newark et al., 1996; Campbell-Dollaghan, 2014). The introduction of long-range weapons and improved, multispectral observation technology forced a change in camouflage technology. Modern camouflage attempts to break up the outline of the object being concealed and minimize the contrast between the object and the environment in the visible spectrum, through the near IR (NIR) spectrum to the thermal spectrum. Some environments also require specific camouflage to adequately conceal the object. The electromagnetic spectrum from radio to ultraviolet (UV) frequencies is shown in Fig. 15.1, and the general requirements of a camouflage fabric in the frequencies of interest are shown in Table 15.1.

Engineering of High-Performance Textiles. http://dx.doi.org/10.1016/B978-0-08-101273-4.00016-0 © 2018 Elsevier Ltd. All rights reserved.

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Engineering of High-Performance Textiles Radio

Microwave

Infra Red Vis UV Mid IR

Radar

10

104

108 Frequency (Hz)

Radar

Thermal

1012

NIR

1014

1015

Fig. 15.1 The electromagnetic spectrum from radio waves to UV light.

Table 15.1

General requirements and users of camouflage.

Spectral range

Frequency or wavelength

Acoustic

20–20,000 Hz

Radar Thermal

1–18 GHz 90–98 GHz 8000–14,000 nm

MIR

3000–8000 nm

NIR

780–3000 nm

Visible

380–780 nm

UV

200–380 nm

Camouflage general requirements

Principal user

Reduce rustle and swish from fabrics, noise from movement, and mechanical noise Avoid or disguise movement of both people and equipment Temperature of exposed surfaces near to environmental temperature, control emissions from equipment No long distance signal due to water absorption Match reflectance, contrast, and texture of the background Match color, texture, and reflectance of the background Match the reflectance properties of ice and snow

Hunter and military Military Military

Military Military Hunter and military Hunter and military

15.1.1 The solar spectrum Passive detection systems, including eyesight, depend on reflected radiation. The principal source is solar radiation. The solar spectral intensity (ASTM, 2012), shown in Fig. 15.2, is a subset of the electromagnetic spectrum. For convenience, the solar spectrum is often divided into three regions, UV from 100 to 380 nm, visible (380–780 nm), and IR (780 nm–1 mm). The IR region is further divided into the near infrared region [NIR also known as shortwave infrared (SWIR)] between 780 and 3000 nm, mid infrared (MIR) region between 3000 and 8000 nm, and far infrared (FIR) at wavelengths greater than 8000 nm. Visible radiation contributes approximately 44% of the total solar radiation at the surface of the earth and NIR approximately 53% with the remaining 3% being UV radiation. The natural environment is a relatively poor reflector of visible wavelengths. Typical visible reflectance values for bushland are of the order of 10%–20% over

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2.5

Spectral irradiance (Wm–2 nm–1)

UV

Visible

Infrared

2.0

1.5

1.0

0.5

0.0 0

500

1000

1500

2000

2500

3000

Wavelength (nm) Extraterrestrial

Surface

Fig. 15.2 Solar spectral irradiance at the surface and top of the atmosphere plotted using data from ASTM G173-03 (ASTM, 2012).

a range of colors. In the NIR, much higher reflectance is seen from plant material, particularly leaves and values as high as 60% are common over the 700–1000 nm range. A rapid increase in reflectance is observed at the transition from visible to NIR near 700 nm, called the chlorophyll rise (Kokaly et al., 2003; Rubeziene et al., 2008; Datt, 1999; Escribano et al., 2010; Goward et al., 1994). Camouflage materials attempt to match this reflectance profile with green and khaki colors. Other environmental elements such as sand, soil, rock, and tree trunks show an increase in reflectance in the NIR compared with the visible wavelengths. The addition of water results in two broad absorption peaks near 1450 and 1940 nm, which appears as reduced reflectance at these wavelengths in Fig. 15.3. Streck et al. (2003) measured the spectral signature of some soils between 400 and 2400 nm. They found the reflectance varied significantly depending on the soil type and moisture content. Sand was the most reflective material studied, while sandy soil was more reflective than dark loams. Adding water decreased the overall reflectance and increased the water absorption bands. The NASA Jet Propulsion Laboratory, California Institute of Technology (JPL) has developed a library of spectral reflectance data for many natural and synthetic materials, available at http://speclib.jpl.nasa.gov/search-1.

15.1.2 The visual system The design of camouflage patterns is dependent on the application. The principal users of camouflage are the military and hunters; however, these two groups have significantly different requirements. Hunter camouflage primarily requires visible matching

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80 70

% Reflectance

60 50 40 30 20 10 Dry soil

0 300

800

Wet soil

Bark

Wet sand

1300

1800

2300

Wavelength (nm)

Fig. 15.3 Reflectance spectra of environmental samples (Denning, 2016).

with the environment and extending slightly into the UV to match the vision of the most animals, which is shifted from human vision to slightly shorter wavelengths. Deer eyes, for example, have lower sensitivity to red wavelengths and more sensitivity to blue and violet than human eyes (VerCauteren and Pipas, 2003; Jacobs et al., 1994), which enables them to see into deep shadow. Military camouflage covers a much wider wavelength range. Visibility is dependent on the available detection technology. Human vision can be enhanced by the use of UV cameras, NIR cameras and vision systems, and thermal cameras and vision systems that convert wavelengths we cannot see to visible images. Radar can also be used to see through foliage and camouflage nets or other devices used to hide the presence of people or equipment. Human vision and color perception depend on the total ambient light. In bright light, red color is perceived as more intense, while in dull light, green and blue appear brighter (Boynton, 1990). In addition to apparent color shifts depending on the ambient light level, the human vision system interpolates observed shapes to fill in the gaps. Cues such as shading, direction, and symmetry are used to recognize objects (Maguire et al., 1990). This has implications for the design of modern disruptive camouflage, where symmetry in the pattern should be avoided.

15.2

Detection technologies

Detection technologies can be divided into three groups: optical-, acoustic-, and movement-based detection systems.

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15.2.1 Optical technologies Optical technologies can be divided into four types: thermal, low light, visible, and UV devices. Low light and visible devices are the most commonly used, although thermal devices are becoming more common. Combined devices also have a growing popularity, particularly in military applications. Low light sighting devices have been used by the military since the Second World War. Initially, these devices were active IR devices such as the sniper scope, which illuminated the target with a NIR source. These could be defeated using low NIR reflectance fabrics. Active devices were replaced with passive devices that amplified reflected natural light, initially in the visible range, under moonlight. Moonlight has similar spectral characteristics to sunlight, with approximately two-third the energy between 400 and 700 nm. Using shadows and natural concealment provided good protection from observation with these devices. The next development was night vision devices. These amplified both the visible light and NIR light. The optical range was considered to be 400–900 nm. On a moonless night, there is more energy in the NIR range (700–900 nm) than the visible range. As the source is scattered radiation in the atmosphere rather than a “point” source such as the moon, there is greater difficulty hiding in shadows from these devices. Uniform specifications were therefore developed to match the NIR reflectance of the environment over this wavelength range, for example, the (NATO) specifications shown in Fig. 15.4. Recent developments have seen the technology diverge in several directions including the reemergence of active systems. NIR lasers, as used in active sights, are common over the wavelength range of 750–850 nm, and “eye-safe” lasers at wavelengths greater than 1500 nm are commercially available (Ettenberg, 2005). Military

70 Visual

Near infra red Khaki

60

% Reflectance

50

60 + 5%

Green 45 + 5%

40 NATO IRR green envelope

30

Brown <25% 20 10

Black

<10%

0 400

500

600

700

1000 800 900 Wavelength (nm)

1100

1200

Fig. 15.4 NATO camouflage reflectance specifications reproduced from Scott (2005).

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NIR illuminators are available that are essentially NIR spotlights for use with NIR cameras and sights. These have found application as vehicle headlight replacement when combined with night vision goggles. Passive systems have also developed significantly. Recent developments have seen improvements in the resolution of NIR and FIR images (Fig. 15.5) and the extension of the observed spectral range by the combination of NIR and visible (daylight) or NIR (400–1000 nm) and FIR (also known as LWIR or thermal, 9–14 μm) technologies. These systems use two cameras that combine NIR reflected images with visible reflectance or thermal emission images, respectively, to give an enhanced, artificially colored composite picture, often called a hinted picture, such as that shown in Fig. 15.6. Typical spectral response curves for visible and NIR photodetectors are shown in Fig. 15.7 (Thorlabs Inc., 2016). NIR cameras that operate over the spectral range of 400–1700 nm are readily available (Fig. 15.8), and new cameras and sensors are becoming available that extend the operating range to 2500 nm (UTC Aerospace Systems (Sensors Unlimited Inc.), 2016). Fig. 15.7 shows these devices have limited sensitivity at visible wavelengths; however, stacked devices, or two devices on a single chip, are available that combine long wavelength sensitivity with near visible range devices and extend the observed range into the visible wavelengths of current night vision devices (De Borniol et al., 2012; Coffey, 2011; Chuh, 2004).

Fig. 15.5 High-resolution thermal image (https://www.x20.org/product/x28-clip-onthermal-rifle-scope/). Reproduced from Hyperstealth Biotechnology with permission.

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Fig. 15.6 Example of an urban night scene with NIR (SWIR) (4a) and LWIR (4b) images and a Hinted SWIR composite, color-enhanced image (4c) produced by a blending SWIR with thermal hints with an Equinox ARC-1000 engine (http://www.sensorsinc.com/hinted-swir.html). Reproduced from Hyperstealth Biotechnology with permission. 1.6 InGaAS 1700

1.4

InGaAS 2500 Response (A/W)

1.2 Si 1

Ge

0.8 0.6 0.4 0.2 0 0

500

1000

1500

2000

2500

3000

Wavelength (nm)

Fig. 15.7 Response (sensitivity) curves for typical photodetectors based on data from Thorlabs Inc. (2016).

15.2.2 Acoustic technologies While acoustic signatures have been used for many centuries, it is only relatively recently the technology has advanced. This is due to advances in data processing and electronics. Early uses include the use of acoustic sensors to detect troop

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Fig. 15.8 NIR image of a soldier before bushland using a NIR camera sensitive over the wavelength range of 900–1700 nm. Photo courtesy of Dr. Austin Richards, FLIR Systems.

movements in Vietnam and for monitoring the demilitarized zone in Korea (Metcalf, 1995; Kaushik et al., 2005). Increased sensitivity and signal processing, combined with other detectors such as IR, are able to detect ground troops, munitions fire, and vehicles (Kumar and Shepherd, 2001).

15.2.3 Movement detection technologies The best detector of movement is the human eye (Maguire et al., 1990). Movement triggers a response that draws our attention to the moving object, aiding identification. Electro-optical detection is by image analysis of a series of images. Sophisticated routines have been developed, which will not be discussed here, to allow autonomous machines to detect movement. These same routines can be used to show the presence of troops or partially camouflaged vehicles on a battlefield. The most common detection technology is, however, the battlefield radar. Synthetic aperture radar uses movement of the radar platform to increase the resolution over other radar methods. Increased resolution and changing viewing angles reduce the effects of ground clutter to give a clear three-dimensional image. Moving objects appear out of focus or smeared in the direction of movement, giving a signature by which they can be recognized (Goldstein et al., 2010). Alternate radar methods that use Doppler shifts to identify moving targets, such as Ground Moving Target Identification, are traditionally used to detect moving vehicles or planes. These systems have difficulty separating slow moving human targets from ground clutter (McDonald, 2015).

15.3

Textiles for camouflage

Camouflage textiles are primarily used by the military and to a lesser extent by hunters. Military camouflage can be in the form of fabrics (uniforms, protection suits, ballistic protection, tents, tarpaulins, backpacks) or nets to protect assets and

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personnel. The patterns used are designed to blend into a number of environments and take advantage of human perception limitations. Hunter camouflage tends to be designed for specific environments, taking on the pattern of the environment and may include safety coloration such as orange that is poorly observed by animals.

15.3.1 UV camouflage UV camouflage is of most importance to hunters. As stated above, many animals have enhanced eyesight in the blue/UV part of the spectrum. Camouflage for this application should therefore match the UV reflectance of the environment. There are many patents in this area (Butz, 2012). Fluorescent fabrics such as optically brightened fabrics absorb UV light and fluoresce blue so are brighter than the surrounding vegetation, making the hunter easy for the prey to see if used. Many commercial products are available to reduce the fluorescence of fabrics. Washing powders also often contain optical brighteners. These should be avoided to protect the UV camouflage. Military applications are restricted to snow and ice camouflage. Snow and ice have a high UV reflectance, so camouflage for these environments typically enhances the UV reflectance of the fabric. Titanium dioxide or zinc oxide is common white pigments, either added to synthetic fibers or as print pigments. Both compounds, however, have strong UV absorption spectra, and therefore have less reflectance than the environment, allowing easy observation with UV sensitive cameras if used. Zirconium dioxide, barium sulfate, and aluminum silicate are a better match to snow and ice (Saravanan, 2007; Buechner-Hoetzler, 1992). Digital camera sensors are sensitive to UV, visible, and short wavelength NIR radiation. Filterers are usually incorporated in the camera to remove the UV and NIR; however, filters for digital cameras that allow observation in the UV are readily obtained and low cost. Any camera can therefore be converted to a UV sensor to assist detection; hence, the contrast between the uniform and environment needs to be managed. Fig. 15.9 shows the effect of using a UV enhancing filter in the observation of a soldier in bushland. The camouflage is effective at visible wavelengths but is clearly visible in the UV. Typical reflectance values of different surfaces are shown in Table 15.2. Fabric treatments have been developed that manage the UV reflectance of uniforms, for example, UVRC (UVR Defense Tech Ltd., 2012). Many Vat dyes used for printing camouflage fabrics also show strong absorptions in the UV, giving low reflectance similar to bushland, increasing the effectiveness of the camouflage against UV detection.

15.3.2 Visible camouflage The objective of visible camouflage is to disguise or break up the outline of a person or object observed under visible light conditions by reducing or eliminating the contrast with the background and disguising the edges of the object. This is usually achieved by selecting colors and patterns found in the background or by breaking up the outline with bold, contrasting colors. Bold contrasting colors that break up the edge of the target are most effective (Cuthill et al., 2005; Selj, 2015). Detection technologies

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Engineering of High-Performance Textiles

Fig. 15.9 MultiCam in visible light and using a UV filter. Reproduced with permission from UVR Defense Tech Ltd., 2012. Providing ultraviolet camouflage for any terrain. Available from: http://www. uvrdefensetech.com/index. php?home (accessed 25.08.16).

include eyesight, binoculars, cameras, and gun scopes. Observation may be enhanced by computer-aided image analysis. Visible camouflage should match the color, gloss, texture, and appearance of the surrounding environment. When we scan an environment, the human visual system looks for anomalies in the general pattern such as prey (human or animal) in bushland. The human visual system as it relates to camouflage has been described in detail (Frisˇkovec and Gabrijelcˇicˇ, 2010). They describe a set of criteria for visual camouflage design. Human vision and perception interpolate the three-dimensional shape from partial cues including motion, stereo, or parallax observations and position (Carman and Welch, 1992).

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UV reflectance of some natural materials, fabrics, and dyes

Table 15.2

Surface Snow Sand (dry) Loam/soil Grass field Tree leaves Tree bark Water Concrete Polyester Cotton Optically brightened fabric Camouflage dyes (MultiCam)

% UV reflectance (wavelength where available) 98 (360 nm) 90–92 (350–400 nm) 22 8 (365 nm) 5 (320–400 nm) 5–6 (300–400 nm) 15–50 5 15 <5 (320–400 nm) 30–50 (300–500 nm) 15 (360 nm) 120 (450 nm) 4 (dark brown) to 19 (khaki) (360 nm)

Reference Grenfell et al. (1994) Leblanc et al. (2016) Curry (2012) Coulson and Reynolds (1971) Coulson and Reynolds (1971) Grant et al. (2003) Curry (2012) Curry (2012) Curry (2012) Zimehl et al. (2004) Liu et al. (2010) Own work Own work

A review of camouflage and visual perception identifies a number of factors that aid in the identification of camouflaged objects. These include the intensity of the diffuse reflected light, which for glossy surfaces are observation angle dependant; intensity of borders relative to the background, the presence of shadows, lighting effects, and texture; and the color and reflected light and movement of the prey or background. The implications for camouflage are to match the texture, color, and intensity to the background; provide false edges that hide the true shape; provide high contrast details inside the edges that do not reveal the true shape; and mimic the movement of the background or randomize movement (Troscianko et al., 2009). The first camouflage patterns were developed around 1909 by artists, using concepts such as color theory, cubism, impressionism, and futurism using disruptive outlines and abstraction to conceal the subject. During the Second World War, the additional function of friend or foe identification was added to the requirements of camouflage patterns as each nation developed their own patterns. Military camouflage is, by its nature, a compromise pattern. While recreational camouflage can use many colors and patterns specific to a particular environment, such as patterns that mimic particular bushland for deer hunters or the reed patterns of swamps for duck hunters, military camouflage must provide protection in a range of environments and at both short and long distance, which requires both large and small pattern elements. A number of patterns have evolved to provide this level of protection, based on different theories that will not be discussed here. One of the earliest patterns was the “duck hunter” pattern developed during the Second World War, on which the Australian DPCU may be based. This pattern used

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Engineering of High-Performance Textiles

relatively large spots of a limited range of colors and derived its nickname from its popularity with duck hunters. Patterns developed around the same time include Woodland, Flectran, and Frog Skin. The Australian DPCU pattern, developed in the late 1970s, was based on the color of the Australian bush for use in bushland. It broke the outline of the wearer and matched the texture of the Australian bushland (Boyd, 1995). The next major development was the Canadian CADPAT (Clausen et al., 2009; King, 2014), which used a granitic pattern, often referred to as a pixilated pattern, based on the work of Lieutenant Colonel Timothy O’Neill to break up the edges between colors. O’Neill states in his report (O’Neill et al., 1977) “At longer ranges, the Dual-Texture Gradient (DTG) pattern merges into macropattern of broad light and dark areas which matches the texture of the ground at that distance. At closer range (under optical magnification) a micropattern resolves which provides a continuing match with the background texture. This micropattern is conceived as based on a square grid: not because squares are particularly effective shapes for concealment, but because pattern design is simplified by developing the micropattern from the US Army pattern (as a macropattern) by computer, as a relatively simple linear programming technique.” Hence, pixilated is an incorrect description of these patterns as any shape could have been used to produce the pattern; however, squares are easier for a computer to handle. CADPAT uses relatively small patches of color on a single scale. Many US designs are based on this pattern. Other pattern categories include Tigerstripe, Puzzle, Lizard, and Splinter. A recent trend is the development of “universal camouflage” patterns suitable for a range of environments (Dugas et al., 2005). One of the first attempts of a universal camouflage pattern was the US Army UCP (Universal Camouflage Pattern) (Table 15.3). This pattern was considered a failure in operations and in many environments made the wearer more visible. US Army trials showed this pattern performed poorly in all environments (Unknown, 2009). MultiCam is the latest offering in universal camouflage patterns. It uses the same theory as CADPAT, broken boundaries between colors. Rather than using a pixelated boundary as used in CADPAT, MultiCam blends the two adjoining colors into each other, effectively eliminating the boundary. MultiCam is, however, more of a compromise than camouflage designed for a specific environment as it attempts to be a universal camouflage. US army evaluations (Unknown, 2009; Hepfinger et al., 2010) show the camouflage performs well in most environments but is outperformed by purpose designed camouflages as shown in Table 15.4. Despite these deficiencies, MultiCam was the camouflage of choice by US soldiers in Afghanistan (Hepfinger et al., 2010). The US Army has since introduced three variants of MultiCam, colored for desert, woodland, and transitional terrains (United States Government Accountability Office, 2012).

15.3.3 NIR camouflage The most common low light or NIR surveillance equipment is the “night vision” equipment, for example, night scopes and binoculars or cameras. These traditionally operate over the wavelength range of 700–1200 nm. However, recent developments in

Camouflage fabrics

Table 15.3

361

Examples of camouflage pattern classes

Example pattern

Name and class UCP Granitic

CADPAT Pixilated Public domain (Wikipedia)

MARPAT Pixilated Public domain (Wikipedia) German WWII Erbsenmuster Flecktarn Public domain (Wikipedia)

US Airforce Tigerstripe Public domain (Wikipedia)

British disruptive pattern Woodland Public domain (Wikipedia)

Multicam Multiterrain (Denning, 2016)

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Engineering of High-Performance Textiles

Effectiveness of camouflage patterns in different environments

Table 15.4

Pattern MARPAT Woodland French Iraq China MARPAT Desert Desert Brush Desert British UCP MultiCam

Desert

Urban

Overall ranking (multienvironment)

2

11

12

Poor

4 5 1 11

10 13 12 3

13 7 10 1

Poor Poor Poor Good

10 13 12 6

4 1 9 6

2 3 8 4

Good Good Poor Good

Woodland

Number indicates ranking in observation distance in US Army trials (Unknown, 2009; Cramer, 2013; Hepfinger et al., 2010), where 1 is the least observable (able to get closest before observation) and 13 the most observable.

sensor technology have seen the range extended to 2500 nm (Coffey, 2011). This region is normally referred to as the NIR that is associated with reflected light rather than emissions from hot objects. Fabric printing to give good camouflage in the 700–1000 nm region is well established with a large number of publications and patents in the area. Burkinshaw et al. (1996) reviewed the technology in 1996. Their conclusion was “Clearly, the requirements of infrared camouflage will change as advances in infrared surveillance technology are made, particularly as observation of the battlefield at longer wavelengths of the near infrared becomes feasible. As long as existing infrared observation equipment continues to be improved, or new devices introduced, efforts to develop near infrared camouflage to counter the technology will continue.” Even though detection technology has significantly advanced since 1996, NIR camouflage technology has changed little since this review was written. Current NIR or SWIR reflectance requirements for camouflage fabrics are specified to approximately 1000 nm, which is the spectral range of generation 1 night vision devices, with a few exceptions depending on the country. For example, the reflectance properties of Australian uniforms are specified to 1050 nm, while the NATO specifications (Fig. 15.4) are extended to 1200 nm and Sweden, Norway, and Canada specify the NIR reflectance properties to 2000 nm (Scott, 2000). Fig. 15.10 shows several camouflage uniform observed with at different wavelength ranges using several cameras showing the advantages of observation in the NIR and SWIR range. A number of investigations of the NIR reflectance of natural materials have been carried out. Many of these studies were aimed at mapping vegetation and land use (Barry et al., 2009; Clark et al., 2003; Datt, 1999; Goward et al., 1985, 1994;

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Fig. 15.10 An example of the effectiveness of visible (A), NIR (B), SWIR (C), and thermal (D) images under low light conditions (Lagueux et al., 2013) used with permission.

Kokaly et al., 1998, 2003) using the spectral range of 400–2500 nm, although some used a reduced range of 400 nm to approximately 1000 nm. In general, green plants show a weak reflection in the green region of the visible spectrum and a rapid rise in reflectance between 700 and 800 nm, where the reflectance plateaus to approximately 1300 nm. A slow decrease in reflectance is then observed with two strong absorption bands centered near 1450 and 1940 nm attributed to water associated with chlorophyll. Typical reflectance curves are shown in Fig. 15.11. Dry plant material shows weaker absorption bands at 1450 and 1940 nm and a slower rise in reflectance between the visible and NIR wavelengths, which has been attributed to the lack of chlorophyll and water (Elvidge, 1990). Most cotton textile systems use vat dyes to achieve the required NIR spectral reflectance. Anthraquinoid-type dyes, such as Vat Green 3 shown in Fig. 15.12, have suitable NIR reflectance and absorption properties over the wavelength range of 700–1000 nm. Vat dyes also have good light fastness and wash fastness and a range of colors suitable for military applications (Burkinshaw et al., 1996). Typical vat dyes

75 70 60 50

%R

40 30 20 10 0 −10 −19 320

500

1000

1500

2000

2500

nm

Fig. 15.11 Reflectance spectra of fresh (green line) and dry (yellow line) Eucalyptus leaves (Denning, 2016).

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Fig. 15.12 Vat Green 3 structure (anthraquinone type). O H N O

O

Some typical vat dyes used for camouflage dyed cotton fabric and pigments used for printing camouflage (Burkinshaw et al., 1996; Gupta et al., 2001)

Table 15.5

Color index vat dyes

Color index pigments

Vat Black 27 Vat Black 30 Vat Brown 1 Vat Brown 6 Vat Brown 35 Vat Green 28 Vat Orange 15 Vat Red 24

Pigment Pigment Pigment Pigment Pigment Pigment Pigment Pigment

Black 31 Black 32 Green 36 Green 7 Red 101 Red 146 Red 179 Yellow 12

used to produce camouflage colors are shown in Table 15.5. A combination of three anthraquinone vat dyes, such as Vat Blue 6, Vat Yellow 2, and Vat Red 13, have been used to match the NATO reflectance specification over the wavelength range of 400–1200 nm (Goudarzi et al., 2014). A range of pigments suitable for noncellulosic fibers were also developed. Almost all military camouflage uniforms use NIR reflecting dyes and pigments to mimic the chlorophyll rise and plateau over the spectral range 700–1000 nm. Developments over the past 15 years have focused more on the visible pattern than the NIR reflectance at longer wavelengths even though the observation technology has been available for a considerable portion of this time. Canada appears to be the first country to publish camouflage specifications that extend to wavelengths longer than 1200 nm. In their patent (Clausen et al., 2009), they gave spectral reflectance curves over the range of 400–2000 nm. The general shape of the curves shows a small reflectance in the visible region corresponding to

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the color followed by a rapid rise in reflectance near 700 nm to a plateau between 800 and 1300 nm. Following the plateau, an absorption centered on approximately 1450 nm is seen. Typically used camouflage colors have similar reflectance in the NIR with light green and brown colors at approximately 60%, average green slightly lower between 800 and 1300 nm and black averaging approximately 4% reflectance. Two methods other than dyeing or printing have been suggested for improving the NIR reflectance properties of fabrics. Leong (2008) suggested a series of nanomeshes, wire mesh in the fabric, could be tuned to absorb particular wavelengths such as 1900 nm. Meshes could be combined with fibers that absorb at 1400 nm to give good spectral coverage. Liu et.al (Liu et al., 2008) designed a biomimetic camouflage material based on a polyurethane foam that had a similar structure to leaf cells. When the foam was filled with water, the reflectance spectrum was a very good match to leaves. Synthetic fibers typically have very high NIR reflectance when compared with the natural environment. Piece dyed nylon shows a maximum reflectance near 70% at 800 nm. Frankel et al. developed a method to reduce the signature by including nano- and micro-sized particles in the fiber during production (Frankel et al., 2004; Frankel, 2006). Reflectance values similar to the natural environment could be achieved. The US Army has also developed a wool/Nomex flame-resistant fabric with acceptable NIR signature (Mehta et al., 2008).

15.3.3.1 Other uses of NIR reflective dyes Much of the dye development in recent years has been aimed at the development of improved digital recording media and optoelectronics. Fabian et al. (1992) reviewed these applications and a range of NIR absorbing dyes. Dyes identified with NIR absorption maxima near 1450 nm were unstable or not isolated as pure compounds. The other major use of NIR reflective dyes and pigments is “cool colors.” These are formulations that reduce the temperature of surfaces exposed to sunlight by increasing the reflectance of the surface. Levinson (Levinson et al., 2005a,b) reviewed the NIR reflectance of a range of pigments. They found inorganic pigments have good reflectance in the NIR and a strong broad absorption near 1500 nm when included in paint films.

15.3.4 Thermal camouflage All surfaces emit radiation at wavelengths dependant on the temperature. For a relatively cool surface, this emission is in the thermal IR region of the spectrum between 3 and 14 μm as shown in Fig. 15.13. A human body at 36°C has an emission maximum at 9.4 μm, while a background at 20°C has a maximum emission at 9.9 μm. The intensity of the emission is also dependant on the temperature, given by the StefanBoltzmman equation, as shown in Fig. 15.14, increasing as the temperature increases (Howell et al., 2010).

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Wavelength max emission (mm)

11

Wavelength max emission (mm)

10 9 8

10.5

20°C

10

36°C 9.88 mm

9.5

9.37 mm

9 8.5 10

20

7

30 40 Temperature (°C)

50

6 5 4 3 0

100

36

200

300

400

500

Temperature (°C)

Fig. 15.13 Black body emission wavelength maxima over the temperature range 10–500°C based on Wien’s displacement law.

Radient energy differance (W/m2)

800

600

400

200

0 0 −200

20

40

60

80

100

120

Temperature (°C)

Fig. 15.14 Change in radiant energy relative to an environment at 20°C calculated using the Stefan-Boltzmann law.

Thermal detection cameras are gray scale cameras that convert IR energy into electrical signals. As the energy emitted by a surface is dependent on the surface temperature, warmer objects give higher signals than cooler objects and therefore appear lighter in color. Temperature differences as low as 0.05°C can be detected (FLIR Systems Inc., 2016). Mammals can therefore be easily detected by thermal

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imaging devices (Boonstra et al., 1994). At rest, the human body emits approximately 200 W energy; however, under heavy exertion, this can increase to 1000 W, leading to skin temperature, increasing the exposure to thermal detection (Albertoni, 2011). False color can be applied to color objects at a selected temperature as shown in Fig. 15.6. To camouflage an object from thermal emission detection, the observed surface must be the same temperature or have the same emission intensity as the surrounding environment (Lee, 1999). This can be achieved using low emissivity coatings (Calvert et al., 1984; Kelsey et al., 2011; Lars and Karlsson, 1985; Zhang et al., 2009), the use of screens, mylar sheets, thermal blankets (Hellwig and Weber, 2006), insulation blankets, and nets (Saravanan, 2007), or clothing that reflects the radiation away from the observer (Kastek et al., 2012; Lagueux et al., 2013). Clothing design is also important for controlling thermal emissions. An air gap of at least 9 mm had been shown to reduce the thermal emission of a fabric in a simulated man/fabric/environment experiment to a similar intensity as the environmental emissions (Lee, 1999). The Sniper Suit, which uses layers of fabric or yarn and air to control the temperature gradient between the body and environment, is also effective (Glowtrade (M) SDN. BHD, 2010). Phase change materials (PCMs) may be included as thermoregulators of the fabric and therefore shield the object from thermal detection (McKinney et al., 2002). PCMs are generally compounds with high latent heat that melt at the temperature to be maintained. Incorporating the material in fabrics allows excess heat, such as from a body at work, to be absorbed while maintaining the temperature of the fabric close to that of the environment. The major disadvantages of PCM’s are (a) the effect lasts only as long as the latent heat is not fully absorbed, (b) the PCM increases the weight of the fabric, and (c) the fabric stiffness increases (Tang and Stylios, 2006). The duration of the effect depends on the air movement within the garment and number of layers of fabric containing PCMs (Ghali et al., 2004).

15.3.5 Acoustic and movement camouflage Acoustic sensor systems analyze the collected sound for distinctive target signatures. This could be the footsteps of marching troops, vehicle noise, gunfire, or even the sound of fabric rustling (Metcalf, 1995). Animals are particularly sensitive to foreign sounds, hence hunters avoid fabrics that rustle and Velcro-type fasteners. Fabrics have been used from the early days of mechanized warfare such as the use of fabrics to muffle the sound of cannon wheels through to the use of sound insulation bats to muffle engine noise. The use of decoys to mask acoustic signatures is under investigation by the Army Research Laboratory (Letowski, 2012). Movement can be camouflaged by the use of striking contrast patterns such as the stripes of a tiger. Early “Dazzle” patterns were designed to confuse the eye so location, direction, and speed could not be easily determined (CampbellDollaghan, 2014).

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Engineering of High-Performance Textiles

Movement detection by radar is best camouflaged by scattering of the radar signal and reducing the radar cross section to match that of the background. Common methods using textiles and nets include using conductive fibers or films and radar absorbing materials (Redlich et al., 2014; Yu et al., 2007). A low percentage of carbon fiber (0.036%) in a 10 mm thick nonwoven mat has been shown to be an effective radar absorber over a range of 8–18 GHz (Zhu et al., 2007).

15.3.6 Hybrid systems Hybrid detection imaging systems combine more than one detection technology into a single image. Early systems combined visible images with thermal images (Ardeshir Goshtasby and Nikolov, 2007), often applying false color to the thermal image as shown in Fig. 15.6. Recent developments include visible, thermal, and NIR vision systems combined to a single image (Coffey, 2011) and multispectral single chip room sensors (Chuh, 2004). Camouflage for these systems involves combining the techniques mentioned above into a single fabric. Camouflage nets have been developed that hide objects from visual observation, combine NIR and thermal barriers and metallic films for radar scattering (Pusch et al., 1985; McCullough et al., 1994). Some examples of commercial multispectral net camouflage systems are found in the following Web sites (Saab AB, 2016; Miranda Military, 2016; Intermat Group SA, 2016; Geiger International, 2016).

15.4

Future trends

Camouflage development tends to follow sensor development. As a camouflage for a particular sensor is developed and proved effective, new sensors are developed to counter the camouflage. Sensor and detection system development is progressing in two directions: (a) miniaturization and extended wavelength multispectral sensors and (b) active source/sensor systems (Coffey, 2011). Two areas of particular interest in camouflage research at present are active camouflage and multispectral universal camouflage. Active camouflage is defined as a system that changes to match the environment in real time (McKee and Tack, 2007). Tachi Laboratories (Japan) have developed a Retroreflective Projection Technology that takes an image from behind the object to be camouflaged and projects it on the front of the object (Tachi, 2003). Camouflage is one proposed application of this technology as the retroreflective object appears transparent. Other active camouflage approaches have been reviewed by Schwarz and are summarized in Table 15.6 (Schwarz, 2015). Multispectral universal camouflage is still the holy grail of camouflage research. ˚ kerlind, 2014) summarizes the state of the art for A recent review (Andersson and A coating technologies. While improvements have been made, there is still no universal visible camouflage. Guy Cramer, CEO of Hyperstealth Biotechnology Corp, states that “In the past Camouflage design has been a semi random placement of color and shape to disrupt the targets true shape, or camouflage patterns have attempted

Camouflage fabrics

Table 15.6

369

Types of active camouflage

Principle

Description of textile application

Mechanical-chromic

Reversible coat poncho or net with different camouflage patterns (e.g., woodland and desert), reflectivity, or texture on each side. The main disadvantage is only two states are available Thermochromic dyes and pigments or polymers that change color with changing temperature. These systems require a temperature change to effect the color change, either environmental (sunlight) or electrical heating Materials change color by the application of a voltage. Examples include dyes, metal oxide films, and conduction polymers that change color by a redox reaction. Mechanical robustness and the need to apply a voltage are the main disadvantages. An example of an electrochromic fabric is shown in Fig. 15.16 Active devices emit light, for example, LED’s, OLED’s, backlit displays, and plasma displays. Power requirements could be as high as 10 sW/m2 with power supplies weighing a few kilograms Thermal insulation using air or inert gas to inflate chambers in a garment or fabric panel. Increased insulation from the inflated chamber reduces thermal signatures. As the air in the chamber heats over time, continuous flow may be needed to maintain thermal equilibrium with the ambient temperature Heating or cooling elements to mask thermal signatures. Devices such as heating mats or pads and Peltier elements could be used. Peltier devices are relatively inefficient but can provide either cool or warm surfaces

Thermochromic

Electro-chromic (passive)

Electrochromic (active)

Mechanical-thermic

Electrothermic

to mimic natural camouflage, in both cases these designs actually go too far in random patterns or specific mimicry to provide a better camouflage. Our subconscious actually notices these fractal shapes and once the brain has identified them as natural and commonplace in particular settings the subconscious retains a memory of those shapes. When the visual part of the brain analyzes a setting, it quickly notes the common fractals it’s seen in the past and ignores them, treating them as background noise—not something which requires further scrutiny. If the pattern is not a fractal—such as a random placement of blobs, it may in-fact stand out to the visual system, the subconscious will note that something is out of place—an anomaly. If the pattern is too specific, such as many hunting patterns with trees and branches, this becomes easier to detect once placed in a different background than the camouflage was designed for. A fern is a fractal—the small leaves that make up the large leaf of a fern are miniature versions of the larger leaf” (Cramer, 2013). Quantum Stealth (Fig. 15.15) may be the first truly universal camouflage.

Fig. 15.15 Photo of a mock-up of Hyperstealth Biotechnology Inc’s “Quantium Stealth” fabric. Used with permission from Hyperstealth Biotechnology Corp.

Fig. 15.16 Example of “Smartcamo,” a colur adaptive camouflage fabric showing change from Desert to Transitional to Woodland colors. Used with permission from Hyperstealth Biotechnology Corp.

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