PERFORMANCE OF THE @@@ THERMAL IMAGER IN U.S. COAST GUARD APPLICATIONS

Report Prepared by:Everett George

Dahlgren DivisionNaval Surface Warfare CenterElectro-Optical Systems Branch (F44)Dahlgren, VA 22448

Technical Revision:1991-09-30

Format Revision:2014-05-08

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TABLE OF CONTENTS1.0 INTRODUCTION................................................................................32.0 BACKGROUND INFORMATION........................................................3

2.1 SOURCE RADIATION...................................................................32.2 ATMOSPHERIC ABSORPTION....................................................52.3 FLIR PERFORMANCE..................................................................72.4 TARGET DETERMINATION..........................................................82.5 PROBABILITY OF DETERMINATION.........................................10

3.0 METHODOLOGY.............................................................................103.1 LOWTRAN PROGRAM...............................................................113.2 CONTIRR PROGRAM.................................................................113.3 TIS PROGRAM...........................................................................11

4.0 ANALYSIS RESULTS.......................................................................124.1 ATMOSPHERIC CONDITIONS...................................................134.2 TARGET SIZE.............................................................................134.3 SENSOR FIELD-OF-VIEW..........................................................144.4 DETERMINATION PROBABILITY..............................................144.5 BOAT TYPE AND ORIENTATION...............................................154.6 GEOGRAPHIC LOCATION AND SEASON.................................15

5.0 CONCLUSION.................................................................................166.0 REFERENCES.................................................................................16

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1.0 INTRODUCTION

The purpose of this report is to provide information concerning Forward Looking Infrared Receiver (FLIR) performance in U.S. Coast Guard (USCG) applications. The data in this report is intended to aid in determination of specification parameters for USCG procurement.

This report has six sections. Section 1.0 is this introduction. Section 2.0 gives basic background information in FLIR nomenclature, applications, and modeling. Target radiation, atmospheric effects, FLIR performance, information levels of target determination, and target determination probabilities are discussed in this section. Section 3.0 describes the methodology and software models used in FLIR analyses. This section provides information that FLIR performance analysts may use to reproduce or expand on this study if necessary. Section 4.0 states the findings of this report. Section 5.0 concludes the report, and Section 6.0 gives reference material used in the report development.

The FLIR analyzed for this application is the @@@ Thermal Imager which monitors the 3 to 5 micron infrared (IR) wave band. The basic application of the FLIR will be in a @@@ environment. The sensor will be mounted shipboard topside with the sensor aperture @@@ feet above the ship water line. An operator will be required to observe the FLIR display, point the FLIR at and identify targets. The principal target of the FLIR will be a @@@ boat at @@@ nautical miles (nm).

2.0 BACKGROUND INFORMATION

This section gives basic information on technical factors which affect FLIR performance in light of USCG applications. The material is explained at the technical level of a scientist or engineer who may not be familiar with IR emissions, propagation, or detection processes. Information given here is referenced in Section 3.0 and Section 4.0 .

2.1 SOURCE RADIATION

The mechanics behind IR radiation emission determines the types of objects which can be detected by thermal imaging devices. These devices detect wavelengths generated from objects themselves instead of relying on reflected wavelength as with visible light or radar. All bodies emit radiation dependent on their surface temperatures. For extremely hot objects, such as a flame or the sun, emitted wavelengths are in the visible spectrum. Cooler objects with temperatures found at the earth's surface emit light at much longer wavelengths in the infrared band.

Typical intensity distributions of wavelengths emitted by surfaces at different temperatures are illustrated in Figure 2-1. Along the horizontal axis of the figure is plotted radiation wavelength. The graph shows spectral portions of ultraviolet, visible, and infrared light. Smaller X-ray wavelengths would lie to the left of the graph; values of larger wavelengths such as radar would be to the right. The vertical axis of the graph gives the relative spectral intensity of radiation. Extremely hot bodies, such as the sun, emit radiation across the visible light range (0.4 to 0.7 microns). Cooler surfaces present at environmental temperatures emit energy primarily in the infrared spectrum. Additional information concerning thermal radiation can be found in standard physics text books such as Reference [1] . The @@@ FLIR window, between 3 and 5 microns, spans the smaller wavelengths of environmental IR radiation.

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Figure 2-1 shows a significant separation between spectral intensities of radiation emitted from surfaces of extreme temperature differences. However, other than the sun and stars, temperatures as high as 9955° Fahrenheit are rarely seen in the earth environment. Several energy sources on sea-going vessels such as lights and power plant exhaust are significant IR emitters, but target temperatures most likely to be encountered by the FLIR will be very near the surface air (background) temperature. To a large degree, the ability of a FLIR to detect small temperature differences near background determines its performance.

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Figure 2-1 Spectral Intensities of Black Body Radiation

Figure 2-2 illustrates spectral radiancy within the FLIR window of target and background surfaces with a temperature difference of @@@ degrees Fahrenheit. This difference is representative of @@@ conditions. Figure 2-2 plots emitted spectral radiation energy per surface area as a function of wavelength. Energy levels emitted from different surfaces are the fundamental quantities the FLIR uses to construct the thermal image. The FLIR sums the energy received from these surfaces across the 3 to 5 micron window to determine IR radiation intensity in different directions. Through comparison of adjacent directions, changes in radiation intensity indicate changes in surface temperature which imply the presence of a target.

2.2 ATMOSPHERIC ABSORPTION

For a FLIR to register IR radiation from an object, the radiation must propagate through the atmosphere with sufficient energy to activate sensor elements. Propagation energy losses due to atmospheric extinction play an important role in determining FLIR performance. Radiation losses are determined with respect to wavelength of radiation and are expresses as percent of absorption. Radiation propagation with no extinction has zero percent absorption (transparent); radiation which is completely dissipated has 100 percent absorption (opaque) in the medium.

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Figure 2-2 Target and Background Spectral Radiancy

Figure 2-3 shows percent absorption for wavelengths of radiation emitted through a range of wavelengths. The curve indicates absorption areas present for radiation propagation in a @@@ atmosphere along a @@@ nautical mile path close to sea level. An immediate observation of Figure 2-3 is that large portions of the infrared and ultraviolet spectrum are opaque to radiation propagation, i.e., radiation produced at this wavelength is dissipated in the atmosphere within a @@@ nautical mile range. Narrow windows of transmission allow certain wavelengths of radiation to pass. These windows are dependent on the composition and physical characteristics of gases and other airborne particles in the propagation path. A different composition of other gases would produce a different set of windows.

The window for visible light permits a large degree of radiation at wavelengths from surfaces as hot as the sun (compare Figure 2-1 and Figure 2-3). However, the amount of radiation emitted from surfaces at environmental temperatures is extremely weak within the visible light window. The 3 to 5 micron window used by the @@@ FLIR is closer to peak spectral intensities created from cooler environmental temperatures. A small 8 to 12 micron window is centered on peak intensities of an 85 degree temperature surface. Unfortunately, absorption levels in the 8 to 12 micron window are significantly higher than in the 3 to 5 micron window. The FLIR window monitors IR radiation with less intensity but greater propagation transmission.

Atmospheric absorption of radiation is dependent on many factors which can significantly affect radiation losses. Primary factors include propagation distances and altitude, composition of gases such as water vapor, carbon dioxide and nitrogen, water particle content, amount of aerosols, wind, air temperature and pressure. Figure 2-4 gives absorption values of radiation propagating near the earth's surface for a @@@ nautical miles propagation path. The absorption values are based on atmospheric parameters which reflect a @@@ environment with @@@ visibility.

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Figure 2-3 Atmospheric Absorption

Figure 2-4 shows the major atmospheric constituents which cause absorption in the FLIR window. Water vapor is the largest contributor of absorption. Suspended liquid water particles and carbon dioxide also significantly reduce IR transmission. Other components which have effects are nitrogen, ozone, and aerosols. Absorption characteristics of the FLIR window will change as atmospheric components fluctuate. Changes in humidity and precipitation will influence FLIR detection ranges of targets. Wind blown aerosols produced from plant pollen or pollutants over land may also impact FLIR capabilities in certain conditions.

2.3 FLIR PERFORMANCE

Performance of a FLIR is determined by its ability to detect IR radiation with a high degree of precision in both intensity and direction, and display this information graphically for observation. In many FLIR analyses, this ability is expresses in a Minimum Resolution Temperature (MRT) curve. An MRT curve describing @@@ FLIR performance is shown in Figure 2-5. An understanding of an MRT curve provides insight into the factors affecting FLIR performance. The curve plots the target-background temperature difference against the “spatial frequency” of the target. As discussed in Section 2.1 , image temperature difference gives an indication of the radiation levels the FLIR receives. A good definition of “spatial

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Figure 2-4 Effects of Gas Type on Atmospheric Absorption

Figure 2-5 MRT Curve

frequency” is provided through examination of MRT curve measurements. Further information concerning MRT curve measurements can be found in Reference [2] and Reference [3] .

The determination of an MRT curve employs a procedure which allows test setting measurements to describe FLIR performance in operating conditions. Graduating pattern sizes of parallel bars are observed through a FLIR at various contrast temperatures and right angle orientations. Figure 2-6 shows a typical bar pattern used in the tests. For a fixed range and pattern temperature, the background temperature is slowly changed to increase the temperature difference between it and the pattern. The temperature difference increases until the FLIR operator can barely resolve distinct bars within the pattern. Minimum temperature differences at which different patterns can be resolved at given distances define the MRT curve for a specific FLIR.

The characteristic dimension of the bar pattern is the bar separation within the pattern. This dimension along with the pattern range from the sensor defines the subtended image angle of the pattern as viewed from the sensor. This angle is expressed in milliradians per bar. The resultant “spatial frequency” is defined in the following equation as cycles / milliradians:

f = d / 2

where

d - angle subtended by bar / space cycle in pattern (milliradians)f - spatial frequency (cycles / milliradians)

Patterns with smaller bar separation are observed to determine MRT for larger frequencies.

It is assumed that pattern distances from the sensor are relatively small, and that losses due to radiation propagation to the sensor are negligible. The performance data can then be applied to greater subtended viewing angles of larger targets at greater distances. Although the targets will not have simple bar patterns for identification, it can be assumed that the bar pattern represents the worst-case most complex configuration of a radiating body of a given area. Because temperature differences and subtended viewing angles are used, the MRT curve can be applied over a range of absolute temperatures and target distances.

2.4 TARGET DETERMINATION

FLIR performance is also dependent on the ability of an operator to interpret the FLIR display to detect and identify targets. In many cases, it is not only sufficient to determine that a target is present at a specific bearing, the size, type, or function of a target may also be necessary to determine a course of action. As target range decreases, the amount of information displayed by the FLIR increases. Ranges at which certain levels of information can be supplied by the FLIR must be categorized and defined to analyze FLIR capabilities.

Drawing from military literature in which extensive work has been produced on FLIR performance and

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Figure 2-6 MRT Bar Pattern

target determination of surface vessels (Reference [2] ), information levels of target determination can be expressed as the pixel number of the target discernible by the FLIR. Information levels are divided into three categories dependent on the decisions encountered in FLIR use by the military. Initial “detection” indicates that a target of possible interest is in a given direction. Course heading can then be altered to close range with the target until “classification” becomes possible. With classification, the operator is able to determine if the target is a warship or a commercial craft. If the target is a warship, target range can be decreased further until the third level of determination “identification” is reached. At the third level, the observer is able to identify the class of warship, and a decision can be made to engage the warship if necessary.

Another level of information used for target determination is “orientation”. At this level, the amount of information exceeds that required for detection, but is less than that needed for classification. Due to the large length to height ratio of a ship from the side, and relatively small beam to height ratio in viewing a ship from the bow, ship orientation can be perceived with little information.

Levels of information in the categories are defined through the number of pixels required to construct the image. FLIRs detect and display thermal information in discrete squares or pixels which are used to build a picture. Pixel number is directly related to the FLIR's ability to resolve the target, and gives a direct indication of how much information is available to the operator. Military findings (Reference [4] ) have determined that in a cluttered background, approximately @@@ are required for target detection; @@@ pixels are required for target orientation; @@@ pixels are necessary for target classification; @@@ pixels are required for target identification. Images of a @@@ boat (beam view) at different pixel numbers are shown in Figure 2-7.

Proposed USCG applications of a FLIR parallel military applications with restrictions. Military derived pixel numbers for target detection and orientation categories should apply to that of USCG. However, based on applications of the FLIR to smaller boats, “classification” and “identification” criteria for the Coast Guard may differ.

In military target determination, “classification” occurs when a target can be perceived as commercial or military; then a decision can be made to pursue. Although military verses commercial determination of a target provides information of limited use in USCG applications, the same concept can be used to formulate more applicable classification criteria. The primary attribute of a sea vessel in USCG FLIR usage is size. Assuming a “detection” has occurred, determination of target size can eliminate many of the larger ships as possible targets. For military applications, perception of boat size can be considered one of several factors needed in determining a warship from a commercial vessel. The military pixel requirement for classification is probably a conservative estimate for USCG applications. With size as the primary classification criteria, FLIR operators should be able to “classify” USCG targets for pursuit at greater ranges than in military applications.

After FLIR “identification” of a target, the function and purpose of the ship has been determined. A decision of engagement can be made. The information is gained through examination of ship superstructure, deck equipment, gun mounts, stacks and other features of the warship. In USCG applications, ship purposes and shapes are much more varied and complex. FLIR operators will probably

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Figure 2-7 Pixel Images of a @@@ Motor Yacht (Beam View)

require closer ranges than their military counter-parts to identify USCG targets such that a decision can be made to engage.

Based on the above discussions, pixel count requirements for target determination by a FLIR are given in Table 2-1.

BLANKED

Table 2-1 Minimum Pixel Number for Target Determination

It should be noted, that in many military studies, IR applications were considered in a stand-alone mode. However, the Coast Guard may be able to use radar as a cross-reference to help clarify FLIR information, and improve FLIR performance.

2.5 PROBABILITY OF DETERMINATION

Because of the subjective nature in quantifying human performance in target determination, and the range of shapes which these targets can take on, the pixel numbers required for each category are rough approximations. Additional research into target determination from FLIR displays have produced determination probability curves. These curves give a certainty that a target will be classified or identified from a given pixel number. Figure 2-8 shows the probability of target determination based on percent of nominal pixel number required for determination.

The determination status of a target is dependent on training and experience of the operator. For an equivalent set of conditions with identical targets, identification and classification ranges will differ with different operators. The probability curves express that fact. Naturally, as the target gets closer to the sensor, more information is displayed, and the probability of detection increases. There are ranges outside which detection probability is zero. This is due either to horizon blockage of the radiation path, or dissemination of the radiation due to transmission losses. There are also ranges within which nearly all operators with an assumed skill level can be expected to make target determination if the information is presented. A value of @@@ percent determination probability can be considered nominal for the pixel numbers given in Table 2-1.

3.0 METHODOLOGY

This section reviews the methodology employed to arrive at analysis results. FLIR characteristics were modeled using standard methods applied frequently in IR sensor analyses. As much as possible, parameters specific to the @@@ FLIR and USCG operating environment were selected in determining FLIR capabilities. Performance of the FLIR was primarily gaged in terms of maximum classification determination distances of applicable targets.

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Figure 2-8 Determination Probability

Three software programs were used to analyze FLIR performance: (1) LOWTRAN atmospheric modeling program, (2) Contrast Irradiance (CONTIRR) sensor detection modeling program, and (3) Thermal Imaging Sensor (TIS) FLIR modeling program. Application of these programs are discussed in this section.

3.1 LOWTRAN PROGRAM

LOWTRAN was used to estimate losses from surface target radiation and spectral radiancy of surrounding atmosphere in the 3 to 5 micron wavelengths. LOWTRAN is a generic atmospheric modeling program which can be used to analyze radiation propagation under many atmospheric conditions (Reference [5] ). The LOWTRAN standard @@@ atmosphere without multiple scattering was selected to model most propagation conditions. The centroid height for a typical @@@ boat was set at @@@ above sea level. Primary output values used from LOWTRAN were spectral emission ratios and foreground / background radiancy given at discrete intervals of wavelength.

3.2 CONTIRR PROGRAM

CONTIRR is a program developed at Naval Surface Warfare Center / F44 designed specifically to analyze LOWTRAN output data with FTD formated target radiation signature data (Reference [6] ). CONTIRR was used to integrate spectral information from LOWTRAN with target spectral radiation distributions to determine contrast irradiance of the target against its background at various distances. The contrast irradiance data was then curve-fit with respect to range to describe radiation propagation.

Target radiation signature distributions in CONTIRR runs and in Figure 2-1 and Figure 2-2 are derived from back body spectral radiation equations (Reference [7] ). Black body radiation gives the maximum possible thermal radiancy emitted from a surface at a given temperature (excluding reflected radiation from other sources). In practice, thermal radiation of sources is usually below black body values. Because FLIR analyses depend on radiation differences, black body calculations should remain good approximations.

3.3 TIS PROGRAM

TIS is the main program which simulates FLIR performance (Reference [4] ). TIS was used to calculate target determination ranges based on FLIR and target characteristics, and level of information for target determination. With temperature difference and atmospheric extinction coefficients to simulate target radiation, MRT curve data to simulate FLIR performance, and target area and pixel number to simulate image information, determination ranges were estimated for each set of conditions. Areas of typical targets were measured from applicable photographs (Reference [8] ).

In the TIS Program, MRT data was calculated from equations as a function of spatial frequency. Measured data was curve-fit to the following equation to determine coefficient values:

MRT = SL + @@@ ·SC·FR·exp( @@@ ·FR2·ER2 )

where

ER - equivalent resolution (milliradians)SC - sensitivity constant (Kelvin / milliradians)SL - sensitivity limit (Kelvin)FR - spatial frequency (cycles / milliradians)MRT - minimum resolution temperature (Kelvin)

Based on measurements taken in Reference [9] , least-squares analyses yielded the following values for curve-fit coefficients:

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ER = @@@ milliradiansSC = @@@ Kelvin / milliradiansSL = @@@ Kelvin

Figure 2-5 indicates MRT data measurements taken from the @@@ FLIR and resulting curve-fit values.

To analyze FLIR performance based on field-of-view, the MRT curve-fit coefficients were modified with respect to field-of-view angles. Changes in the values of SL (sensitivity level) and ER (equivalent resolution) were set directly proportional to the angular change in field-of-view. From Reference [9] , MRT data was measured using a lens with a @@@ millimeter (mm) focal length. The field-of-view of the FLIR with this lens configuration was @@@ degrees. In the calculations, it was assumed that the f- number remained constant.

4.0 ANALYSIS RESULTS

This section presents the results of the FLIR performance analysis with respect to U.S. Coast Guard applications. Initially, standard operating conditions were defined for the sensor. Performance ranges were calculated for these conditions. Then, parameters were selected which were anticipated to vary in application of the FLIR. These parameters were changed in the analysis to determine ranges over which the FLIR could be expected to operate. This section will examine impact of the parameters on FLIR performance.

The nominal state modeled for FLIR performance is described as follows:

1) Sensor height @@@ feet above the water line2) @@@ FLIR performance parameters3) @@@ degree Fahrenheit temperature difference between target and background (@@@

environment)4) @@@ micron waveband5) @@@ latitude6) Clear sky (@@@ nautical mile visibility)7) @@@ motor yacht (beam view)8) Classification level of information for target determination9) @@@ degree horizontal field-of-view (@@@ mm focal length lens)

The following parameters were modified to determine their effects on sensor performance.

1) Atmospheric conditions2) Target size3) Sensor field-of-view4) Information level required for target determination5) Target type and orientation6) Geographic location and season

Atmospheric conditions and target size have the greatest influence on FLIR performance. Depending on visibility and target size, determination ranges can be extremely large or near zero. To a lesser extent, sensor field-of-view and required target information level impacts target determination range. The least influential of the parameters are target type, orientation, and geographic location.

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4.1 ATMOSPHERIC CONDITIONS

Atmospheric conditions produce the single most dramatic changes on FLIR performance in an operational environment. Effects of atmospheric parameters on target radiation were discussed in Section 2.2 . Figure 4-1 shows the influence of atmospheric effects on classification ranges of the @@@ motor yacht (beam view) in a @@@ environment. “Clear” denotes @@@ nautical mile visibility in a @@@ atmosphere. “Haze”, “Mist”, and “Rain” show increasingly greater amounts of air-born particles and humidity. Consequently, classification ranges drop to near zero. The number of clear days occurring in an area in which FLIR applications is anticipated should be an important consideration.

It should also be noted that operator skill becomes less of a factor as visibility decreases. The classification curves become more vertical at lower ranges. With decreased visibility, radiation intensities increase more rapidly with smaller distance changes. Therefore, as a target approaches, its pixel count will increase significantly over a small change in range.

In developing these curves, a @@@ degree Fahrenheit target-background difference was used. It is unknown whether rain would reduce this delta over standard conditions thereby decreasing FLIR performance even further.

4.2 TARGET SIZE

Along with atmospheric visibility, target size greatly affect FLIR performance. The amount of IR radiation a target emits is directly related to its surface area. With respect to FLIR performance, the target cross-sectional area apparent to the FLIR determines the amount or radiation received. Sizes of possible targets can vary dramatically from bodies of land to buoys. Target determination ranges will vary just as dramatically. Figure 4-2 show variation in classification ranges of several sea-going vessels through a clear sky in a @@@ environment. Data for the @@@ power boat was taken from a @@@ . The @@@ boat used is the @@@ motor yacht. The Coast Guard cutter considered is the @@@ . The naval carrier considered is the @@@ carrier.

For large ships, significant increases in ship size do not drastically increase classification ranges. The carrier is over twice as large as the cutter; however, the classification range increases only @@@ percent. For small ships, significant decreases in size do affect classification range. If the boat size is halved, the classification range is approximately decreased the same amount. A @@@ boat will have much different classification range from a @@@ boat relatively speaking. Operator skill becomes more of a factor as ship size decreases. The classification curves become more slanted at smaller boat sizes.

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Figure 4-2 Effect of Target Size on FLIR Performance

Figure 4-1 Effect of Atmospheric Conditions on FLIR Performance

Target height is significant since the apparent height decreases with increasing range due to the curvature of the earth. For a sensor at @@@ feet above the water line, horizon blockage will start to reduce apparent target height at around @@@ nautical miles. This range does not take into account refraction effects.

4.3 SENSOR FIELD-OF-VIEW

Sensor field-of-view (FOV) indicates the angle of vision scanned to construct an image. For a FLIR, an increased FOV increases the amount of an image area displayed on the screen. Areas can be searched more efficiently using a sensor with a larger field-of-view. Large FOVs also increase sensor track capability in that the sensor can maintain contact with a target with more error in movement from the operator. However, an increased FOV decreases the pixel number for a given image, and degrades FLIR performance.

Figure 4-3 shows variations in FLIR performance due to changes in FOV. A decrease of @@@ degrees in FOV, yields only a small increase in classification range. Further decreases in FOV will yield less performance increases. Increasing FOV to increase search efficiency brings FLIR performance below specification criteria. The manufacturer can supply a @@@ focal length lens with the FLIR. However, in this configuration, FLIR performance is unacceptable.

Once FLIR performance criteria has been specified, sensor FOV can be optimized for the application. The specification that a boat be classified at @@@ nm optimizes FOV at @@@ degrees given a @@@ percent chance of classification. FOVs below this value give extra performance with classification ranges greater than @@@ nm. FOVs greater than @@@ degrees decrease classification ranges but increase search and track capabilities.

4.4 DETERMINATION PROBABILITY

Figure 4-4 shows the four levels of determination of the representative @@@ motor yacht as its range decreases. Images of the boat with different pixel counts are shown in Figure 2-7. Information levels of determination and their probabilities of occurrence are based on military studies which were reviewed in Section 2.4 and Section 2.5 . The atmosphere model used is a clear sky in a @@@ environment. Initial detection of the target is between @@@ and @@@ nautical miles. Orientation determination of the target can be made between @@@ and @@@ nautical miles. Classification can occur over a wide range from @@@ to @@@ nautical miles. The slanted slopes of the classification and identification curves indicate that increased FLIR operator skills can produce large increases in classification and identification ranges whereas orientation and detection ranges would change little.

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Figure 4-3 Effect of Field-Of-View on FLIR Performance

Figure 4-4 Effect of Determination Criteria on FLIR Performance

4.5 BOAT TYPE AND ORIENTATION

For a given boat length held constant, boat type and orientation affect, to a limited extent, the classification range of the target. Boat type and orientation vary the amount of target area radiating IR waves to the sensor, and, consequently, the target determination ranges. Figure 4-5 shows different types of @@@ boats at different orientations and the effects on classification range through a clear sky in a @@@ environment. Boats with lower profiles are more difficult to classify. Boats viewed from the bow or stern will also be classified at less ranges than boats viewed from the side (beam).

For this comparison, the @@@ motor yacht was compared with a @@@ , a speed boat which has roughly @@@ the profile height. Each boat was analyzed from both the bow and the beam. The motor yacht, as viewed from the bow, has @@@ percent less area than when viewed from the beam. However, the reduction in area causes a @@@ percent decrease classification range. The decrease in range due to orientation is significant. However, in most situations, boats will be viewed from the beam or close to it. Bow classification ranges represent the worst-case conditions which may exist.

A @@@ speed boat, when viewed from the beam, has a @@@ percent smaller profile than a @@@ speed boat when viewed from the same angle. The reduction in area causes a @@@ percent decrease in classification range. If the motor yacht and speed boat can be considered extremes in @@@ boat profiles, then small variations in classification ranges due to boat type can be expected. Sail boats were not considered in this report. If their sails can be considered invisible to the FLIR, @@@ sail boats have lower profiles, and less IR radiating areas.

4.6 GEOGRAPHIC LOCATION AND SEASON

Atmospheric conditions change with latitude and season due to temperature, wind currents, pollen accumulations and other factors as discussed in Section 2.2 . These changes were analyzed to determine how FLIR performance was affected. Figure 4-6 shows classification ranges calculated at @@@ , @@@ , @@@ , and @@@ conditions. The target was a @@@ motor yacht in a @@@ clear sky of a @@@ environment. The graph indicates that geographic location and season have little effect on FLIR performance. The data changes little across the situations considered. Provided clear days are available in these locations, the FLIR should have equivalent performance in a majority of latitudes.

5.0 CONCLUSION

Based on a @@@ percent chance of classification by the operator, the target is estimated to be correctly

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Figure 4-5 Effect of Boat Type & Orientation on FLIR Performance

Figure 4-6 Effect of Geographic Location on FLIR Performance

classified at @@@ nautical miles. This range indicates that the @@@ FLIR performance is acceptable for the conditions specified in Section 1.0 provided the FLIR configuration and operating characteristics are similar to that tested in Reference [9] .

From Reference [9] , MRT performance values for the @@@ Thermal Imager are provided in Table 5-1.

BLANKED

Table 5-1 @@@ Thermal Imager MRT

The @@@ FLIR field-of-view angle is stated at @@@ degrees for horizontal and @@@ degrees for vertical field-of-view directions. Noise equivalent temperature difference (NETD) of the FLIR is measured in a range from @@@ to @@@ degrees Celsius.

In summarizing analysis results, greatest degradation in FLIR performance is due to atmospheric conditions and target size. Sensor fields-of-view and information levels of determination moderately affect determination ranges. Target type and orientation along with geographic location affect FLIR performance the least of the factors considered.

Special considerations in USCG usage of the FLIR with respect to military usage are: (1) military information levels of determination for “classification” and “identification” are applicable with restrictions, (2) surface search radar cross-referenced with FLIR thermal imagery may improve FLIR performance.

6.0 REFERENCES[1] Goody, R.M., Atmospheric Radiation, First Edition, Oxford University

Press.

[2] Moser Paul M., “Mathematical Model of FLIR Performance”, Technical Memorandum NADC-20203:PMM, 19 October 1972.

[3] Hepfer, Kenneth C., “Sensor Image Quality and Visual Task Performance”, NSWC, Dahlgren Division Internal Report, Dahlgren, Virginia,, 4 January 1989.

[4] Hepfer, Kenneth C., “Simple TIS / TVS Range Model”, NSWC, Dahlgren Division Internal Report, Dahlgren, Virginia, 17 January 1989.

[5] Kneizys, F.X., et al, “User's Guide to LOWTRAN 7”, U.S. Air Force Technical Report AFGL-TR-88-0177, 16 August 1988.

[6] Rudzinsky, M.R., “Directions for Calculating IRST Sensor Performance Using the LOWTRAN 7 and CONTIRR Models”, NSWC, Dahlgren Division Internal Report, Dahlgren, Virginia, 10 September 1990.

[7] Pathria, R.K., Statistical Mechanics, First Edition, Pergamon Press.

[8] McKnew, Ed and Mark Parker, Powerboat Guide, Second Edition, American Marine Publishing, Inc.

[9] “Laboratory and Field Evaluation of @@@ Thermal Imager for @@@ Applications”, DCS Corporation, August 1991.

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