This document presents a new technique for evaluating infrared flares used as countermeasures against infrared-guided missiles. It involves modeling the seeker of an IR missile using video from an infrared pod and software algorithms. The software tracks the centroid of intensity within the simulated field of view of the missile seeker. It can detect when a flare is dispensed based on a rapid rise in intensity. The technique allows quantification of the effectiveness of flares by measuring errors introduced into the missile's tracking loop. Compared to prior subjective testing, this provides an objective method to evaluate IR countermeasure performance.

1. A NEW TEST TECHNIQUE FOR FLIGHT EVALUATION
OF INFRA-RED FLARES
Gp Capt NK Nair, Officer Commanding Test Engineering Squadron
Aircraft and Systems Testing Establishment, Yemlur PO Bangalore-560037
INTRODUCTION
1. The Counter Measure Dispensation System (CMDS) offers the last line of defence against
incoming missiles. Over the years, the missile technology has made rapid progress. Earlier generation
of missile could only be launched in a tail aspect and had poor lock on range. The Missiles now have all
aspect engagement, image and pattern recognition.
2. All the modern fighters are now fitted with Counter Measure Dispensation System (CMDS).
The CMDS is capable of dispensing Chaffs and Flares. Flares are used as a counter measure against
Infra-Red (IR) homing missiles. The CMDS is programmable and provides various programming
options for dispensation. Modern CMDS allows programming of different dispensation rates and have
the ability to be cued by other EW sensor like Radar Warning Receiver (RWR), Self Protection Jammer
(SPJ) and Missile Approach Warning Systems (MAWS).
3. Over the years, new CMDS which have better performance specifications. Thus, training and
testing on usage of flares is one area which requires an out-of-the-box thought process and
implementation.
4. The flare dispensation rate is the rate at which flares are dispensed by the CMDS. There are two
circumstances under which the flares could be dispensed; these are reactive and preemptive
dispensations. The dispensation rate is based on the IR missile threat parameters, aircraft flight and
radiant intensity parameters and flare parameters. In order to assess the flare dispensation and provide
realistic training a modeling and simulation approach with Hardware-In-Loop (HIL) was tried out.
AIM
5. The aim of this paper is to present a new test technique for flight evaluation of IR flares.

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BACKGROUND
6. Evolution of IR Missiles. The IR missiles over a period of time have undergone a series
of development. As per open literature, the generation of airborne IR missiles can be classified into five
generation while ground based IR missiles are classified into four generations. These are briefly
discussed in the succeeding paragraphs.
(a) First and Second Generation. These missiles had seekers with detectors operating in
Band I (2-3 µm) and did not have any cooling system. The seekers were single cell detector
mainly using Zinc Sulfide or Germanium. Due to the limited number of detector, the I Gen
missiles had a narrow Field of View (FOV) (30 degrees) and this expanded to 45 degrees in the
II Gen of missiles. Further, as there was no active cooling, the sensitivity of the detector was
low resulting in low lock on ranges. I and II Gen missiles were capable of locking on only in
the tail aspect. Magic I, Early Sidewinders (AIM-9B), R-13 (Vympel K-13) etc are examples of
I Gen missile. AIM-9D/G and H is an example of II Gen missiles.
(b) Third Generation. The Missile in this Generation had all aspect capabilities and
detectors operating in Band II (3 to 5 µm). The detectors (Indium Antimonide) were cooled
with Nitrogen. The detectors were arranged as a series which scanned the FOV. Magic II,
Sidewinder 9L/M are examples of Air to Air IR missiles while SA13 and SA16 are examples of
IR SAM.
(c) Fourth Generation. The missiles incorporated advanced seeker technologies like Focal
Plane Array (FPA) and incorporation of effective ECCM. Missiles in this generation have
ECCM like dual band tracking, velocity rejection, rise time rejection etc. The performances of
these missiles were also better in terms of higher thrust and turn rates. The missiles had larger
FOV and off bore sight capabilities. Mistral and Stinger are examples of IR SAM in this
generation.
(d) Fifth Generation. These missiles have IR imaging seekers. The missile head has an
active array of elements through which the IR image of the scene is captured and tracked. The
missile uses frequency and spatial discrimination to track the target. Flare would not be
effective against this generation of missiles. The usage of Directed IR Counter Measure
(DIRCM) would be the only effective counter measure against this generation of missiles.
AIM-9X, Python-V, IRIS-T, MICA are all examples of this generation of missiles.

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7. Evolution of IR Counter Measures. With the evolution of missiles, the counter measures
against IR missiles have also undergone rapid changes. The initial counter measures were Flares which
were made of Magnesium Teflon Viton (MTV). The MTV flares produced IR signatures which were
higher than the aircraft IR signature, thus were effective against the earlier generations of missiles. With
the evolution of missiles, counter measures were built which were specific to the ECCM in the missiles.
For example against missiles with dual band seekers, spectral flares which produced IR signatures in
both the band were an effective counter measure. For missiles with velocity rejection algorithms,
aerodynamic flares were used. The fifth generation missiles had Imaging seekers, hence conventional
flares would not be effective against this generation of missiles.
8. Airborne testing of IR counter measures is done with training missiles. The test methodology
was to ensure that the missile had a lock on the ac and thereafter flares were dispensed and the effect on
the missile head was observed. The missile lock indication would continue even after the flare burned
out if the lock was retained on the ac. However, if the missile had shifted the lock to the flares, then
there would be a break lock when the flare burnt out. This was also visually correlated by movement of
the missile head. However, this test technique was subjective and depended upon the quality of the
training missile. Additionally, the characteristics of the flare could not be quantified. Thus, there was an
urgent need to look for alternate method for testing of IR counter measures.
MODELING AND SIMULATION OF IR MISSILE USING IR IMAGING SENSOR
9. The data acquisition was done using the IR Pod . The Video of the pod was recorded in the
Video Tape Recorder (VTR) carried onboard the ac. The video of interest was captured using a video
editing software and the video file was converted into a series of bitmap (bmp) images. The avi format
produced video at the rate of twenty five frames per seconds. The bmp images were used for image
processing.
10. Software Algorithm and Processes. The software was developed using MATLAB, a software
for technical computing developed by ‘The Mathwork Inc’, USA. The algorithm is summarized in the
following steps:-
(a) Step 1. Detect maximum intensity of image file

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(b) Step 2. Ask user to define threshold value as percentage and type of processing (Binary
or Intensity tracking), Field of View (FOV) of Sensor and IR missile to be simulated.
(c) Step 3. For the first frame compute the centroid.
(i) Binary Tracking. Check intensity level of each pixel if above threshold
set to 1 else set to 0.
(ii) Intensity Tracking. Check pixel intensity for each pixel, if above threshold no
action else set to 0.
(d) Step 4. Compute the centroid of the image using binary or intensity logic.
(e) Step 5. Load next frame. Recall the position of centroid of the previous frame,
Compute the FOV of the missile. With the centroid as the centre and boundary imposed by the
FOV of the missile, reset intensity of all pixels outside FOV to 0. Process only information
inside the FOV of the missile. Compute the new centroid.
(f) Step 6. Repeat Step 5 till End of File (EOF).
11. The software processes the input bmp/jpg files and produces an avi file. In order to illustrate the
functioning of the software, a few test cases are illustrated in the succeeding paragraphs with still
images of the output. Several instances have been analyzed as cases and are described below:-
(a) Case I. The raw image as seen by pod is placed as Fig 1 below. The entire silhouette of the
target aircraft can be seen.
Fig 1 : Frame capture of raw image

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The superimposed data is treated as noise and needs to be eliminated. The algorithm caters for
the difference in the FOV of the sensor and the missile. In the first frame, the centroid of the
entire image is found out, due to the higher intensity of the aircraft, the centroid would be
centered on the aircraft. Incase the centroid is not on the aircraft, the starting frame would need
to be reselected. After the centroid of the first frame is found out, the position of the centroid in
X and Y Coordinates are transferred to the next image. As the capture rate of the raw data is at
30 frames per second, the aircraft position in the next frame would not be very far from the
previous frame, thus deemed not to make a significant difference in the simulation.
After the centroid from the previous frame is obtained an equivalent missile FOV is
superimposed on the centroid. All the data outside this FOV is eliminated. The FOV is shown
as a red box (2 degree FOV in Search mode). The processed data is placed as Fig 2 below.
Fig 2 : Processed data with missile FOV superimposed
The data inside the box is then processed using the intensity centroid or the binary centroid
method and the new position of centroid is calculated. The image is then drawn. It can be seen
that only the aircraft is seen in the centroid and only parts of the aircraft which are hotter than
the rest of the aircraft is seen. All superimposed data is also eliminated. The ‘*’ marked on the
aircraft is the position of the centroid. The position of the centroid along with the FOV is then
superimposed on the raw data. The resulting image is placed below as Fig 3.

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Fig 3 : Raw data with Centroid and missile FOV superimposed
In order to facilitate easier dissimilation of data, the gray image is converted to a color image using the
ranges from blue to red as extremes and passing through the colors cyan, yellow, and orange. The
resulting image is placed below as Fig 4:-
Fig 4 : Raw data with true colour representation

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(b) Case II . The next instance for analysis chosen was when a flare is fired by the target
aircraft. The series of images produced are placed below as Fig 5. The flare bloom is seen to
mask the aircraft. The centroid is still tracking the combined bloom due to the aircraft and flare.
The aircraft is seen to be in the centroid and only parts of the aircraft which are hotter than the
rest of the aircraft is seen.
Fig 5 (a) Raw Image Fig 5 (b) Processed Image
Fig 5 (c) FOV imposed on Raw Image Fig 5 (d) Color image
Advanced Features
12. Electronic Counter Counter Measures (ECCM). As the imaging device used is akin to the
Imaging Infra Red (IIR) sensor, various known ECCM features could be incorporated into the model.
As per open literature, Intensity Rise Time (IRT) is one feature commonly available in most of the

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missiles upto IV Gen. The ac IR intensity varies inversely proportional to the square of the distance.
Thus, the intensity changes caused by the ac would change very gradually. However, the appearance of
the flare in the FOV of the missile results in a sharp rise in the intensity and can be used to detect
presence of a flare. In order to reduce the short limit of the missile, flares are chemically composed to
reach the peak intensity within a very short time. This results in a rapid change in the intensity as seen
by the missile. The rapid rise in intensity is used to detect and flag the ECCM circuits. Another feature
which is used to discriminate the flare from the ac is the rapid deceleration of the flare compared to the
ac. When the flare is ejected out, it has typically an ejection velocity of 30-50 mtr/sec, however this is
used only to clear the flare from the vicinity of the ac. Once in free stream, the flare decelerates and
drops under the gravitational pull. This is used to discriminate the flare and the ac, a Gated Video
Tracker was developed which would take FOV of the missile to the leading edge of the image in the
direction of the motion. This results in the missile locking on to the leading point in the image which
would be the ac as the flare would separate out due to the deceleration. The software was embedded
into the existing software as described in the preceding paragraphs and produced similar images.. The
algorithm is summarized in the following steps:-
(a) Step 1. Compute the average intensity inside the missile FOV in all preceding frames
and estimate the direction of motion of the ac.
(b) Step 2. Check the current intensity level in the missile FOV. If the rate of change of the
current intensity is more than the average intensity then flag the ECCM.
(c) Step 3. If the ECCM flag is high, position centroid on the leading edge of the image.
Change the shape of the centroid for easier assimilation. If flag is low then continue with
computation of the centroid as previously described.
(d) Step 4. Hold ECCM high flag for 5 seconds, based on average burn out time.
(e) Step 5. Load next frame. Recall the position of centroid of the previous frame,
Compute the FOV of the missile. With the centroid as the centre and boundary imposed by the
FOV of the missile, reset intensity of all pixels outside FOV to 0. Process only information
inside the FOV of the missile. Compute the new centroid.
(f) Step 6. Repeat Step 5 till End of File (EOF).

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13. The test case with the ECCM implemented is described in the succeeding paragraphs:-
(a) The flare has masked the ac. The ECCM flag has been set due to the rise in the IRC and
the centre of the FOV position marked with the ‘*’ mark is on the leading edge of the image.
Fig 6 : ECCM Flag ‘on’ with true colour representation
14. The data produced by this set of simulation is summarized below in the Fig 7. The Figure
consists of three subplots. Sub plot 1 on the top is the representation on the centre of the FOV in the
complete simulation. The X and Y axis represent the X and Y coordinates. The motion of the FOV
centre can be seen to start from the lower right and exit from the top. The Subplot 2 in the middle
indicates the normalized intensity in each frame within the missile FOV. The X axis indicates the
frame no while the Y axis indicates the normalized intensity in the FOV. The Subplot 3 indicates when
the algorithm has set the ECCM flag high (1) based on the Intensity Ratio Change (IRC).

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0 100 200 300 400 500 600
0
200
400
0 50 100 150 200 250
0
5
10
x 10
5
0 50 100 150 200 250
0
0.5
1
Fig 7 : Subplots of data analyzed during simulation
15. Miss Distance. The errors introduced into the tracking loop is one of the key elements is
evaluating the effectiveness of the counter measure. Using this algorithm, the errors in the missile
tracking is quantified. The algorithm is summarized in the following steps:-
(a) Step 1. In the selected series of frames, in which the error is to be estimated, the
position of the ac and extreme position on the wing are marked manually. A software was
written using which the user has to click on the image to store the location into a file. The wing
span of the ac was used to get the pixel equivalent length for each frame.
(b) Step 2. When the raw images are processed, the centre of the FOV for each frame is
computed and stored.
(c) Step 3. For each frame, the error in the position of the ac and the FOV is computed.
This gives the error in pixels. The pixel lengths are then converted to meters by computing the
pixel equivalent length as estimated in Step 1.

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16. A set of 45 image frames were used to estimate the error in the tracking. The ECCM feature
was off and the dispensation of a single flare was able to shift the lock from the ac. As in some of the
images, the ac was masked by the flares, the estimation of the wing span was prone to errors. Thus,
based on estimates, a fit was generated for estimating the wing span in each frame. The plot of the same
is placed below as Fig 8. It shows the frame no in the X axis and has the pixel length of the wing span
on the Y axis. The observed reading and the estimated reading are shown in two colours as shown in the
Fig. The pixel length of the ac was seen to vary from 60 to 90 pixels. With a constant wing span, the
pixel length to equivalent meters was estimated for each frame.
Fig 8 : Estimation of Wing Span in pixels
17. Based on the location of the ac and the centre of the FOV, the relative motion of the two was
studied and is placed as Fig 9. It can be seen that though the ac remains in the centre of the sensor FOV,
the missile FOV has followed the flare trajectory.

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Fig 9 : Relative motion of ac and centre of FOV
18. Based on the equivalent pixel length obtained for each frame and the errors due to the
difference in ac and centre of the FOV, the errors introduced was plotted and is placed as Fig 10. The X
axis indicates the time in seconds while Y axis is RMS errors in meters. The blue vertical at 0.5 sec
indicates the instance of flare dispensation. The errors introduced in the tracking algorithm are indicated
in red.
Fig 10 : RMS Errors introduced due to flare dispensation

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SCOPE FOR FUTURE WORK
19. The work undertaken was based mostly on open literature, the software application could be
further modeled to increase the fidelity of the simulation and suite the user requirements, the areas of
work is described in the following paragraphs:-
(a) Ground and Air Based Thermal Cameras. A wide FOV thermal camera mounted on a
Target Tracking Radar (TTR) would provide track information along with the thermal image.
The missile simulation could be implemented online on the data obtained from the IR camera.
The possibility of using a high fidelity IR camera on a pod for airborne analysis could also be
explored. Effect of flare dispensation and maneuvers carried out by the aircraft would be
quantified in terms of missile miss distances. ECCM of advanced third and fourth generation
missiles would also be implemented in the tracking logic.
(b) Missile Hardware in Loop Simulation. The missile head could be mounted on a
slewable turn table and the control inputs to the control surface could be used to facilitate
tracking using the turn table.
UTILITY OF SOFTWARE IN THE PRESENT FORM
20. In the present form the software could be used in the following areas:-
(a) Comparative Evaluation of flares. The software can be used for comparative evaluation
of different types of flares. The software would provide a qualitative method to compare two
different flares along with a baseline comparison on the radiant intensity with respect to the
aircraft.
(b) Comparison of flare trajectory. Integration on CMDS on newer platform involves
studies for ideal location of the dispenser. The software could be used to study the trajectory of
flares on platforms which are already fitted with CMDS for a comparative analysis on the
location of dispensers.

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CONCLUSION
21. The flares provide self protection to aircraft against IR guided weapons. The CMDS over the
years have improved performance. Different types of flares are being tested on ac. Testing and
operational training on usage of flares is currently undertaken using systems which have degraded
performance and do not provide qualitative data. The software application developed uses the IR image
captured from a IR pod and computes the movement of centroid on a superimposed missile FOV. The
application tracks the centroid and outputs the images as an avi file which can be viewed in any
windows media player. The software also incorporates ECCM features and provides a tool to quantify
the errors introduced due to flare dispensation. This new technique would enhance flight testing by
providing quantifiable data on airborne performance of flares and could also be used to fine tune the
operational training.