Implementation of intensity ratio ch ange and line-of...

Implementation of intensity ratio change and line-of-sight rate change algorithms for imaging infrared trackers

C.R. Viau,

Tactical Technologies Inc., Ottawa, Canada

ABSTRACT

The use of the intensity change and line-of-sight (LOS) change concepts have previously been documented in the open-literature as techniques used by non-imaging infrared (IR) seekers to reject expendable IR countermeasures (IRCM). The purpose of this project was to implement IR counter-countermeasure (IRCCM) algorithms based on target intensity and kinematic behavior for a generic imaging IR (IIR) seeker model with the underlying goal of obtaining a better understanding of how expendable IRCM can be used to defeat the latest generation of seekers.

The report describes the Intensity Ratio Change (IRC) and LOS Rate Change (LRC) discrimination techniques. The algorithms and the seeker model are implemented in a physics-based simulation product called Tactical Engagement Simulation Software (TESS™). TESS is developed in the MATLAB®/Simulink® environment and is a suite of RF/IR missile software simulators used to evaluate and analyze the effectiveness of countermeasures against various classes of guided threats.

The investigation evaluates the algorithm and tests their robustness by presenting the results of batch simulation runs of surface-to-air (SAM) and air-to-air (AAM) IIR missiles engaging a non-maneuvering target platform equipped with expendable IRCM as self-protection. The report discusses how varying critical parameters such track memory time, ratio thresholds and hold time can influence the outcome of an engagement.

Keywords: Countermeasures, IRCM, flares, imaging IR seeker, IRCCM, modeling, simulation, intensity ratio, LOS rate change

1. BACKGROUND Military and civilian aircraft operating in conflict areas continuously face the threat of IR-guided missiles. The latest generation of IR seekers uses imaging technologies to produce a complete picture of the IR scene and provides significant capability enhancements over previous generations. Although IIR systems are more commonly found in air-to-air and anti-ship missile systems, they could potentially find their way into Man Portable Air Defense Systems (MANPADS) and result in an even greater threat to low flying aircraft.

There are typically two approaches to airborne platform protection from IR-guided missiles. The first approach is to use expendable IRCM such as flares and towed decoys to prevent the missile seeker from locking on and lure the missile away from the target platform. The second approach is to use on-board jamming such as Directed Infrared Countermeasure (DIRCM) systems to introduce error signals into the missile seeker’s guidance system or physically damage the seeker head. While each has its advantages and disadvantages, the majority of research and development efforts in this area in the last decade have focused on laser-based DIRCM technologies. There are several major ongoing defense programs1 aimed at developing and fielding more affordable equipment to better protect the various types (fighter, helicopters and large transport) of aircraft. DIRCM systems have demonstrated their effectiveness with helicopter and transport aircraft, however flares continue to be the only IRCM available for fighter aircraft2. According to the same source, even the latest fighter generation will only depend on flares to protect them from the various generations of IR-guided threat.

Flares have long been the primary IRCM to protect aircraft and have kept pace with the evolving threat. Several open-literature sources3,4,5,6 have discussed the development and advancements in pyrotechnic and optical countermeasures. However, it remains unclear how the expendable IRCM, in its current form, can effectively protect airborne platforms from the latest generation of IR-guided missiles. Open-literature sources2,3 suggest that IIR technologies may have rendered all type of point target flares unsuitable for airborne platform protection. One study7 proposed the use of

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, edited by Gerald C. Holst, Keith A. Krapels, Proc. of SPIE Vol. 8355, 83550P · © 2012 SPIE · CCC code: 0277-786X/12/$18 · doi: 10.1117/12.918482

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distributed flares as mean to protect fast jets and demonstrated using software simulations that distributed flares could be effective as a last line of defense against IIR missiles. The Electronic Warfare (EW) and specifically IRCM community must continuously evolve their understanding of the threat in order to exploit its weaknesses. One effective way to gain this knowledge and insight is to use high-fidelity, physics-based simulation tools and hardware-in-the-loop simulators to analyze the non-linear and stochastic interactions between various systems and their environment.

The typical challenge for any form of tracker is to identify and extract targets with specific temporal, spectral and/or physical characteristics from a cluttered background and maintain track until end game. When a target anticipates or suspects it is being tracked, it may attempt to break-lock from the tracker by possibly performing evasive maneuvers and/or introducing false targets in its surrounding space to confuse and hopefully seduce the tracker. The tracker’s ability to resist and reject the target’s attempt to break-lock is referred to as counter-countermeasures (CCM) response.

Several CCM algorithms used by non-imaging and pseudo-imaging seekers have been described in the open-literature. Montgomery et al8 documented a multicolor IR signature measurement experiment of SAMs and AAMs. Oh et al9,10 proposed two IRCM rejection methods based on (i) two-color signature cancelation algorithm for a rosette tracker and (ii) a two-color signature ratio comparison for a crossed-array tracker. Jahng et al11,12 proposed a CCM concept based on the K-means algorithms and an iterative self-organizing data analysis algorithm for rosette trackers. Forrai et al13 described a simulation environment used to assess IRCM effectiveness. They discussed three methods used to reject IRCM in non-imaging seekers: (i) two-color discrimination, (ii) intensity rise time discrimination and (iii) LOS rate discrimination.

With the advancements of microprocessors, more sophisticated and complex algorithms based on pattern recognition, neural networks and artificial intelligence were developed and implemented in IIR seekers to extract target and background features. A wealth of information on proposed image processing14, target tracking15, filtering16 and data fusion17 algorithms with potential applications to IIR systems is available in the published literature. One can only assume that some forms of these algorithms are actually used in fielded systems.

The purpose of this project was to implement IRCCM algorithms based on target intensity and kinematic behavior for a generic IIR seeker model. The seeker model and the algorithms had to be developed in the MATLAB environment and had to be customizable using tunable parameters. The algorithms had to be open, easily verifiable and based on published literature. The design was implemented in TESS to support Electronic Warfare Operational Support (EWOS), development of autonomous national countermeasures expertise, development of effective self-protection tactics and countermeasure programs for improved platform survivability, test and evaluation of countermeasures in hardware-in-the-loop systems and training of operators in countermeasures (electronic attack) and counter-countermeasures (electronic protection) tactics.

The report discusses the Intensity Ratio Change (IRC) and Line-of-sight Rate Change (LRC) algorithms and evaluates their robustness through batch run simulations. The IRC and LRC algorithms can work together or individually and both use a common track database which holds historical data on each object in the sensor’s field of view. The IRC algorithm compares the intensity of individual objects to the historical average of a reference object to identify false targets. The LRC algorithm compares the motion of individual objects with respect to the reference object in order to identify false targets.

2. DESCRIPTION OF THE ALGORITHMS Forrai et al13 presented a generic IRCM assessment model and divided their generic non-imaging seeker into three distinct modules: flare detection, CCM tracking and normal tracking. The first two modules are described in detail in their report and summarized here. The flare detection module employs one or all three of the following discrimination techniques: (i) two-color target signature, (ii) intensity rise time and (iii) LOS rate change. When the CCM tracking module is enabled, the seeker uses one of four ways to respond to the flare detection: (i) rate hold, (ii) angle hold, (iii) rate bias or (iv) angle bias.

The algorithms implemented in the generic IIR seeker and discussed in this report are variants of the intensity rise time and LOS rate change algorithms presented by Forrai et al.

The IRC and LRC are embedded in a MATLAB function block inside a Simulink subsystem. The output of the IR scene generator is converted to a binary pixel map using a threshold function. A Simulink Blob Analysis block operates on this binary pixel array to regroup adjacent pixels into “blobs” or objects. The Blob Analysis block computes the area,



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centroid and bounding box of each detected object. It also provides a label array correlating each pixel to an object. These parameters are used by the Target Discrimination and IRCCM Logic function to perform the target tracking and counter-countermeasure functionality of the generic seeker model. Other CCM input/output parameters of interest are listed in Table 1.

Table 1- IRCCM Logic Input/Output List

Inputs Outputs

Simulation time Centroid (units of pixels) of the tracked object Simulation time-step Total power of the tracked object IRCCM memory time Peak power of the tracked object

Intensity ratio change threshold Intensity ratio of all detected object LOS rate change threshold LOS rate change value of all detected object

Flare hold time Flare detected flag for each of the detected object The top level diagram of the Target Discrimination and IRCCM Logic function is illustrated in Figure 1.

Figure 1- Top Level CCM Logic Diagram

The simulation run starts with the seeker in track mode and assumes that either an operator or target identification algorithm successfully identified the desired target. During the initialization portion of the simulation, the target tracking module sets the initial target as the reference object. At every time-step, the target tracking logic attempts to re-identify the reference object in the sensor's field of view. Once a reference object is identified, the algorithm adds all the other detected objects in the sensor's field of view to the track database. The track database stores the following information for each of the detected objects:

Table 2- Track database

Timestamp Object Size

Horizontal Position

Vertical Position

Horizontal Velocity

Vertical Velocity

Predicted Horizontal

Position

Predicted Vertical Position

Total Power

Peak Power

The Simulink Blob Analysis block outputs all the necessary information to build the track database. An issue that was encountered during the development was object identification numbers (IDs) did not always match from one time-step to the next. The Blob Analysis block determines IDs based on the position of the object in the pixel array. As objects moved around in the field of view (i.e. flare following a ballistic trajectory behind the aircraft), IDs were sometimes reset in the middle of a simulation run resulting in the loss of historical data for that object. It was therefore necessary to implement a track database and additional logic to match objects to existing tracks in order to preserve the historical data.

During this step, detected objects are either matched to existing tracks or added as new tracks. Once this process is complete, any track that was not updated is removed from the database. If one of the CCM methods is enabled, the CCM algorithm looks at the track database to analyze the historical behavior of each track as compared to the reference track. Objects that present flare-like characteristics are identified and removed from the “trackable” list of objects.



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From the remaining list, the objects with the characteristics that best match the user-defined tracking method become the primary target. The following sections describe some of the processes in more detail.

2.1 Identifying the Reference Object

A summary of the process to identify the reference object in the sensor’s field of view is illustrated in Figure 2.

Figure 2 – Reference object logic diagram

At every time-step, the target tracking algorithm must first identify a reference object with which to compare all other objects in the field of view. The algorithm looks at each object’s centroid (in units of pixels) and compares it to the reference object’s predicted position from the previous time-step. If the difference between the predicted and actual position is within five (5) times its size, the object is identified as a possible match. Once all objects have been evaluated, the object with smallest separation distance (closest neighbor) is formally identified as the reference object.

2.2 Updating the Track Database

A summary of the process used to update the track database is illustrated in Figure 3.

Figure 3- Track database update logic diagram

The process update the track database is very similar to finding the reference object. The centroid of the reference object is used as a reference point to compare the predicted and actual positions of each object in the field of view. If the difference between the predicted and actual position is less than one (1) times the object size, the track is identified as a potential match. After comparing the object to all the existing tracks, the algorithm assigns the object to the best match. If no match is found, a new track is added to the database. The process is repeated until all objects in the field of view have been evaluated.

After all the objects have been compared to the existing track database, the algorithm removes tracks that were not updated in the last five (5) time-steps. The lifespan of idle tracks in the database was varied during the development and



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five (5) time-steps seemed to provide the best results. The track database is currently limited to fifty (50) tracks and a longer lifespan means that idle tracks could potentially use valuable memory space. It was also noted that a longer lifespan resulted in matching of unrelated tracks. One observed scenario resulting in unrelated track matching occurred when a series of flares were deployed at an interval in the same orientation. Since the seeker attempts to keep the target platform in the center of the field of view, every deployed flare travelled the same trajectory. As new flares appeared in the field of view, the algorithm matched them to idle tracks (i.e. flares no longer in the field of view) instead of adding them as new tracks.

2.3 Track Rejection Based on the Selected CCM

A summary of the process used to reject tracks based on the selected CCM method is illustrated in Figure 4.

Figure 4- Track rejection logic diagram

During the initialization subroutine, the algorithm initializes three arrays: (1) IRC which is a binary array indicating which object(s) have been identified as flares by the IRC algorithm; (2) LRC which is binary array indicating which object(s) have been identified as flares by the LRC algorithm; and (3) flaredb which is binary array indicating which object(s) have been identified as flares by either the IRC or LRC algorithms. At every time-step the algorithm evaluates every track to identify which ones demonstrate flare-like behavior. It first determines which CCM method is selected and whether or not the user-defined activation delay time is exceeded. If the IRC method is enabled, then once the IRC activation delay has expired the module computes the intensity ratio of the ith track for the current time-step (N) by dividing its detected power (PN(i)) by the reference object’s average power ( )o

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Similarly if the LRC method is enabled, then once the LRC activation delay has expired the module computes the average azimuth ( )θ& and elevation ( )φ& LOS rate change of the ith track for the current time-step (N) using the following equations:

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Once the IRC and LRC values are determined for each track, the algorithm compares these values to the user-defined thresholds. If the values exceed the thresholds, the tracks are identified as potential flares. A track retains its “flare” status for the user-defined Flare Hold Time. The purpose of the Flare Hold Time is to prevent the track status from oscillating.

Finally, the Target Selection module applies the selected target tracking technique to all the objects not labeled as potential flares.

3. EXPERIMENTAL SETUP 3.1 Purpose

The purpose of this study was to implement and evaluate the robustness of the IRC and LRC countermeasure rejection algorithms in a generic IIR seeker and determine the impact of the various tunable parameters on the overall performance. The tunable parameters were:

i. Intensity ratio change (IRC) threshold ii. IRCCM memory time iii. Flare hold time iv. LOS rate change (LRC) thresholds (azimuth, elevation)

3.2 Simulation Tool

The study was conducted using SAAM(IIR), a member of TTI’s Tactical Engagement Simulation Software (TESS™). TESS is an open physics-based suite of tools that enables its users to analyze, evaluate, understand and optimize IR/RF countermeasure effectiveness against various classes of surface-to-air, air-to-air, anti-ship and anti-tank guided threats.

The IRC and LRC algorithms, as well as the simulation tools are implemented in MATLAB, Simulink and various other Mathworks® toolboxes. Target, countermeasure, threat systems and engagement libraries are stored in a front-end database with a programmable Monte Carlo batch running capability.

For the purpose of this study, the background clutter and sensor noise were intentionally suppressed from the IR scene in order to isolate the algorithms from external factors.



3.3 Engagement Geometry

The algorithms were evaluated against a generic fighter deploying expendable IRCM as self-protection. The SAM engagements were in a “look up” geometry while the AAM engagements were in a “look down” geometry. As there is no background clutter included for the purpose of this investigation, the main reason for evaluating the “look up” and “look down” geometry was to understand the impact of the flare’s ballistic trajectory on the algorithms.

As discussed in the Background section, IIR seekers probably use some form of pattern recognition algorithms where features such as size and aspect ratio are used to discriminate true targets from background clutter and decoys. In the initial stages of engagements where target and flares may appear as just a few pixels and features may not be easily extracted, alternative algorithms may be required. The following simulation runs are intended to simulation this specific scenario. The initial threat range was set to three thousand meters. The azimuth angle of arrival was varied between 0° and 360° (with 10° increments) while the elevation was varied between -50° and 50° with respect to the target platform longitudinal axis. The other relevant parameters are tabulated in the following sections.

3.4 Subsystem Parameters

There are over one hundred and twenty (120) parameters and lookup tables characterizing the size, shape, position, motion and IR signature of the IIR seeker, airframe, launch platform, target platform, flares and the environment. Table 3 summarizes the relevant parameters for the study.

Table 3 - Subsystem Parameters

Threat System Target Platform Flares Parameter Value Parameter Value Parameter Value

Servo Bandwidths

5 Hz Platform Type Fast Jet Approx. diameter 0.3 m

Gimbal Limits 60 deg Length 14 m Growth time constant 0.1 sec Field of View 4 deg Wing span 9 m Sustain time 4 sec Detector Array 128x128 Velocity 250 m/s Decay time constant 3.5 sec

Detector Sampling Rate

200 Hz Maneuver Time N/A Number of deployed flares

8

IRCCMs IRC / LRC

Orientation [az el]

4 x [135 -30] 4 x [-135 -30]

Length 1.45 m Timing [0.1 0.2 0.3 0.4] sec Diameter 0.1 m

Wing Span 0.17 m Mass 9.2 kg

Prop. Nav. Coef. 3 Max.

Acceleration 30 g

3.5 Measures of Effectiveness

The simulation computes a point of closest approach (miss distance) for every engagement. Based on user-defined parameters, a probability of kill and survival are also generated. In this study, the primary measure of effectiveness was the miss distance. It was assumed that if the miss distance was less than forty (40) meters at end game, the algorithm had successfully rejected the flares and tracked the target platform. 3.6 Batch Run Simulations

3.6.1 IRC Algorithm Assessment

The IRC batch run (396 runs) was conducted to assess the performance of the IRC algorithm in SAM and AAM engagements using the following set of parameters:



Table 4 - IRC Parameter Set

IRC Threshold 1.5

IRCCM Memory Time 1 sec

Flare Hold Time 0.3 sec

The results of the IRC tests are presented in Section 4.1. From these results, a focus region where the algorithm produced mixed results was selected to evaluate the impact of the tunable parameters listed in Section 3.1. The focus region is between 90° and 180° in azimuth and -30° and -10° in elevation.

3.6.2 LRC Algorithm Assessment

The LRC baseline batch run (396 runs) was conducted to assess the performance of the LRC algorithm in SAM and AAM engagements using the following set of parameters:

Table 5 - LRC Parameter Set

LRC Threshold [Az El] [0.1 0.1] deg/s



The results of the LRC tests are presented in Section 4.2.

3.7 Combined IRC and LRC Algorithm Assessment

A combined IRC and LRC batch run (396 runs) was conducted to evaluate the combined performance of the two CCM techniques against SAM and AAM threats. The parameter set for this batch run is listed in Table 6.

Table 6 – Combined IRC and LRC Parameter Set

IRC Threshold 1.1

LRC Threshold [Az El] [0.1 0.1] deg/s



The results of the IRC/LRC tests are presented in Section 4.3.

3.8 Tunable Parameters Assessment

Four (4) additional batch runs (400 runs each) were conducted in the focus region where at each of the forty (40) different angles of arrival, ten (10) different IRC Threshold, LRC Threshold, IRCCM Memory Time and Flare Hold Time values were used to assess the performance of the algorithm. The parameter set for these batch runs are list

Table 7 - Tunable Parameter List

Batch Run #1 Batch Run #2 Batch Run #3 Batch Run #4

Parameter IRC Threshold LRC Threshold IRCCM Memory Time Flare Hold Time

Parameter Sweep 0.1 < IRC < 2.1 0.05< LRC < 0.45 0.1 < MT < 2.1 0.1 < FHT < 2.1

The results of Tunable Parameter tests are presented in Section 4.4.



4. RESULTS AND DISCUSSION 4.1 IRC Algorithm Results

The results of the IRC batch runs are presented in Figure 5. The diamonds represent the threat initial launch position with respect to the target platform. The blue diamonds indicate that the IRC algorithm successfully rejected the flares while the red hollow diamonds indicate the algorithm was unable to reject the flares. A total of 396 runs were conducted of which 377 (95%) were successful and 19 (5%) were not. Section 4.5 discusses further some of individual engagement scenarios describes why the algorithms was unsuccessful on those areas.

Figure 5 - IRC results for SAM/AAM engagements

4.2 LRC Algorithm Results

The results of the LRC batch runs are presented in Figure 6. A total of 396 runs were conducted of which 329 (83%) were successful and 67 (17%) were not.

Figure 6 - LRC results for SAM/AAM engagements

The results in Figure 6 demonstrate that the LRC algorithm appears to be effective at nearly all angles of arrival for the exception of head-on SAM engagements (-20° to 20°) and tail-chase AAM engagements (150° to 200°). In those two regions, the engagement geometry resulted in the initial flare deployment appearing to separate from the target platform at a slower rate than the specified threshold. As a result, the algorithm did not reject the flares but instead used them as true targets. This is shown by the oscillations of the track point (red trace) in Figure 7 (a) and (b). The yellow trace represents the relative azimuth and elevation angles to the target while the blue traces are those of the flares. The wandering track point between the various flares caused changes in commanded normal and lateral accelerations to the



missile body (Figure 7 c). It should also be noted that the seeker reacquired and settle briefly on the target platform between 2 and 2.3 seconds but lost it again shortly after.

Figure 7 - Seeker Scopes: (a) Azimuth Track Point, (b) Elevation Track Point, and (c) Missile Body Acceleration

4.3 Combined IRC/LRC Results

The results of the combined IRC/LRC batch runs are presented in Figure 8. A total of 396 runs were conducted of which 388 (98%) were successful and 8 (2%) were not.

Figure 8- Combined IRC/LRC results for SAM/AAM engagements

4.4 Tunable Parameters

The results presented in Figure 9a suggest that as the IRC threshold increases, the number of “misses” increases and the effectiveness of the algorithm decreases. This result was expected since the intensity of a flare typically rises quickly and is much larger than that of an aircraft. Varying the threshold may also have an impact on how quickly a flare can be identified. A larger threshold means that the flare has to get closer to its peak intensity before it exceeds the specified IRC threshold. A threshold too close to unity may trigger a flare detection flag in certain engagement geometry because of the high close-in velocity between the target and the threat.

Figure 9b illustrates that as the LRC threshold increases, the number of “misses” increases and the effectiveness of the algorithm decreases. Again this is expected since, the higher the threshold, the “faster” a flare must separate from the aircraft in order to trigger the flare detection flag. The simulation allows the azimuth and elevation LRC thresholds to be set separately in order to be evaluated independently. The algorithm is dependent on the engagement range and



geometry. This type of analysis could be useful for IRCM developers to determine the speed thrusted flares should reach to successfully lure the incoming threat away from the aircraft.

Figure 9- Tunable parameter effects on IRC and LRC algorithms. (a) IRC Threshold,

(b) LRC Threshold, (c) IRCCM Memory Time and (d) Flare Hold Time

Figure 9c suggests that an IRCCM memory time between 1 and 1.5 sec may increase the effectiveness of the algorithm. The memory time is used to define the duration of the running average for the intensity ratio and LOS rate change calculations. In the case of the IRC algorithm, a short memory time (< 1 sec) acts as a high pass filter and the presence of flares in the apparent close-proximity of the platform has a significant impact on the intensity’s historical average. Conversely, a large memory time (>1.5 sec) may not increase the running average fast enough to account for aircraft’s intensity change as a result of the fast approaching missile. This could cause the target aircraft to be rejected as the true target and classified as a potential flare by the algorithms.

Figure 9d suggests that a shorter Flare Hold Time improves the performance of the IRC and LRC algorithms. When an object is identified as a potential flare it retains that state for the hold time duration. The purpose of this parameter is to prevent objects from rapidly changing states. An advantage of using a short hold time is the algorithm reassesses the detected objects more frequently to determine if they exhibit flare-like characteristics. A disadvantage of a short hold time is that when flare intensity starts to decay, it may no longer exceed the IRC threshold but still be greater than the platform and result in a break lock. Conversely, if the target platform is incorrectly identified as a flare (see Figure 11) and a longer hold time (>1.5 sec) is specified, the algorithm will not reassess the target until the hold time is exceeded. During this time the algorithm may track a flare with a much higher intensity which would increase the historical intensity average and may prevent any future reacquisition of the true target.

4.5 Individual Engagement Results

Figure 10 illustrates a generic SAM engagement in which the IRC algorithm successfully differentiates the target platform from the flares. The figure is a timed-sequence (from left to right) presenting three different views of the engagement. The top view (row 1) is the IR sensor view; the middle view (row 2) is the binary (threshold) view and the bottom view (row 3) is the IRC scope which displays the intensity ratio of individual objects in the scene.

At t=0.155 second (col 1), the IIR seeker is locked onto the target and the missile has been fired.

At t=0.565 second (col 2), two series of flares and the target platform can be distinguished in the IR sensor view but the binary processor only detected two objects because of their apparent close proximity. Neither of the two objects exceeded the specified IRC threshold (yellow trace in the IRC scope) and both were considered possible true targets.

At t=1.27 second (col 3), the flares nearly reached their peak intensity and the automatic gain control adjusted the IR sensor view to prevent pixel saturation. This resulted in the apparent attenuation of the target platform’s IR signature. The two series of flares and the target platform were completely separated in the binary view and the IRC scope clearly shows that the ratios are much greater than the threshold (red and cyan trace in the scope). The algorithm identified the two of the three objects as possible flares in the binary view (blue cells) and continued to track the true target (red cell).

At t=2.7 seconds (col 4), the flares separated into individual objects and continued to be rejected by the algorithm because of their high intensity ratios, which can be observed in the IRC scope from the additional traces at t>1.5 sec. As



the missile approached and the flares move out of the sensor’s field of view, the algorithm continued to track the true target (red cell in the binary view) until end game.

Figure 10 - Flare identification and rejection example

Figure 11 demonstrates an engagement where the deployment of the flare in the apparent close proximity of the platform resulted in a temporary break lock from the platform. At t=0.56 second (col 2), the IR sensor view displays the start of the flare deployment but because of the engagement range and resolution of the IR sensor, the processor cannot distinguish between the various objects and regrouped then into a single object. Since there is only one object in the field of view, it was identified as the reference object. The presence of a flare in the vicinity of the target platform raises the running average intensity of the reference object. A higher running average power reduces the ratio of all detected object and prevents the algorithm from correctly identifying potential flares. This can be observed by comparing the signal values in the IRC scope in Figure 10 to the same scope in Figure 11. The effects of the reduced intensity ratios is also illustrated at t=1.015 second (col 3) of Figure 11, where the tracking point (red cell) is set on a group of flares instead of the target platform. In this specific engagement, the algorithm reacquired the target shortly after and tracked it until end-game (col 4). However, the longer flares stay grouped with the reference object, the higher the running average is and the less likely the algorithm can reacquire the true target. Many of the misses in the IRC batch run results (Figure 5) are a result of the engagement geometry which placed a group of flares in the proximity of the line-of-sight of the incoming missile.



Figure 11 - Flare deployment in close proximity of platform

5. CONCLUSION The purpose of this project was to implement IRCCM algorithms based on target intensity and kinematic behavior in a generic IIR seeker model used in an IRCM effectiveness simulation tool called TESS. The IRC algorithm presented in this report compares the intensity of individual objects to the historical average of a reference object while the LRC algorithm compares the motion of individual objects with respect to reference object to discriminate false targets. The report evaluated the algorithms’ performance in a variety of engagement geometries and interpreted the batch run simulation results in terms of platform survivability. Physics-based software simulations and hardware-in-the-loop simulators are essential tools to develop effective countermeasures to defeat the next generation of threat systems.

ACKNOWLEDGMENTS

The author would like to acknowledge the contributions of Dr. T. W. Tucker, Mr. W.B. Vigder and Mr. D.S. Whitmore for their support and invaluable insight and knowledge on the subject matter of Electronic Warfare.

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