W. G. Bath

Overview of Platforms and Combat Systems

William G. Bath

ABSTRACT Air and missile defense is a complex process involving the coordinated operation of equipment and computer programs. The most effective defense generally is multiple layers of defense using different technologies in each layer such as long-range hard-kill, followed by hard-kill area defense, followed by both hard-kill and soft-kill (electronic warfare) self-defense. A combat system must merge, fuse, and de-conflict many sources of sensor data to produce a single usable track picture for decision-making. Throughout, sensors are controlled and sensor resource use is man- aged to meet the overall defense needs. As technical direction agent and technical adviser for many of the combat system elements, the Johns Hopkins University Applied Physics Laboratory (APL) performs the systems engineering, analysis, and experimentation that helps the Navy select the most combat system capability at an affordable cost.

INTRODUCTION Most Navy warships have combat systems capable of in overall air and missile defense capability shown in air and missile defense. Those combat systems are well Figure 1. described by the “detect–control–engage” paradigm; Aegis destroyers and cruisers are the Navy’s most that is, the components of the combat system can be capable air defense units because of their long-range, notionally grouped as follows: multifunction phased-array radars; their inventory of • Detect components that find and track air and many different anti-air warfare, ballistic missile defense, missile targets and electronic warfare weapons; and their complex control processes for processing sensor data, making • Control components that identify the targets and engagement decisions, and controlling those weapons. make the decisions to engage Aegis destroyers and cruisers can defend large areas against ballistic missiles by defeating them during the • Engage components that schedule and perform the midcourse phase of their flight using the Standard engagements with the goal of destroying or other- Missile-3 (SM-3) family in the exo-atmosphere, as well wise negating the targets as closer to impact during their terminal phase using the The scope of those components’ capabilities varies SM-6 family in the endo-atmosphere. The Aegis Ashore significantly with ship class, resulting in the variation combat system deployed in Europe uses a subset of the

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Midcourse defense Ballistic Self-defense missile (shooter is defended point)

Sea-based terminal defense

Naval integrated re control

Height above Earth Area defense

Cruise missiles and aircraft Hard kill Range to defended point

Soft kill

Onboard and/or offboard electronic attack

Aegis destroyer Aircraft carrier

Aegis cruiser Amphibious landing ship

Aegis Ashore Littoral warfare ship

Naval air (not Zumwalt destroyer considered here)

Figure 1. Comparison of the air and missile defense capabilities of different combat systems. (The chart at the top is not to scale.) same detect–control–engage components on land and (TDL) networks enable Aegis and other units to fight as provides for exo-atmospheric defense of US-deployed a coordinated force. forces, their families, and our allies in Europe. Aegis The USS Zumwalt (DDG 1000) brings to the Navy Ballistic Missile Defense (BMD) ships and Aegis Ashore a unique set of volume firepower and precision strike are part of the larger Ballistic Missile Defense System capabilities and is currently nearing deployment. The (BMDS), which is, itself, a global combat system that Zumwalt destroyer has an advanced gun system with integrates Navy, Army, and Air Force detect, control, a long-range land-attack projectile capable of launch- and engage components. Aegis destroyers and cruisers ing a guided projectile at extended ranges. Its air and can also defeat attacks from aircraft and cruise missiles. missile defense capabilities lie in between those of the Aegis is capable of extended-range engagements of Aegis fleet and those of aircraft carriers and amphibious aircraft and cruise missiles both over sea and over land ships. Zumwalt has a vertical launching system similar to using the SM-6 surface-to-air missile. With integrated that of Aegis and the control capability to launch self- fire control support, SM-6 provides an increased battle defense missiles as well as SM-2 missiles. space against threats over the horizon. Within the Aircraft carriers and amphibious ships are capable horizon, Aegis can defend both itself (self-defense) of projecting offensive power (Navy air and Marines and other units (area defense) using the SM-2 missile ashore). The air and missile defense detect–control– family and the Evolved Sea Sparrow Missile (ESSM). engage components on these ships, however, are gen- Aegis also can defeat threats using electronic warfare erally limited to self-defense. Self-defense is achieved measures such as jamming and decoys. The Cooperative either with electronic warfare, with shorter-range mis- Engagement Capability (CEC) and Tactical Data Link sile systems such as ESSM and the Rolling Airframe

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– AN/SPY-1B/D(mod)/D(V) – Aegis BMD 5.0/5.1 – VLS (SM-2, SM-6, ESSM) Aegis Baseline 9 Hard kill – AN/SPQ-9B AMD capabilities Rapid, incremental – CIWS today* – Mk 99 (3-4) improvements to pace – AN/SLQ-32(V)3/6/SKC* the threat via Aegis – Decoys Speed to Capability Soft kill – UPX-29 – Command and decision (on an as-needed basis) – Weapon control Control – CEC Baseline – TDL upgrade Networks

– AN/SPY-6 (AMDR) – Aegis BMD 6 Hard kill – Active missile upgrades – AN/SLQ-32(V)7 – Decoy upgrades Soft kill Capability trade-offs and engineering challenges for the future

– New BMD missions – Advanced offboard electronic attack Selected new – Next-generation weapons scheduling Aegis Baseline 10 algorithms AMD capabilities – Hard kill/soft kill integration 2023 – Next-generation sensor netting and integrated re control – Force sensor and weapons control Modeling, simulation, – Low-cost multifunction X-band radar and critical experiments – Railgun/hypervelocity projectile to select most capability – Nonkinetic kill at affordable cost – Resilient, cyber-resistant combat systems – Next-generation combat information center – Next-generation close-in weapon system * All portions of Baseline 9 are deployed except 9C2, which is imminent. Baseline 9C2 will include AN/SLQ-32(V)6/SKC.

Figure 2. Examples of planned Aegis combat system air and missile defense (AMD) evolution and potential capability trade-offs. VLS, vertical launching system.

Missile (RAM), or with guns (e.g., the Phalanx Close-in concurrent with area and self-defense against air and Weapon System, or CIWS). The combat system for these surface threats. For the area air defense and self-defense ships is the Ship Self-Defense System (SSDS). capability, increased sensitivity and clutter capability are Ship combat systems are major investments that needed to detect, react to, and engage stressing threats evolve over time to achieve new capabilities. Aegis and in the presence of heavy land, sea, and rain clutter. In SSDS ships are undergoing major capability upgrades the control and engage areas, Aegis Baseline 10 includes that include significant new sensor capabilities. The functional upgrades to make use of the richer data pro- Aegis combat system will evolve from Baseline 9 to vided by the AMDR, such as Aegis BMD 6 use of the Baseline 10 (Figure 2). This evolution features many new AMDR’s increased radar sensitivity and bandwidth in capabilities. The AN/SPY-6 Air and Missile Defense the engagement of ballistic missiles. Aegis Baseline 10 Radar (AMDR) will provide multimission capabilities, will leverage ongoing developments in active missiles to simultaneously supporting long-range, exo-atmospheric provide a more effective defense against evolving anti- detection, tracking, and discrimination of ballistic mis- ship cruise missiles. The AN/SLQ-32(V)7, which deploys siles, as well as area and self-defense against air and sur- in Aegis Baseline 10, includes the Surface Electronic face threats. For the BMD capability, increased radar Warfare Improvement Program Block 3, which provides sensitivity and bandwidth over current radar systems onboard electronic attack. The Soft-Kill Coordinator are needed to detect, track, and support engagements of (SKC) capability, an AN/SLQ-32 command and control advanced ballistic missile threats at the required ranges, subsystem, will be expanded to include coordination of

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– AN/SPS-48G* USS Nimitz – AN/SPS-49A* AMD capabilities – AN/SPQ-9B* today* – Mk 9 T/I (4)* Hard kill – Mk 9 T/I (4)* Rapid, incremental – RAM improvements to pace – ESSM the threat via the – CIWS (3) Fire Control Loop – AN/SPN-43* ( ) Improvement Program CATC – AN/SLQ-32A(V)4 – AN/UPX-29 Soft kill (on an as-needed basis) – SSDS Control – CEC – TDL Baseline upgrade Networks

USS Gerald R. Ford (CVN 78) ~2021

Capability trade-offs and engineering challenges for the future

Multifunction radar (DBR) Hard kill – Advanced onboard electronic attack AN/SLQ-32(V)6/SKC Soft kill – Advanced offboard electronic attack – Next-generation weapons scheduling algorithms Modeling, simulation, – Hard kill/soft kill integration and critical experiments – Next-generation sensor netting and to select most capability integrated re control at affordable cost – Low-cost multifunction X-band radar – Resilient, cyber-resistant combat system – Next-generation combat information center * This function will be replaced by the multifunction radar. – Next-generation close-in weapon system

Figure 3. Examples of planned aircraft carrier SSDS combat system evolution and potential capability trade-offs. (Amphibious ships, which also have the SSDS combat system, are also evolving with related improvements and capability trade-offs.) CATC, carrier air traffic control. onboard electronic attack and an improved inventory long-range surveillance and track functions of the of decoys. AN/SPS-48 and AN/SPS-49 radars, provide data for The SSDS-based combat system on aircraft carriers carrier air traffic control (currently provided by the and amphibious ships has historically relied on a AN/SPN-43), and provide the horizon surveillance and suite of older sensors (some initially designed in the tracking capability of the SPQ-9B radar and the fire 1960s) that have undergone periodic modernizations. control functions of the Mk 9 tracker/illuminator. The Radar surveillance and target tracking are provided multifunction radar will enable better control of ESSM by the AN/SPS-48G, AN/SPS-49A, and AN/SPQ-9B missile trajectories and more accurate handover to the radars. Additional surveillance and tracking as well as ESSM seeker, improving ESSM capability against anti- illumination for semiactive missile homing are provided ship cruise missiles. by the Mk 9 fire control system. Carrier air traffic control Selecting the most capability at affordable cost is a is supported by the SPN-43. With the new aircraft carrier challenge in development of any new combat system USS Gerald R. Ford (CVN 78), these functions will be baseline. Figures 2 and 3 show candidate systems and replaced by the new Dual-Band Radar (DBR) (Figure 3). capabilities for future baselines of Aegis and SSDS, This new multifunction radar being developed for the respectively. APL performs modeling and simulation and CVN 78 is a combination of the X-band AN/SPY-3 and critical experiments to inform the selection of an afford- S-band AN/SPY-4. However, alternative radar designs able subset of these systems and capabilities for new are being considered for subsequent aircraft carriers baselines. In addition to the major baseline upgrades, CVN 79 and CVN 80 as well as for new amphibious the Navy continues to explore techniques for deploying ships. The multifunction radar will accomplish the new capabilities rapidly on an as-needed basis. Aegis and

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Combat information centers Missile launchers

Algorithms, computing plant, and code Exo-missiles War ghter interface/training/planning Endo-missiles

Shipboard radars/IFF • Sensor • Target identi cation, • Weapon selection processing typing, and and scheduling Weapons and control discrimination (either organic or through networks)

• Sensor data • Engagement • Soft-kill Sensors association, decision coordination fusion, and – Missiles/guns • Weapons control track manage- – Electronic – Hard kill Electronic warfare ment warfare – Soft kill Guns

Networking

Offboard sensors Offboard (through networks) (through

Communications Weapon control links Illuminators

Figure 4. A general combat system. Actual combat systems have a subset of the components pictured. Successful engagements require coordinated operation of many combat system components. IFF, identification friend or foe.

SSDS use the Aegis Speed to Capability and the Fire is generally a priori context information available to Control Loop Improvement Program, respectively, to the war­fighter. This context will define who the likely respond to urgent needs in the fleet. enemy is, what sort of threats he has in his inventory, Air and missile defense is a complex process involving and, in general terms, how he is likely to attack. Within the coordinated operation of equipment and computer today’s combat systems, this information is held as “doc- programs. Figure 4 shows a general ship combat system. trine,” a collection of rules that define how the combat The workhorses of sensing on a ship are its shipboard system will respond to sensor information. For example, radars—particularly the multifunction radars. These today’s identification doctrine defines, given the con- radars are augmented by other shipboard radars serving text, which additional pieces of sensor evidence are nec- specific purposes. In addition, ships can access offboard essary to conclusively identify the target. The next likely sensors located on other ships, aircraft, land sites, input to the combat system is some early indication from and space via secure communications. Sensors are ISR (intelligence, surveillance, and reconnaissance) controlled and sensor resource use is managed to meet that an attack is coming; this early indication alerts the the overall defense needs. Individual measurements combat system to the object’s presence and often identi- made by the entire sensor set are associated, and in some fies the object, but it does not necessarily provide precise instances fused, with other sensor data. In all cases, kinematics or low latency. Today, there is little quantita- tracks are generated. Each track should correspond tive integration of contextual and ISR data with organic to one physical object. A track is the combat system’s sensors. The quantitative integration of a priori context sum total knowledge of an individual object, including and ISR is a challenge and growth area for new combat its kinematics—e.g., vector position and velocity; the system designs. classification of the object (aircraft, cruise missile, Once targets are within sensor range, the combat ballistic missile, clutter, debris, etc.); the type of the system receives sensor measurements (e.g., onboard or object (e.g., if it is a cruise missile, which cruise missile offboard radar) indicating more precise kinematics at type is it); and when applicable, the identity of the low latency, but these data may or may not include fea- object (e.g., friend or foe). tures for identifying the object. One of the challenges Figure 5 illustrates the association and tracking prob- is to correctly associate all of these pieces of data into lem. In any part of the world on any given day, there “tracks.” As measurements are associated to form tracks,

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sensor networks such as the CEC. The principal chal- lenge is the diversity of the data received and the need to make one unit’s track management process interopera- ble with multiple units’ track management processes. For X example, each source will generally have a different way of characterizing the accuracy of the kinematic track data, and some sources may provide incomplete charac- terizations. Similar diversity exists in the characteriza- tion of target identity and type. Different units designed Y in different time frames and with different missions will A priori context: predicting likely object locations, have different rules and algorithms for supporting the types, and behavior creation of a common track numbering and identifica- tion system. In addition, the networks may deliver data ISR indicating object presence and often identity, but not necessarily with precise kinematics or low with different time delays, biases, and data dropouts. latency The process by which all these sources are reconciled into a single usable track picture is generally called track Sensor no. 1 measurements (e.g., onboard or offboard radar) indicating more precise kinematics management and has been an active area of research at low latency, but may or may not include features and development at APL for many years. for identifying the object A good example of the metrics for the single track Sensor no. 2 measurements (e.g., onboard or picture is given by the Single Integrated Air Picture offboard radar) indicating more precise kinematics Metrics1 developed by the joint services (Army, Navy, at low latency, but may or may not include features for identifying the object Air Force, and Marine Corps) for air track (vice ballis- tic missile) tracking (Figure 7). Note that the metrics Object position (ground truth) cover both track kinematics and attributes. In addition,

Figure 5. A two-dimensional representation (x, y) of a multi­ dimensional tracking problem. In this example, three targets are close enough together to challenge association and filtering algorithms. the track kinematic state is calculated (and used for X subsequent associations). Track filtering refers to the algorithms that transform a sequence of measurements into such a track state and is discussed in the article by S. A. Hays and M. A. Fatemi in this issue. Figure 6 shows notional track states that have been calculated by asso- Y ciating and filtering the measurements in Figure 5. In A priori context: predicting likely object locations, this illustration, the tracking process has worked well. types, and behavior The number of tracks in Figure 6 equals the number of ISR indicating object presence and often identity, objects, the track states converge over time to the actual but not necessarily with precise kinematics or low latency object positions, measurements from different sensors have been associated correctly, and the tracks can be Sensor no. 1 measurements (e.g., onboard or offboard radar) indicating more precise kinematics extrapolated into the future to accurately predict target at low latency, but may or may not include features position. However, the tracking process can be chal- for identifying the object lenged in all these areas by large sensor measurement Sensor no. 2 measurements (e.g., onboard or accuracies, low sensor update rates, highly unpredictable offboard radar) indicating more precise kinematics object motion, and object spacing. In the case of mul- at low latency, but may or may not include features tiple sensors, measurement biases and different sensor for identifying the object measurement dimensions are also challenges. Overcom- Object position (ground truth) ing these challenges remains a subject of research in Object track calculated in combat system using combat system design. context, ISR, and sensor measurements A combat system must merge, fuse, and deconflict many sources of track data to produce a single usable Figure 6. The combat system calculates tracks representing a track picture for decision-making. This includes all local best estimate of the object kinematics. This figure depicts quan- sensors as well as track data from tactical data links such titative integration of contextual and ISR data with organic sensor as Link 16/11 and measurement and track data from tracking—a challenge in the design of new combat systems.

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the metrics measure the degree of Track management metrics Importance of metrics commonality between the track Completeness: The air picture is complete Tracks on all targets available pictures on different ships and air- when all objects are detected, tracked, and for engagement reported. craft. This commonality is essen- Prevents confusion leading Clarity: The air picture is clear when it does tial for sharing of engagement and to delays and “waste” of not include ambiguous or spurious tracks. identification data. engagement resources Continuity: The air picture is continuous Once tracks exist, they become when the tracks are long-lived and stable. the organizing tool for the engage- Kinematic accuracy: The air picture is ment sequence. The success of the kinematically accurate when the position engagement depends on the fidel- and velocity of a track agrees with the position and velocity of the associated Preserve short reaction time: ity of the track on the target being object. prevent delays due to engaged. As the target closes in ID completeness: The ID is complete when reidentifying targets and/or range to its objective (Figure 8a), all tracked objects are labeled in a state restarting engagements other than unknown. more sensor measurements are ID accuracy: The ID is accurate when all made, resulting in continual tracked objects are labeled correctly. improvement (Figure 8b) in the ID clarity: The ID is clear when a tracked accuracy of the track kinematics object has no con icting ID states. (e.g., position, velocity, and accel- Commonality: The air picture is common Enables resource coordination: when the tracks held by each participant – Shooter to shooter eration) and in the certainty in have the same track number, position, – Shooter to provider target identity and characteristics and ID. – Tracker to track (Figure 8c). However, most weap- ons require that additional sensor Figure 7. Typical metrics for the combat system air track picture.1 resources (e.g., different radar waveforms, higher update rates, high priority in radar scheduling, (a) or in some instances, additional Threat 2 sensors) be applied to achieve Threat 1 Threat 3 Range to z a “fire-control-quality track” intercept (Figure 8d) capable of supporting Closer range all or part of the following:

• Determination of intent (b)

• Decision to engage Errors in target Threshold needed • Determination of acceptable kinematics for successful engagement weapon launch times and Smaller errors intercept points (scheduling) More Combat systems are designed certain (c) using error budgets for their criti- Threshold needed Certainty for successful cal functions. These budgets in target engagement identify the maximum errors that identity and characteristics

each combat system function can More certain tolerate, and they allocate a por- tion of that maximum error to (d) each of many contributing factors. Kinematic track state errors are Sensor generally significant contributors resource Fire-control- to the maximum error. To meet use quality tracking Tracking Surveillance challenging error budget alloca- More resource use tions, most combat systems filter Time Intercept Initial detection measurement data differently for times times different combat system func- tions (Figure 9). One example of Figure 8. A typical engagement sequence against a raid of threats. Sensor data are gath- these differences is the degree of ered beginning as soon as a target is detected, eventually leading to sufficient kinematic filtering. Heavier filtering (smaller accuracy and certainty in identity and characteristics for a successful engagement. Sensor filter gains) will weight new mea- resource needs to meet these thresholds vary throughout the engagement.

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The next part of the Errors in long-term prediction (low target maneuvers) engagement decision is to Errors in short-term prediction (medium/high target determine which weapons maneuvers) (missiles, guns, and/or electronic Point where errors are minimized by gain selection warfare) have the capability to negate the threat and to select Medium target (maneuvers) the weapons to be used based on their inventory and predicted High target (maneuvers) effectiveness. The engagement decision is one of the factors that drives the need for precise track kinematics because the track kinematic state may need to be

Kinematic track state errors predicted well into the future (e.g., to account for the fly-out time of a ship-launched missile to a predicted intercept point). An underlying principle 0 in weapons selection and Heavier ltering Tracking ltering gains Lighter ltering scheduling is the concept of Filter design region for combat Filter design region for combat “depth of fire”—multiple layers system functions such as: system functions such as: of defense in range. The most • Engagement decision • Measurement/track association effective defense generally is • Intercept point prediction • Seeker handover multiple layers of defense using • Weapon and resource scheduling • Illuminator pointing different technologies in each layer such as long-range hard Figure 9. To meet engagement error budgets, most combat systems process sensor mea- kill (e.g., naval integrated fire surement data differently for different combat system functions. For example, heavier track control), followed by hard-kill filtering (smaller filter gains) will produce kinematic estimates with a smaller variance due to measurement noise and enable longer-term time prediction. Lighter track filtering will area defense, followed by both produce kinematic estimates more tolerant of unpredictable target motion (maneuvers). hard-kill and soft-kill (electronic warfare) self-defense. The ballistic missile defense analogue of this surements less relative to the current track state and pro- would be midcourse defense followed by sea-based duce kinematic estimates with a smaller variance due to terminal defense. measurement noise. However, these filters are not very Consider air defense as an example. Assume a raid of tolerant of unpredictable target motion (maneuvers). NT threats. A typical measure of air defense performance Lighter filtering (larger filter gains) will weight new mea- is the probability of raid annihilation (PRA). For NL layers surements more relative to the current track state and of defense, each with a probability of killing the target produce kinematic estimates with a larger variance due (PK), the mathematical advantage of depth of fire can to measurement noise. Although these filters are more be easily demonstrated in a very simplified analysis. To tolerant of unpredictable target motion (maneuvers), annihilate the entire raid, each of the NT targets must be their variances makes them less desirable for functions killed, and there are NL opportunities to kill each target. that require long-term time prediction. The simplified analysis assumes that all these events are Once tracks exist, they need to be characterized as to statistically independent, in which case, PRA is given by N their type and identity. Is this target a threat attacking PRA = [1 – (1 – PK)NL] T. a defended area (which should be engaged) or another This equation is plotted in Figure 10 for a raid size object such as a commercial airliner or a nonlethal piece of five threats. Note that achieving a very high level of debris (which should not be engaged)? In addition, of defense (high PRA) with only a single layer requires the greater number of target characteristics that can be a very high probability of kill in that layer. That high known (e.g., type of threat), the more effective the engage- probability of kill can be difficult to achieve with a ment can be. Both determination of type and determina- single technology (e.g., with a single missile type or tion of identity generally require dedication of additional single electronic warfare strategy) because any defensive sensor resources to achieve the confidence necessary for technology has weaknesses that could be exploited by a successful engagement. The identity and characteristics the adversary. A layered defense using different tech- of the track, as well as its kinematics, are compared with nologies in each layer requires a relatively lower prob- operational doctrine to make the engagement decision. ability of kill in each layer and generally makes a high

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1.0 ments to obtain a track state that is well matched to indi- Depth of re = 3 vidual weapon control requirements (e.g., obtain stable 0.8 velocities for long time predictions until intercept). The Depth of re = 2 combat system initializes the missile (in the launcher) to 0.6 set up a common time frame and coordinate frame for communication of data between the combat system and R A

P the missile during flight. Once the missile is properly 0.4 initialized, the motor is ignited. Shortly after launch, the combat system begins communication with a link- 0.2 Depth of re = 1 capable missile using the weapon control link (Figure 4). Depending on the missile type and phase of flight, it will 0 be controlled (1) autonomously (fire and forget); (2) by 0.5 0.6 0.7 0.8 0.9 1.0 P providing it with ongoing uplinked target data; or (3) by K providing it with acceleration commands. Target data Figure 10. Multiple layers of defense using different technolo- from the combat system will be used by the missile to acquire the target with its seeker (generally active radio gies generally make a high PRA more achievable. This graph depicts the results of simplified analysis for a raid of five threats frequency [RF], semiactive RF, passive RF, or infrared), and the assumption that all engagements are statistically after which the missile can begin homing on the target. independent. REFERENCE P more achievable. The different technologies used 1E. Byrd et al., “Single Integrated Air Picture (SIAP) Attributes Ver- RA sion 2.0,” Single Integrated Air Picture (SIAP) System Engineering in different layers make it more likely that the statistical Task Force (SE TF), Arlington, VA, Tech. Rep. 2003-029, Aug. 2003. independence assumption is valid and thus more likely that the gains from multiple layers occur. As a result, most ships have a mixture of both hard-kill and soft-kill William G. Bath, Air and Missile defensive technologies as well as different types of hard- Defense Sector, Johns Hopkins Uni- kill and soft-kill weapons. versity Applied Physics Laboratory, In addition, depth of fire can conserve inventory. If Laurel, MD the engagement is successful in the first layer, and if that William G. (Jerry) Bath is supervi- success can be confidently measured, then the resources sor of the Combat Systems Branch in required for the subsequent layers do not need to be APL’s Air and Missile Defense Sector. expended on that threat. He received BES, MSE, and PhD degrees in electrical engineering from Johns Hopkins Uni- As the engagement proceeds, more combat system versity. Dr. Bath’s research interests are sensor integration resources of are generally required for success (Figure 8d). and automation, radar systems tracking, filtering, and sta- A significant challenge in combat system design is tistical estimation. His experience includes development deciding which of these weapons to employ when and of technical requirements and the algorithmic approach how to schedule combat system resources (e.g., sensors, for the Navy’s Cooperative Engagement Capability—now deployed on 130+ ships and aircraft; development of a launchers, illuminators) to accomplish the engagements. reduced-cost/rapid-deployment solution for Navy interop- The schedule is dynamic, changing as new sensor erability; response to Seventh Fleet electromagnetic data are provided, additional targets are disclosed, and warfare issues—resulting in a change in fleet concepts of initially scheduled engagements are executed. These operations, and development of avionics models for foreign challenges are discussed in the article by M. R. Smouse anti-ship cruise missiles (to enable analysis of both US Navy hard kill and soft kill). He is currently the principal et al. in this issue. investigator for an APL initiative to develop foundational As engagements are scheduled, the combat system combat system concepts to close the kill chain against maintains the overall engagement schedule and exe- next-generation threats. He is a fellow of the IEEE. His cutes the engagements per that schedule. Most combat email address is [email protected]. systems perform custom filtering of the sensor measure-

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