This article was automatically translated from the original Turkish version.

Dogfight

Dogfight is a highly dynamic interaction in which two or more aircraft simultaneously manage geometry, energy, and perception dimensions over very short time scales to gain mutual advantage. Geometry encompasses relative position, orientation, and angular variables; energy includes speed, altitude, and total energy state; perception involves situational awareness and sensor/aiming data. Within this framework, the primary objective of the dogfight for the attacker is to generate a firing solution that simultaneously satisfies the range, angle, and timing requirements of the weapon-aiming-target triad; for the defender, it is to prevent the emergence of these same conditions. Success depends on the ability to balance these two objectives within a single decision process.

The defining characteristic of the WVR (within visual range) environment is the high decision rhythm and command frequency. In BVR (beyond visual range) engagements, outcomes are primarily shaped by sensor ranges, network-centric information flow, and long-range missile envelopes; in WVR, visual and short-range perception become dominant. This manifests concretely in how the line-of-sight (LOS) direction and LOS rate (angular velocity relative to the target) directly determine the firing window. Reliable operation of aiming systems depends not only on the target entering range but also on reducing LOS rate sufficiently to maintain stable aim on the target. Therefore, dogfighting is less a problem of optimizing spatial position alone and more a problem of managing the “angular meaning” that the spatial state creates relative to the target. This approach, known in the literature as the “space–angle game,” refers to integrating spatial decisions—such as turn plane, altitude layer, and distance—with angular decisions—such as AOT (angle-off-tail), lead/pure/lag tracking angles, and HOBS (high off-boresight)—and proper timing. In other words, advantage often arises from the combination of selecting the correct plane, maintaining the correct speed band, and sustaining LOS rate within the aiming system’s required range.

The physical conditions of a firing solution are defined by kinematic and energy constraints. Idealized, the relationship between turn radius and angular turn rate is

R≈gV2 , ψ∝Vg

where V is flight speed and g represents the effective centripetal acceleration requirement. As speed decreases and effective g increases, ψ grows, meaning angular generation accelerates; however, energy consumption simultaneously increases and sustainability decreases. Therefore, instantaneous maximum turn capability must be distinguished from sustained turn capability; the former is evaluated for short-term angle gain, the latter for tracking and position maintenance. In energy–maneuverability theory, specific excess power Ps quantifies this balance: positive Ps enables stabilization before firing and escape after firing, while low or negative Ps restricts the continuity of aggressive maneuvers. Selecting the speed band near the “corner speed” provides a practical compromise between sustained angle generation and energy conservation for most platforms.

Weapon–aiming integration is tightly bound to this kinematic foundation. In gun firing, projectile flight time and predicted impact point relative to the target require low LOS rate and stable application of lead angle corrections. For short-range infrared-guided missiles, reliable seeker lock within the no-escape zone (NEZ) is essential; this necessitates simultaneous satisfaction of range and angular constraints at the moment of firing. Although helmet-mounted sights and HOBS capabilities expand the firing volume, the practical firing window narrows if LOS rate and tracking quality are insufficient. Thus, the firing solution is not merely a matter of entering range but a stabilization problem requiring “temporal continuity.”

For the defender, the goal is to disrupt the firing solution along all three axes (geometry, energy, and perception). Geometric disruption is achieved through sudden high-g turns that increase LOS rate and challenge aim stabilization, by breaking plane alignment with out-of-plane components, and by managing closure at close range to induce overshoot. On the energy axis, unloaded extension and rapid “reset” maneuvers allow the defender to move the opponent away from the corner region while recovering their own Ps budget, often using spiral or OOP combinations. On the perception and guidance axis, countermeasures such as flares/chaff and aggressive plane-breaking maneuvers saturate the seeker or degrade visual/tracking quality, thereby shortening the firing window; even against HOBS, rapid directional changes can reduce helmet cueing opportunities. These actions do not rigidly separate attack and defense; most maneuvers can produce both production (offensive) and denial (defensive) effects depending on context.

Execution relies on a continuous decision loop. In the perception phase, LOS direction and rate, AOT/aspect, closure, and Ps difference are assessed; these data form the basis for selecting the appropriate turn plane (one-circle, two-circle, or OOP) and speed band. In the application phase, the attacker enters a plane that reduces LOS rate, remains near the corner band, and attempts to open the lead/pure window; the defender increases LOS rate, breaks plane alignment, and performs an energy reset. In the evaluation phase, the continuity of the window, proximity to g/AOA/structural limits, and remaining Ps budget for post-firing escape are reassessed. Thus, the definitions of problem (multi-target maneuver game), distinction (WVR centrality of LOS and tempo), and goal (producing–denying a firing solution) converge into a single technical framework.

This unity repositions dogfighting not as a mere pursuit of “getting behind” but as an interdisciplinary problem requiring the integrated management of energy, geometry, and perception. From an objective performance standpoint, what determines success is not the repertoire of individual maneuvers but the capacity to sustain LOS rate within the desired range through correct plane–speed–timing selection, maintain continuity without violating the Ps budget, and simultaneously satisfy aiming–weapon requirements. This constitutes a common metric valid for both human pilots and autonomous systems.

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History and Evolution of Doctrine

The historical evolution of dogfighting has been shaped by technological advancements as profound as aviation itself, involving gradual yet radical redefinitions of doctrine and tactical priorities. During World War I, aerial combat was primarily based on basic directional capability and pilot visual tracking. Aircraft of the era had low speeds and limited altitude performance; engagements therefore mostly consisted of tight circular maneuvers, particularly continuous horizontal turns at low speed, with a linear geometric understanding prevailing that “who gets behind wins.” This phase laid the groundwork for the embryonic forms of what would later become the core of BFM (Basic Fighter Maneuvers): lead–lag–pure pursuit, yo-yo variants, and scissors. The transition from piston-engine to turbojet eras dramatically increased speed and climb rates; however, these new performance levels fundamentally altered maneuver dynamics. Continuous horizontal turning became increasingly impossible due to structural limits and high speeds, directing pilots toward energy-conserving vertical or semi-vertical maneuvers. Thus, dogfighting ceased to be merely an art of geometric positioning and became simultaneously an energy management problem.

One critical breakthrough enabling this transformation was the introduction of fly-by-wire (FBW) technology. NASA’s digital FBW test program on the F-8 Crusader replaced traditional mechanical flight controls with digital computer-based control laws, redefining the relationship between pilot inputs and aerodynamic response. This system allowed aircraft to be controlled without relying on natural aerodynamic stability, making it possible to safely execute critical maneuvers at angles of attack far beyond what pilots could normally manage. FBW did not merely ease control; it introduced a layer of envelope protection that automatically safeguarded against structural g-limits, AOA exceedance, or critical speed thresholds. This development reshaped classical dogfight geometry intuition by replacing it with the question: “How critically can the aircraft be maneuvered safely?” The FBW era also marked the foundational period for the conceptual understanding that “the pilot flies the tactic, not the aircraft.”

Fourth- and fifth-generation fighter aircraft have redefined dogfight doctrine not only through aerodynamic and energy capabilities but also through perceptual dominance elements. While “energy superiority” remains a critical parameter, “perceptual superiority” has become an equally decisive factor in modern engagements. Sensor fusion capabilities, IRST (Infrared Search and Track), helmet-mounted sight systems (HMD/HOBS), AESA radar architectures, and network-centric data sharing have enabled pilots or autonomous systems to evaluate not only the immediate environmental situation but also potential tactical outcomes in advance. In this context, dogfighting today is not merely a physical maneuvering contest but also a contest of perceptual and cognitive temporal superiority. On modern platforms, a maneuver is executed not only to alter geometry but also to delay the opponent’s decision cycle; this represents an elevation of the classical “maneuver game” approach to a higher level.

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This transformation also demonstrates that dogfight doctrine is not confined to the atmosphere. With the emergence of the possibility of satellite-to-satellite engagements in space—particularly in low Earth orbit—the concept of “space dogfighting” has entered the literature at a theoretical level. In this above-atmosphere scenario, the primary determining factors are not lift generation and aerodynamic drag but orbital phase relationships, apsidal precession, relative velocity differences, and Keplerian dynamics. Pilot commands are replaced by phase synchronization and timing strategies; the classical turn radius relationship R=V2/g is superseded by relative position and time coordination dictated by orbital mechanics. In this context, space dogfighting can be viewed not as a complete departure from traditional dogfighting but as a natural extension of the “space–angle game” approach: the medium differs, but superiority is still determined by the synthesis of positional and temporal advantage.

Kinematic and Aerodynamic Foundations

The physical foundations of dogfighting require an understanding of fundamental aerodynamic forces and their effects on aircraft control axes. An aircraft’s maneuverability depends on the continuous reconfiguration of the balance between lift, weight, thrust, and drag. The aircraft manages this force balance along three primary axes (pitch, roll, and yaw) through control surfaces and flight control systems. During maneuvers, particularly in the pitch axis, acceleration generates positive g-loads. These accelerations are constrained by pilot physiological tolerance and structural limits. The diagram known as the “maneuvering egg” provides a practical visualization of how these limits vary with speed–g-load relationships; at low speeds, the aircraft cannot generate high g, and at high speeds, structural limits impose similar constraints. This model illustrates the aircraft’s instantaneous maneuvering envelope.

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Factors determining an aircraft’s maneuverability include both fixed and variable elements. Structural load limits (e.g., +9g limit), maximum AOA (angle of attack) values, and the boundaries permitted by flight control algorithms are defined as immutable physical constraints. Wing loading (weight per unit wing area) and thrust-to-weight ratio are fundamental flight characteristics that directly determine performance; vertical energy retention capacity depends on thrust-to-weight ratio, while horizontal turn performance depends on wing loading. The relationship between these parameters is illustrated in the V–n diagram, which plots speed on the horizontal axis and g-load on the vertical axis, showing at which speeds the aircraft can generate what g-load and at which speeds structural limit exceedance risks occur. The speed band known as “corner speed” is a critical breakpoint in this diagram: at this point, the aircraft achieves its maximum instantaneous turn rate, or highest angular velocity generation capacity. However, the sustainability of the corner region depends on the engine’s ability to overcome drag, as well as factors such as engine cooling and fuel consumption.

When analyzing turn performance, two key quantities emerge: turn radius and angular turn rate. In an idealized scenario, turn radius R and angular rate ψ can be expressed as:

R≈gV2,ψ∝Vg

where V is flight speed and g is effective centripetal acceleration. These relationships show that low speed and high g conditions produce high angular turn rates, while high speed results in larger turn radii. However, the distinction between instantaneous and sustained performance becomes evident here. Instantaneous maneuvers achieve sudden angle gain through short-duration high AOA and g values, while sustained maneuvers provide longer-term tracking and positional advantage by conserving energy. The difference is fundamentally determined by energy parameters such as engine thrust, drag curve, and fuel efficiency.

In this context, energy–maneuverability theory (EM) transforms the physical nature of dogfighting from a purely geometric problem into an energy-based model. In this approach, an aircraft’s total mechanical energy at any moment is divided into kinetic energy (21mV2) and potential energy (mgh), and their rates of change are analyzed. Specific excess power Ps is defined as the rate of change of total energy relative to weight:

Ps=WT−D.V

where T is thrust, D is drag, W is weight, and V is speed. When Ps>0, the aircraft is gaining energy; when Ps<0, it is losing energy. This value guides critical decisions during both attack and defense: positive Ps is required for pre-firing stabilization and post-firing escape; negative Ps leads to rapid degradation of maneuverability. Thus, the pilot or autonomous system strives not only to select the correct angle but also the correct energy state; for example, high or low yo-yo maneuvers consciously trade speed and altitude to optimize either instantaneous angle generation or sustained tracking.

Geometry and Basic Maneuvers (BFM)

Geometry and basic maneuvers are concrete tools directly linked to the ultimate goals of dogfighting: achieving a firing solution and preventing the opponent from achieving one. In this context, maneuver selection is based not on aesthetic advantage but on a combination of relative speed, turn capability, load factor, and weapon/sensor constraints. The mathematical basis of geometric concepts can be summarized by the relationships between centripetal acceleration and load factor; during a flight turn, centripetal acceleration ac=V2/R and load factor n are related by ac=ng; thus, turn radius R and angular turn rate ψ can be expressed as:

R=ngV2,ψ=RV=Vng

These relationships directly show how maneuver choice is constrained by speed and achievable n limits; low speed and high n increase instantaneous angle generation but negatively affect sustainability and energy cost.

The concepts of one-circle and two-circle describe the macro-geometry of relative turn patterns between two platforms. In one-circle interactions, both parties’ turn centers lie approximately on a common circular path; in this case, the relative position between tracker and target is resolved along a single circular track. In two-circle scenarios, each party’s turn occurs with different centers and radii, leading to more complex outcomes in terms of expected intersection points and waiting times. Which geometry emerges depends on factors such as the platforms’ entry speeds, angle gain capabilities (turn rate), and initial closure rate. Practical tactical selection is evaluated based on how the chosen plane alters LOS angular velocity and generates the necessary weapon windows.

The use of out-of-plane (OOP) maneuvers provides various tactical advantages compared to planar (single-plane) interactions. Adding an OOP component disrupts the opponent’s expectation of a horizontal plane turn match, creating unexpected 3D geometry that can reduce LOS angular velocity and temporarily challenge seeker/aiming configurations. OOP choices can be energetically costly, as out-of-plane maneuvers typically require speed/altitude trades or high load factors. Therefore, OOP is only preferred when energy budget and structural limits permit, specifically to achieve unexpected geometric disruption.

Among offensive maneuvers, the lead/pure/lag pursuit categories determine which target point is aimed at. Lead pursuit involves aiming at the target’s predicted future position to compensate for missile or projectile flight time; this is especially important in gun firing. Pure pursuit directs the weapon line toward the target’s current direction and can be used for short-duration shots; lag pursuit is generally preferred for closure control and energy gain, followed by a transition to lead at the optimal moment to create a firing window. Yo-yo maneuvers (high and low yo-yo) are applications of these pursuit strategies along the energy–geometry axis: in high yo-yo, the attacker adds a vertical component to generate lead angle while trading speed for altitude; in low yo-yo, speed is preserved to enable more aggressive directional changes at shorter range. Both variants offer a conscious trade-off between lead angle and speed/altitude exchange.

Tactics such as scissors and barrel-roll attacks aim to disrupt the line-of-sight at close range or increase the opponent’s likelihood of overshooting. Scissors tactics involve reciprocal short turns and directional changes that cause the opponent to lose alignment, induce excessive maneuvering, and thereby narrow the firing window. Barrel-roll attacks and displacement roll maneuvers are used to alter the attacker’s aim alignment and achieve an unexpected angular position to create a firing window; displacement roll is particularly effective for rapidly changing aspect angle.

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Defensive maneuvers typically focus on geometric disruption and energy reset. Break turns—sudden maximum- n turns—are a classic method to disrupt the attacker’s aim stabilization; this maneuver rapidly increases LOS angular velocity and can temporarily invalidate the attacker’s aim. Unloaded extension (unloaded acceleration and extension) is a strategy to gain speed, increase distance, and recover energy; the goal is to escape the missile threat envelope and rebuild the Ps budget (specific excess power) for the next engagement. Horizontal-plane defensive variants such as flat-scissors are preferred under low-altitude and limited vertical-space conditions; spiral dive escapes, though risky, are used to confuse missile seekers or disrupt the attacker’s tracking direction.

Energy and weapon integration constitute a high-level coordination principle determining BFM effectiveness. Weapon types impose different kinematic and angular requirements: gun firing requires consideration of projectile flight time, aim stabilization, and lead calculation; thus, gun firing demands short, stable windows with very low LOS rate. Short-range guided missiles involve constraints represented by concepts such as seeker cone angle and no-escape zone (NEZ); reaching these requires a specific immediate-target geometry and closure profile. High-off-bore-sight (HOBS) capability and helmet-mounted sight systems (HMD) expand the firing volume by enabling the pilot to fire at targets with high off-axis angles using head gaze; however, the practical benefit of HOBS is limited by seeker and control delays in relation to LOS rate. Therefore, tactically, a platform must consciously balance its ability to rapidly place the target within the NEZ against the Ps budget it retains for post-firing escape.

Dogfight Under Missile Threat

Missile threat emerges as a factor that fundamentally alters engagement dynamics in dogfighting; the presence of a missile threat in close combat directly affects both tactical choices and energy/performance management. Air-to-air missiles generally operate on a two-phase guidance logic (cruise + terminal); in the terminal phase, seeker data and guidance laws determine target acquisition. This section provides a brief technical overview of missile kinematics and guidance principles, then evaluates the multifaceted nature of evasion strategies from an energy–tactic dilemma perspective, and finally examines the role of practical countermeasures and mission context.

One of the most common modeling approaches for missile kinematics and guidance is the proportional navigation (PN) law. According to this principle, the commanded lateral acceleration of the missile is proportional to the angular rate of the line-of-sight (LOS) to the target. The simple PN formulation can be expressed as:

acmd=NVcλ

where acmd is the missile’s commanded lateral acceleration, N is the navigation constant, Vc is the closing velocity, and λ is the LOS angular rate. PN effectiveness is tightly dependent on the dynamic limits of the target and missile and the accuracy of the seeker’s measurements; modern systems enhance PN with augmentations (e.g., augmented PN or nonlinear PN variants) that account for seeker noise, delays, and aerodynamic limits. The missile’s own maneuver limits—determined by thrust capacity and maximum effective lateral acceleration from control surfaces—define the boundaries of this guidance; thus, a target’s evasion success fundamentally depends on this force–acceleration balance.

Evasion principles counter this guidance logic. Since PN-based guidance attempts to suppress LOS angular rate, the most effective evasion maneuver for the target is typically to increase LOS rate or reduce closing velocity. Sudden angular maneuvers in horizontal or vertical planes, maneuvers adding OOP components, and rapid unloaded extensions can alter LOS dynamics and reduce the effectiveness of PN commands. However, evasion maneuvers incur energy costs: high lateral acceleration demands accelerate energy consumption (specific excess power Ps) and can approach structural or pilot tolerance limits. From the missile’s perspective, another critical concept is the no-escape zone (NEZ), defined as the region where any maneuver the target can execute with its current performance cannot defeat the missile’s intercept capability, making escape impossible. NEZs are calculated using the missile’s maximum lateral acceleration, closing velocity profile, and seeker capabilities and serve as a critical threshold in practical engagement decisions.

Decisions under missile threat become a multi-objective optimization problem: on one hand, survivability is paramount; on the other, mission objectives—maintaining position and tactical superiority over the opponent—persist. These two goals often conflict; for example, an evasion maneuver may increase survivability but simultaneously lose tactical position or close an offensive firing window. Modern analytical approaches address this conflict within the framework of a Pareto-optimal solution set. Using multi-objective evolutionary algorithms (e.g., MOEA/D), nondominated (Pareto) strategy sets can be generated for different survivability/tactical reward weightings; operational selection involves choosing an appropriate point from this set based on mission context and the decision-maker’s risk preferences. Thus, the pilot or autonomous agent is presented not with a single “best” maneuver but with a spectrum of strategies showing the trade-offs between competing objectives.

Countermeasures operate effectively on both kinetic and electronic/perceptual levels. Flares (infrared decoys) physically generate heat sources against thermal seekers, while chaff (radar-reflective particles) and electronic countermeasures (ECM) disrupt seeker detection against radar-guided threats. These tools are effective only when used with proper timing and appropriate maneuvers; for example, flare deployment may confuse the target’s guidance for a brief period, but if followed by insufficient high-g maneuvering or lack of OOP component, the missile may reacquire the target. Additionally, helmet-mounted sights and warning systems (helmet-mounted displays, missile-approach warning systems) assist the pilot in optimizing timing, thereby enhancing countermeasure effectiveness. Mission context is decisive: for an escort platform, evasion and protection take priority; for an attack platform, more aggressive tactics may be employed, accepting the risk of entering the NEZ; maneuver choices vary depending on munition type, team tactics, and command-and-control structures.

Human Factors, Safety, and Training

Human factors, safety, and training are directly linked to operational success in dogfighting and require careful design of procedures and training to manage both pilot physiological limits and their mitigation. The high-g maneuvers characteristic of close air combat induce hemodynamic changes in the pilot’s body; the most critical consequence is visual impairment and loss of consciousness (g-induced loss of consciousness, G-LOC). Technically, the centripetal acceleration ac experienced during a turn is related to speed V and turn radius R by ac=RV2, and the load factor n relates to ac=ng. Therefore, a specific turn profile applied in the aircraft directly determines the g-load experienced by the pilot; training and operational limits are defined based on this physical relationship. Pilot g-tolerance varies individually; training, physical conditioning, and application of anti-g techniques (e.g., breathing-muscle straining maneuvers) are the main factors influencing this tolerance. In this context, safety procedures must encompass not only technical limits of the aircraft envelope but also human physiological limits.

The lookout doctrine and loss-of-sight rules provide a cognitive and tactical framework for close combat. Loss-of-sight or loss-of-contact situations require standardized procedures for both safety and conflict deconfliction; the primary purpose of these procedures is to reduce uncertainty in the battlefield, separate flight corridors, and define appropriate steps for reacquisition of visual or situational contact. Training documents implement specific checklists and routines to help pilots automate these rules; these cover both individual pilot behavior (e.g., executing a safe escape maneuver in critical situations) and team procedures (e.g., wingman position and communication). In loss-of-sight events, measures such as radio communication, pre-briefed separation profiles, and visual reacquisition points help ensure the situation is resolved in a controlled manner.

The training architecture must be designed as a multilayered structure integrating human factors and technical competence. An effective training system typically combines theoretical foundations (ground school), simulation-based practice (synthetic trainer), and actual flight application. Ground school provides foundational conceptual knowledge and interdisciplinary understanding of energy–maneuverability theory and BFM repertoire; simulation environments allow safe repetition of high-risk maneuvers, testing of emergency procedures, and evaluation of human–system interaction. The final phase, flight-based training, validates simulated skills under real flight dynamics and physical stress. This triad approach must be designed to progressively develop the student’s cognitive models, motor skills, and decision-making under stress.

The effectiveness of training programs must be evaluated not only by individual performance metrics but also by team performance, safety culture indicators, and analysis of incident/infraction records. Simulation-based assessments provide valuable data, particularly in reproducing G-LOC scenarios, sensor blind spots, and loss-of-sight conditions; these data quantitatively reveal individual pilot reaction times, situational awareness levels, and procedural compliance. Training strategies must also include pilot physiological preparation; for example, g-tolerance techniques, rest/acclimatization principles, and appropriate physical conditioning requirements must be integral components of training packages.

From Numerical Modeling to 6-DOF Simulation

Numerical modeling and 6-DOF simulation are fundamental components in dogfight research, providing both physical realism and tactical accuracy. The modeling level used must be selected according to the intended purpose; for high-level tactical analysis, 3-DOF approaches may suffice, but for controller design, sensor–effector interactions, and real-world counterparts of pilot/autonomous agent behavior, 6-DOF models are required.

Three degrees of freedom (3-DOF) models typically simulate the position and linear velocity changes of a body within a plane; they do not encompass orientation and angular dynamics. Such models provide computational efficiency for range–time analyses, rapid scanning of high-level tactical policies, and numerous Monte Carlo experiments. In contrast, six degrees of freedom (6-DOF) models simultaneously solve for the three-dimensional position and three-dimensional angular state of the body. Within the rigid body dynamics framework, 6-DOF equations take the following fundamental form:

mv=Faero+Fthrust+Fgrav,

Iω+ωx(Iω)=Maero+Mcontrol

where m is mass, v is velocity vector, I is inertia tensor, w is angular velocity, Faero and Maero are aerodynamic force and moment terms, Fthrust are thrust forces, and Mcontrol represent moments from control surfaces. Aerodynamic terms are generally functionally dependent on velocity, angular velocity, AOA (angle of attack), sideslip, and control surface positions; environmental variables such as atmospheric density, temperature, and wind turbulence directly affect aerodynamic models. Therefore, 6-DOF simulations integrate atmospheric models (standard atmosphere, local wind/turbulence models) to achieve better real-world congruence.

Control laws and effector constraints form the engineering backbone of the simulation. Low-level control is typically addressed through four-channel (pitch, roll, yaw, speed/altitude) configurations or similar reconfigured control laws; however, this abstraction must be fed with real aircraft actuator limits, bandwidth, delays, and saturation effects. In modeling control laws, delay (τ) and bandwidth (fbw) parameters can be decisive for performance; for example, increased total actuator and sensor delay in a control loop negatively affects closed-loop stability and tracking error. Therefore, controller design in simulations must be tested with as realistic effector models as possible rather than idealized behavior.

Maneuver libraries provide the executable action set for high-level decision layers. These libraries parametrically define basic BFM maneuvers (lead/pure/lag pursuit, yo-yo variants, break, unloaded extension, scissors, etc.) and specify each maneuver’s inputs and expected outputs (e.g., angular velocity profile, expected energy change). A high-level decision-maker selects from this library; the chosen maneuver is then implemented by low-level controllers in a safe and performance-compliant manner. This two-tiered structure ensures both the engineering feasibility of decisions and accelerates learning by constraining the action space for complex learning or optimization algorithms (e.g., DRL).

Scenario and adversary modeling determine the tactical accuracy of the simulation. Approaches used to model opponent behavior offer varying degrees of generalization and prediction capability. Deterministic decision trees and finite-state machines provide interpretable and engineer-controllable behavior sets; these are easily tested and debugged but have limited adaptability. Learning agents and self-play approaches enable behavior evolution; self-play, in particular, is a powerful method for adapting to opponent strategies and generating new tactics. However, the reliability of policies derived from self-play in the real world requires careful attention to sim-to-real transfer issues (model mismatch, sensor noise, transmission delays). In this context, hybrid approaches—using decision trees to guarantee basic behaviors while optimizing parameters through learning—are frequently preferred for practical acceptability.

Validation and verification determine the reliability and scientific value of simulation studies. In experimental design, criteria must include both operational success metrics (e.g., kill ratio, first-shot success, time-to-shot) and performance margins (e.g., energy error, number of NEZ violations, maximum g-values). For example, statistical distributions of closing velocity, average firing window durations, and distributions of remaining specific excess power Ps provide both tactical and engineering perspectives. Quantitative metrics can be formulated as: closing velocity Vc, time-to-shot tshot, miss distance dmiss. Studies typically derive distributions of these metrics through multiple repetitions (Monte Carlo) and evaluate significant differences using hypothesis testing, confidence intervals, or non-parametric statistical methods. Additionally, ablation studies can isolate the contribution of a control policy or sensor module; for example, a drop in kill ratio or change in energy consumption after removing a component quantitatively demonstrates that component’s impact.

To strengthen model reliability, simulation results must be cross-validated against physical tests or higher-fidelity reference models. Sensitivity analyses reveal the impact of parameter uncertainties on performance; robust optimization and conservative design principles help ensure safety under uncertainty. Result evaluation must consider not only averages but also the tail behavior of distributions (e.g., worst-case 5% scenarios); in military operations, the impact of extreme scenarios is often more critical than average performance.

Artificial Intelligence for Maneuver Decision and Autonomy

AI-based maneuver decision and autonomous dogfighting systems have transformed close air combat from a classical control problem into an AI design challenge integrating decision-making, learning, and perceptual integration under a single framework. Approaches developed in this field progress along three axes: human imitation (imitative learning), tactic–policy discovery (reinforcement learning), and sensor-fusion-based perceptual integration. Advanced systems combine these three axes to simultaneously optimize mission objectives along with safety and energy constraints.

Imitative learning (behavior cloning / imitation learning) is widely applied, particularly in Pursuit–Lock–Launch type missions, by directly learning from behavioral traces of human or expert agents. In this approach, the agent learns the policy function by observing actions corresponding to environmental observations; for example, it can learn to select lead/pure/lag based on inputs such as AOT, LOS rate, and relative energy variables. This method offers rapid convergence; however, its quality is only as good as the expert policy and can internalize flawed strategies at the same speed.

Visual perception-based drone-vs-drone scenarios diverge from classical geometry-based observation formats. In CVPR-level studies, neural network models have been used to real-time estimate from camera images whether a target poses a threat, whether to continue tracking or evade, based on video data. In this approach, neural networks simultaneously classify, predict motion direction, speed, and threat level. Such perception systems form the input layer for autonomous maneuver algorithms; that is, before transitioning to sensor-fused decision loops, the target can be labeled as “drone” or “high-threat angle.”

Early AI agent architectures were built on deterministic state–action tables, differing from today’s learning-based policies. In these systems, maneuver decisions are selected based on predefined condition blocks; for example, “if AOT > 45°, initiate OOP maneuver.” These architectures are highly favorable for interpretability and safety and form the basis of hybrid systems designed to secure learning-based methods.

Overall, the evolution of AI-based maneuver decision-making is progressing not merely toward “generating maneuvers” but toward “managing them alongside energy, perception, and safety constraints.” Future systems will likely not consist solely of a DRL or imitative model; instead, human-simulated strategies, dense sensor fusion, temporal representations, and context-optimized hybrid decision structures will work together. Thus, AI autonomy in dogfighting continues to develop as a multidisciplinary field requiring coordinated attention to technical, cognitive, and engineering layers.

Human-Centered Modeling and Cognitive Dimensions

The historical and technical framework of dogfighting is only complete when evaluated not only through aerodynamic and avionics parameters but also through the influence of human factors on decision-making. Pilot behavior is determined not only by physiological capacity but also by cognitive models, situational awareness (SA), and decision cycle speed. Therefore, modern approaches have developed human-centered mathematical models capable of numerically representing not only physical flight dynamics but also human decision-making mechanisms.

One method used to model pilot behavior is three-dimensional fuzzy logic-based performance modeling. In this approach, pilot decisions are represented not deterministically but as probabilistic behavior sets with varying “confidence levels” under uncertainty. Three-dimensional fuzzy systems are typically constructed along axes of decision speed (reaction time), perceptual latency (sensory latency), and error tolerance (error margin). Membership functions are defined for each axis; for example, linguistic labels such as “fast reaction,” “uncertain,” and “weak energy calculation” are expressed as numerical fuzzy sets. The decision output generates a continuous value (e.g., maneuver aggressiveness coefficient) based on combinations of these sets. This structure enables simulation of human performance fluctuations and testing of autonomous systems against such variations.

Cognitive decision tree approaches elevate this representation to a strategic level, aiming to reproduce human pilot decisions at a higher level of abstraction. Unlike classical rule-based “if–then” architectures, modern cognitive decision trees are hybrid structures shaped by uncertain conditions and scenario layers. For example, decision branches enabling a pilot to switch between an “aggressive risk-taking mode” and a “defensive energy-focused mode” are determined by mutually dependent factors such as perceived threat level (e.g., target aspect angle or missile threat) and energy reserve (e.g., specific excess power, Ps). Such decision structures, when combined with DRL or imitative learning models, enable the creation of both intuitive and explainable autonomous decision architectures. Imitating human-like decision pathways is considered critical, especially in flight test certification processes, for preserving “safe behavior templates.”

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AuthorBeyza Nur TürküFebruary 20, 2026 at 7:39 AM

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History and Evolution of Doctrine
Kinematic and Aerodynamic Foundations
Geometry and Basic Maneuvers (BFM)
Dogfight Under Missile Threat
Human Factors, Safety, and Training
From Numerical Modeling to 6-DOF Simulation
Artificial Intelligence for Maneuver Decision and Autonomy
Human-Centered Modeling and Cognitive Dimensions

Dogfight

History and Evolution of Doctrine

Kinematic and Aerodynamic Foundations

Geometry and Basic Maneuvers (BFM)

Dogfight Under Missile Threat

Human Factors, Safety, and Training

From Numerical Modeling to 6-DOF Simulation

Artificial Intelligence for Maneuver Decision and Autonomy

Human-Centered Modeling and Cognitive Dimensions

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