Adaptive autonomous beam control system

Abstract

The present disclosure pertains to an adaptive beam control apparatus (100) for controlling an electromagnetic beam source (1) includes an optical system (120) with an adaptive lens array (121) for modifying physical parameters of an input beam (B1). A programmable phase plate (130) applies phase modulation, while a segmented gain module (140) adjusts beam intensity. A beam emitter (150) outputs a second beam (B2) in a predetermined direction. A processor (160) controls the lens array (121), phase plate (130), gain module (140), and emitter (150) to dynamically adapt the shape, phase, and intensity of the second beam (B2).

Description

[0001] ADAPTIVE AUTONOMOUS BEAM CONTROL SYSTEM

[0002] TECHNICAL FIELD

[0003] The present invention relates to adaptive autonomous beam control systems. In particular, the present invention relates to an adaptive beam control apparatus for controlling an electromagnetic beam source, a sensor system configured to receive an electromagnetic signature from another apparatus, a method for controlling an adaptive beam control apparatus, an electromagnetic beam source and a corresponding computer-readable storage media storing instructions.

[0004] BACKGROUND

[0005] The increasing integration of directed energy weapons (DEWs) with advanced targeting systems for tactical aircraft has driven significant research and development efforts in recent years. Despite notable advancements in areas such as autonomous engagement, beam control, and adaptive aiming, current systems exhibit substantial limitations, particularly when deployed in military scenarios involving emitting beams towards multiple, fast-moving targets. Existing approaches have largely developed autonomous engagement and electromagnetic beam control technologies independently, leading to inefficiencies in synchronization and operational reliability. Consequently, there remains a pressing need for real-time beam control solutions that can dynamically adapt to complex and rapidly changing engagement conditions.

[0006] For instance, beam control technologies are known as systems designed to optimize the direction, focus, and stability of laser beams for precision targeting and engagement. Specifically, it is known how to employ adaptive optics to compensate for distortions caused by atmospheric turbulence and other environmental factors, and to utilize beam steering mechanisms for dynamic targeting.

[0007] However, one or more drawbacks of these aspects are identified as the inability of current systems to maintain adaptive responsiveness to rapidly changing environmental conditions or target behaviors during real-time operation. Additionally, the lack of integration with decision -making frameworks limits the synchronization and prioritization of beam parameters, leading to inefficiencies in scenarios requiring simultaneous engagement of multiple dynamic threats.

[0008] In a related manner, Artificial Intelligence (Al)-enhanced autonomous targeting and methodologies for Al-driven multi-target tracking are known from the state of the art, highlighting constraints in real-time adaptation and dynamic beam integration.

[0009] However, one or more drawbacks of these aspects are identified as the inability to achieve seamless real-time adaptation in decision-making processes required for highly dynamic threat scenarios. Current Al-driven systems often fail to incorporate adaptive beam steering or effectively merge disparate sensor inputs to continuously refine engagement parameters. Thelatency inherent in Al processing limits the systems’ responsiveness, resulting in delays in updating target information and suboptimal engagement performance. This limitation is particularly critical in swarm scenarios, where large numbers of fast-moving and agile targets, such as drones, demand simultaneous and precise engagement decisions, often exceeding the current capabilities of Al-driven systems.

[0010] Still in a related manner, the monitoring and targeting of threats is known from the prior art, such as systems utilizing radar and electro-optical sensors capable of tracking multiple targets.

[0011] However, one or more drawbacks of these aspects are identified as the failure to integrate multi-target tracking data with adaptive beam control and Al-driven decision-making in real-time. This disconnection leads to inefficiencies, with current systems often addressing threats sequentially rather than concurrently engaging multiple high-priority targets. Furthermore, the lack of synchronization between autonomous threat prioritization algorithms and beam parameter adjustments results in suboptimal engagement strategies. The inability to dynamically modify beam attributes, such as intensity, focus, and direction, in response to evolving scenarios significantly hampers the effectiveness of these systems, particularly in complex, high-stakes environments involving numerous agile and rapidly maneuvering threats.

[0012] In light of the above, there is a necessity for systems and methods enabling real-time beam control while also tackling one or more of the previously mentioned drawbacks.

[0013] SUMMARY

[0014] Examples of the present disclosure seek to address or at least alleviate the above problems.

[0015] In a first aspect, there is provided an adaptive beam control apparatus for controlling an electromagnetic beam source, comprising:

[0016] an optical system including an adaptive lens array operatively connected to said electromagnetic beam source, said adaptive lens array being configured to receive a first electromagnetic beam, called input beam, from the electromagnetic beam source, the adaptive lens array being further configured to change at least one physical parameter of the received input beam,

[0017] a programmable phase plate optically connected to the optical system, the programmable phase plate being configured to apply a phase modulation to the input beam,

[0018] at least one segmented gain module optically connected to the programmable phase plate, said at least one segmented gain module being configured to apply an intensity change of the input beam,

[0019] a beam emitter optically connected to the at least one segmented gain module, the beam emitter being configured to output, in a first predetermined direction, calledoutput direction, a second electromagnetic beam, called output beam, based on the input beam received from the at least one segmented gain module, and

[0020] a processor operatively connected to the optical system, to the programmable phase plate, to the segmented modulated gain medium, and to the beam emitter, the processor being configured to control the at least one physical parameter change by the adaptive lens array, the applied phase modulation by the programmable phase plate, the intensity change by the at least one segmented gain module, and the emission, by the beam emitter, of the output beam in the output direction.

[0021] In a possible embodiment of this aspect, the adaptive beam control apparatus further comprises at least one of:

[0022] a cooling system thermally connected to the processor, to the optical system, and to the at least one segmented gain module, said cooling system comprising a phasechange cooling module, said phase-change cooling module comprising a phasechange material, PCM, said PCM being configured to absorb and store thermal energy when undergoing a phase transition from a solid state to a liquid state as a result of heat provided to the phase-change cooling module, the PCM being further configured to release the stored thermal energy when undergoing another phase transition from the liquid state to the solid state, and / or

[0023] an energy management unit operatively connected to the processor, to the optical system, and to the at least one segmented gain module, said energy management unit being configured to dynamically allocate power distribution to the processor, to the optical system, and to the at least one segmented gain module depending on the real-time operational data received from the processor, said real-time operational data including at least one of a first power demand determined by the processor based on the at least one physical parameter change of the input beam performed by the adaptive lens array of the optical system, a second power demand determined by the processor based on the phase change applied to the input beam by the programmable phase plate, a third power demand determined by the processor based on the intensity change of the input beam performed by the at least one segmented gain module, and a power variation resulting from an operation of the processor to coordinate the optical system, the programmable phase plate, the at least one segmented gain module, and the beam emitter.

[0024] In a possible embodiment of this aspect, the adaptive lens array of the optical system comprises a plurality of electrically adjustable lenses, at least one of said electrically adjustable lenses being made of electroactive polymer, EAP, embedded glass, the electrically adjustablelenses being distributed in a multi-layered configuration, each lens of the adaptive lens array being adapted for dynamically altering a refractive index and / or a divergence of the input beam in response to a corresponding control signal of the processor.

[0025] In a possible embodiment of this aspect, the programmable phase plate is configured to induce a phase modulation to a profile of the input beam, the processor being configured to control the programmable phase plate to provide the profile of the input beam, with the provided profile being selected among a flat-top beam profile, a Gaussian beam profile, and a Bessel beam profile.

[0026] In a possible embodiment of this aspect, the adaptive beam control apparatus further comprises a sensor system configured to receive an electromagnetic signature from another apparatus, said other apparatus being distinct from the adaptive beam control apparatus and being called target, the sensor system comprising:

[0027] an infrared sensor module configured to detect a thermal radiation emitted by said target, said infrared sensor module being configured to operate at multiple wavelengths including a mid-wave infrared, MWIR, wavelength and / or a long-wave infrared, LWIR, wavelength,

[0028] an optical sensor module configured to detect a visual signal of the target, said optical sensor module being configured to operate in the visible spectrum and / or in the nearinfrared, NIR, spectrum, and / or

[0029] an electromagnetic, EM, sensor module configured to detect radio frequency, RF, emissions and / or EM signals emitted by the target, the EM sensor module optionally comprising a frequency spectrum analyzer.

[0030] In another aspect, there is provided a method for controlling an adaptive beam control apparatus, the method comprising the steps of:

[0031] receiving, by an optical system of said adaptive beam control apparatus, said optical system including an adaptive lens array, a first electromagnetic beam, called input beam, from an electromagnetic beam source,

[0032] modifying the received input beam, the modification comprising

[0033] o changing, with the adaptive lens array, at least one physical parameter of the received input beam, the adaptive lens array being configured to receive the input beam and change said at least one physical parameter including a focus or a divergence,

[0034] o applying, with a programmable phase plate of the adaptive beam control apparatus, said programmable phase plate being optically connected to the optical system, a phase modulation of the input beam,o changing, with at least one segmented gain module of the adaptive beam control apparatus, said at least one segmented gain module being optically connected to the programmable phase plate, an intensity of the input beam, the segmented gain module being configured to selectively amplify or attenuate an intensity of the input beam,

[0035] emitting, by a beam emitter of the adaptive beam control apparatus, said beam emitter being optically connected to the at least one segmented gain module, in a first predetermined direction, called output direction, a second electromagnetic beam, called output beam, based on the modified input beam, wherein the processor is operatively connected to the adaptive lens array, to the programmable phase plate, and to the at least one segmented gain module, wherein, using control signals, the processor is configured to dynamically control the changes of the at least one physical parameter by the adaptive lens array, to dynamically control the changes of the phase modulation by the programmable phase plate, and to dynamically control the changes of the intensity by the at least one segmented gain module, producing the output beam, and wherein the processor is further configured to coordinate real-time operational data received from the optical system, the programmable phase plate, the segmented gain module, and the beam emitter to optimize the modification and emission of the output beam.

[0036] In a possible embodiment of this aspect, the method further comprises the steps of: receiving, by the processor, real-time sensor data from a sensor system, said sensor system being configured to receive an electromagnetic signature from another apparatus, called target, the sensor system comprising

[0037] o an infrared sensor module configured to detect a thermal radiation emitted by said target, said infrared sensor module being configured to operate at multiple wavelengths including a mid-wave infrared, MWIR, wavelength and / or a longwave infrared, LWIR, wavelength,

[0038] o an optical sensor module configured to detect a visual signal of the target, said optical sensor module being configured to operate in the visible spectrum and / or in the near-infrared, NIR, spectrum, and / or

[0039] o an electromagnetic, EM, sensor module configured to detect radio frequency, RF, emissions and / or EM signals emitted by the target, the EM sensor module optionally comprising a frequency spectrum analyzer,

[0040] the method further comprising the steps, after receiving said real-time sensor data, ofanalyzing, with at least one Al decision-making engine operatively connected to the processor, the received real-time sensor data to prioritize one or more targets based on operational attributes of the targets,

[0041] generating, with the at least one Al decision-making engine, control signals configured to be subsequently sent to the processor, said control signals being based on the analyzed real-time sensor data, the control signals being configured to dynamically adjust, with the processor:

[0042] o the optical system to modify at least one physical parameter of the input beam, o the programmable phase plate to modify the phase distribution of the input beam, and

[0043] o the at least one segmented gain module to control the intensity of the input beam.

[0044] In a possible embodiment of this aspect, the method further comprises the steps of dynamically optimizing, with the processor in conjunction with at least one Al decision-making engine, configurations of the processor to dynamically control:

[0045] the changes of the at least one physical parameter by the adaptive lens array, the changes of the phase modulation by the programmable phase plate, the changes of the intensity by the at least one segmented gain module, producing the output beam,

[0046] the dynamical optimization comprising a sub-step of employing said at least one Al decision-making engine to refine said changes of the at least one physical parameter of the phase modulation and of the intensity based on a Bayesian inference network, a contextual weighting, and / or a self-enhancing design, the dynamically optimized configurations of the processor being used during the modification step of the received input beam.

[0047] In a possible embodiment of this aspect, the method further comprises the steps of dynamically optimizing, with the processor in conjunction with at least one Al decision-making engine, configurations of the processor to dynamically control:

[0048] the changes of the at least one physical parameter by the adaptive lens array, the changes of the phase modulation by the programmable phase plate, the changes of the intensity by the at least one segmented gain module, producing the output beam,

[0049] the dynamical optimization comprising a sub-step of applying a recurrent neural network, RNN, trained on historical datasets comprising trajectories and environmental conditions associated with a target, to forecast the most probable future path of said target, the dynamically optimized configurations of the processor being used during the modification step of the received input beam.In another aspect, there is provided at least one computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method for controlling an adaptive beam control apparatus, the method comprising:

[0050] receiving, by an optical system of said adaptive beam control apparatus, said optical system including an adaptive lens array, a first electromagnetic beam, called input beam, from an electromagnetic beam source,

[0051] modifying the received input beam, the modification comprising

[0052] o changing, with the adaptive lens array, at least one physical parameter of the received input beam, the adaptive lens array being configured to receive the input beam and change said at least one physical parameter including a focus or a divergence,

[0053] o applying, with a programmable phase plate of the adaptive beam control apparatus, said programmable phase plate being optically connected to the optical system, a phase modulation of the input beam,

[0054] o changing, with at least one segmented gain module of the adaptive beam control apparatus, said at least one segmented gain module being optically connected to the programmable phase plate, an intensity of the input beam, the segmented gain module being configured to selectively amplify or attenuate an intensity of the input beam,

[0055] o emitting, by a beam emitter of the adaptive beam control apparatus, said beam emitter being optically connected to the at least one segmented gain module, in a first predetermined direction, called output direction, a second electromagnetic beam, called output beam, based on the modified input beam, wherein the at least one processor is operatively connected to the adaptive lens array, to the programmable phase plate, and to the at least one segmented gain module,

[0056] wherein, using control signals, the at least one processor is configured to dynamically control the changes of the at least one physical parameter by the adaptive lens array, the phase modulation by the programmable phase plate, and the intensity by the at least one segmented gain module, producing the output beam, and

[0057] wherein the at least one processor is further configured to coordinate real-time operational data received from the optical system, the programmable phase plate, the segmented gain module, and the beam emitter to optimize the modification and emission of the output beam.

[0058] BRIEF DESCRIPTION OF DRAWINGSExamples of the disclosure will now be described by way of example only with reference to the accompanying drawings, in which like references refer to like parts, and in which:

[0059] -Figure 1 is a diagram of an adaptive beam control apparatus for controlling an electromagnetic beam according to an example,

[0060] -Figure 2 is a diagram of a sensor system configured to receive an electromagnetic signature from an apparatus according to an example,

[0061] -Figure 3 is another diagram of an adaptive beam control apparatus for controlling an electromagnetic beam according to an example,

[0062] -Figure 4 is a block diagram of a power distribution and cooling system according to an example,

[0063] -Figure 5 is a block diagram of a method for controlling an electromagnetic beam source with an adaptive beam control apparatus according to an example. Unless otherwise indicated, common or analogous elements in the figures are denoted by the same reference numerals and exhibit identical or analogous features, so that common or analogous elements are generally not described again for the sake of clarity and conciseness.

[0064] DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0065] An adaptive beam control apparatus, a sensor system, a method for controlling an electromagnetic beam source and a corresponding computer-readable storage media storing instructions are disclosed. In the following description, a number of specific details are presented in order to provide a thorough understanding of the examples of the disclosure. It will be apparent however to a person skilled in the art that these specific details need not be employed in order to practice the examples of the disclosure. Conversely, specific details known to the person skilled in the art are omitted for the purposes of clarity in presenting the examples.

[0066] Figure 1 illustrates an adaptive beam control apparatus for controlling an electromagnetic beam according to an example.

[0067] Specifically, the adaptive beam control apparatus 100 illustrated in Figure 1 provides a system for precise control of an electromagnetic beam source 1. As illustrated, the apparatus 100 includes an optical system 120, which is configured with an adaptive lens array 121. This lens array is designed to receive an input beam B1 from the electromagnetic source 1 and modify at least one physical parameter of the beam, such as focus or divergence, while potentially excluding phase adjustments.

[0068] Herein, the focus of a beam is defined as the point or region where the beam converges to its smallest cross-sectional area, achieving maximum intensity, while the divergence of a beam is defined as the angular measure of the beam's spread as it propagates away from its origin.The lens array 121 incorporates variable electrically adjustable lenses arranged to facilitate real-time shape reconfiguration and intricate beam shaping. This ensures adaptability in focus modes and compensates for system impairments through self-healing functionality.

[0069] In an embodiment, the lens array 121 enables an Adaptive Optics System, called ABCS, with Variable Lens Arrays, called VLA. In this embodiment, the lens array 121 consists of multiple layers of adjustable lenses capable of dynamically altering their curvature in response to electrical signals. This arrangement permits modifications to beam shape, focus, and divergence, providing enhanced adaptability compared to traditional systems employing deformable mirrors.

[0070] The multi-layered lens structure enables fine-tuned control of beam properties, supporting real-time transitions between narrow and wide focus modes, essential for addressing various operational scenarios. Furthermore, the self-healing capability of the system ensures that, in the event of obstruction or impairment to a lens segment, the remaining lenses dynamically adjust their curvature and arrangement to maintain beam quality and directionality.

[0071] The programmable phase plate 130 is optically connected to the optical system 120. It applies phase modulation to the input beam B1, enabling precise phase adjustments to achieve desired beam profiles such as flat-top, Gaussian, or Bessel configurations. As will be described hereafter, a flat-top configuration is a beam profile characterized by a uniform intensity distribution across its cross-section, a Gaussian configuration is one where the intensity distribution follows a bell-shaped curve, decreasing gradually from the center, while a Bessel configuration consists of concentric rings with high-intensity peaks, resulting from non-diffracting wave properties.

[0072] As illustrated, the phase plate 130, which is programmable or controlled by the processor 160, operates via dynamically adjustable settings informed by control signals provided by said processor 160. As illustrated, the processor 160 is operatively connected to the optical system 120, the phase plate 130, the segmented gain module 140, and the beam emitter 150. This phase control permits real-time alterations in the beam's morphology and intensity distribution, enhancing adaptability and performance in varying operational environments.

[0073] The programmable phase plate 130 is preferably configured to induce phase shifts in the laser beam B1 by modifying the spatial distribution of the phase across the beam cross-section. This allows the creation of custom beam profiles to suit specific applications.

[0074] For instance, the phase plate can adjust the beam's intensity distribution to produce a flat-top beam for uniform energy delivery or a Gaussian beam for high precision in targeting. Bessel beams, which are advantageous for non-diffracting propagation, can also be generated by appropriately modulating the phase. By leveraging its programmability, the phase plate 130 facilitates meticulous control over the beam's spatial and intensity features, ensuring compatibility with diverse operational requirements. These adjustments can be dynamically managed by the processor 160 to achieve the desired output configuration in real time.A segmented gain module 140 is positioned optically downstream of the programmable phase plate 130. This module is configured to implement intensity adjustments, including amplification or attenuation of specific beam segments.

[0075] In a possible embodiment, the segmented modulated gain medium 140 comprises a plurality of independently controllable segments, each segment configured to adaptively modify local beam intensity based on real-time demands. In this case, the processor 160 can control the segmented modulated gain medium 140 to selectively amplify beam intensity for heavily shielded targets and reduce beam intensity for softer targets, thereby optimizing energy consumption and improving system efficiency.

[0076] The segmentation allows for localized modifications to beam intensity by activating or deactivating particular sections, which optimizes energy efficiency and improves intensity control responsiveness. The gain module is controlled through modulation inputs from the processor 160, providing tailored adjustments to meet operational requirements.

[0077] By directing the segmented modulated gain medium 140 to adapt beam intensity based on the shielding characteristics of the target, the processor ensures the system balances energy consumption while maintaining operational effectiveness, further enhancing efficiency in beam control.

[0078] By selectively activating or deactivating these sections, one can precisely control the intensity distribution across the beam, reducing power consumption and enhancing the system's efficacy. This enables the segmented gain module 140 to adapt dynamically to operational requirements, concentrating energy only where needed and avoiding excess power usage, which is particularly critical in energy-limited scenarios.

[0079] This segmentation also ensures that energy is directed only where required, enabling optimal performance in applications demanding fine-grained intensity adjustments or rapid dynamic response to varying conditions.

[0080] The output beam B2, modified in intensity and phase, is directed by a beam emitter 150 that is connected optically to the gain module 140. This emitter ensures the beam is output in a predetermined direction. The processor 160 serves as the central control unit, coordinating operations of the optical system 120, programmable phase plate 130, gain module 140, and beam emitter 150. It dynamically adjusts beam parameters through integrated feedback mechanisms to optimize performance. The processor is assisted by subcomponents such as power sources and calibration modules to maintain accuracy and operational efficiency.

[0081] The system enables significant improvement in terms of adaptive optics, beam shaping, and intensity control, indicating hypothetical performance improvements of 50%, 60%, and 40%, respectively, in terms of shape adaptability, beam shaping accuracy, and intensity control responsiveness for the emitted beam.In a possible embodiment, the optical system 120 of the adaptive beam control apparatus 100 comprises a quantum entanglement module 11, which can be configured to generate entangled photon pairs.

[0082] Herein, entangled photon pairs refer to pairs of photons whose quantum states are interdependent, such that the state of one photon can instantaneously influence the state of the other, even when spatially separated. This phenomenon, known as quantum entanglement, can be described mathematically by quantum mechanical principles and can be obtained using nonlinear optical processes such as spontaneous parametric down-conversion, SPDC, or four-wave mixing.

[0083] In principle, entangled photon pairs can enable the adaptive beam control apparatus 100 to significantly enhance the detection of low-observable targets by possibly improving the signal-to-noise ratio, SNR. This technical effect can arise from the ability of entangled photons to provide correlated signal and idler beams, which can be used to detect targets with higher precision by reducing background noise and interference.

[0084] For producing entangled photon pairs, the adaptive beam control apparatus 100 can optionally employ a periodically poled lithium niobate, PPLN, crystal for SPDC or a photonic crystal-based four-wave mixing setup. Quantum states of an electromagnetic beam can be prepared and manipulated through precise control of photon pair generation parameters, such as phase matching conditions and pump beam intensity, which can ensure reliable and reproducible quantum entanglement.

[0085] Optionally, the quantum entanglement module 11 can operate across many frequencies, enabling to traverse diverse air conditions and identify stealth devices that utilize radar-absorbing materials. The multi-frequency capability can yield multidimensional data, thereby possibly improving target recognition accuracy by providing a richer data set for analysis.

[0086] In a possible embodiment, the programmable phase plate 130 and the segmented gain module 140 can be adapted to manipulate quantum states of the electromagnetic beam B1 , which can allow phase alterations and intensity modulations that preserve the entanglement properties of the photon pairs.

[0087] Additionally, the processor 160 can optionally be configured to enhance detection capabilities of low-observable targets by improving the SNR of the adapted electromagnetic beam B2.

[0088] Examples of quantum illumination techniques include entanglement-assisted target detection, which can use correlated signal and idler photon pairs where the signal photons can interact with the target while the idler photons can remain in a reference channel, possibly enabling enhanced detection through coincidence counting; quantum ghost imaging, which can employ entangled photon pairs to reconstruct target images with fewer photons, possibly improving detection sensitivity under low-light conditions; and quantum homodyne detection,which can enhance the SNR and reduce false-positive rates in target detection by measuring quantum states with high precision.

[0089] In a possible embodiment, entangled photon pairs can optionally be directed through the adaptive lens array 121 and manipulated by the programmable phase plate 130 to apply precise phase alterations that can optimize the detection and identification of targets. Optionally, the segmented gain module 140 can be configured to amplify the quantum signal while possibly preserving the entanglement properties of the photon pairs. The beam emitter 150 can output the adapted electromagnetic beam B2, possibly encoded with such quantum-enhanced properties, in a predetermined direction.

[0090] In one or any combination of the aforementioned embodiments, the processor 160 can be configured to control the operation of at least one of the adaptive lens arrays 121, the programmable phase plate, and the segmented gain module. When quantum illumination principles are possibly used to enhance the SNR, this can enable the detection of targets with higher accuracy, even if they are more difficult to observe optically or electromagnetically.

[0091] Also illustrated in the figure, in an optional embodiment, the adaptive beam control apparatus 100 can include a cooling system 170 thermally connected to the processor 160, to the optical system 120, and to the segmented gain module 140.

[0092] In an embodiment, the thermal connections of the cooling system to the processor 160, to the optical system 120, and the segmented gain module 140 can be carried through liquid-metal cooling channels traversing the high-temperature regions of the apparatus, including the optical system 120, the segmented gain module 140, and the beam emitter 150, the liquid-metal cooling channels being configured to transfer heat efficiently to external radiators, with smart flow control mechanisms modulating coolant flow based on real-time thermal conditions.

[0093] In addition, the cooling system 170 can comprise a phase-change cooling module 170a, incorporating a phase-change material, PCM, capable of absorbing and storing thermal energy during a phase transition from solid to liquid due to heat generated by the apparatus. The PCM can release the stored energy during a reverse phase transition from liquid to solid, providing a passive thermal management. This phase-change cooling mechanism can optimize thermal dissipation cycles, to manage heat absorption and release rates effectively.

[0094] In a possible embodiment, one or more PCMs can store thermal energy from high-heat components during peak operational periods and release the stored heat during reduced thermal loads, thereby maintaining operational stability and prolonged operation of all components connected to the cooling system 170.

[0095] In possible embodiments, the PCMs can be enhanced with carbon nanofibers. This material is lightweight, exhibits enhanced heat absorption, and has an adaptable melting point. Its innovative feature lies in its ability to retain heat effectively during cooling cycles, with an approximate weight of 125 grams.In a possible embodiment, the cooling system 170 can also employ thermoelectric cooling modules, TECs, positioned near high-temperature components to enable localized cooling or heating depending on the electrical current polarity.

[0096] For instance, various TECs can be configured to dynamically regulate local temperatures by providing precise cooling or heating based on the polarity of the applied electrical current.

[0097] In possible embodiments, the TECs are composed of bismuth telluride (Bi2Te3) nanostructured materials. These modules exhibit high thermoelectric efficiency, compact size, and rapid temperature modulation. The innovative aspect of these modules is their ability to achieve high efficiency and fast thermal responses, with an approximate weight of 50 grams. The combination of these materials and features provides efficient and adaptive cooling solutions for complex systems.

[0098] In a possible embodiment, the cooling system 170 can further include liquid-metal cooling channels utilizing gallium-indium alloys encased in silicon nanotubes, offering high thermal conductivity and flexibility. These channels can rapidly dissipate heat from beam optics and power nodes to external radiators or heat exchangers.

[0099] In a possible embodiment, the cooling system 170 can further be operated in combination with a decision-making engine, e.g., a decision-making engine operated with Machine Learning, ML, or Artificial Intelligence, AL Specifically, the cooling system 170 can be operated with an Al-driven thermal regulation engine operatively connected to the cooling system 170 and / or to the processor 160.

[0100] Advantageously, such an Al-driven thermal regulation engine can be configured to analyze real-time thermal data from sensors and to control the phase-change cycle, liquid-metal flow rates, and TEC power input to ensure efficient thermal management during dynamic operational conditions.

[0101] The integration of the cooling system 170 with phase-change cooling, liquid-metal channels, and TECs, combined or not with Al-driven thermal regulation, ensures continuous operation of the adaptive beam control apparatus 100 by preventing overheating and maintaining component stability under high-demand scenarios.

[0102] In a possible embodiment, the addition of smart flow control valves can regulate the coolant flow rate based on thermal output, increasing flow for heat-intensive components such as the segmented gain module 140 while reducing flow for less demanding regions. The dynamic modulation of coolant flow can maintain operational stability by prioritizing cooling where needed most. Such integration allows the adaptive beam control system to handle elevated thermal loads during beam modulation and emission processes.

[0103] Also illustrated in the figure, in an optional embodiment, the adaptive beam control apparatus 100 can include an energy management unit 180 operatively connected to the processor 160 and to the optical system 120. The energy management unit 180 can dynamically allocate power to the processor 160, optical system 120, and segmented gain module 140 basedon real-time operational data provided by the processor 160. Said data can include a first power demand determined by the processor 160 based on physical parameter changes performed by the adaptive lens array 121, a second power demand for phase changes applied by the programmable phase plate 130, a third power demand for intensity changes performed by the segmented gain module 140, and a power variation due to processor coordination of these subsystems.

[0104] Advantageously, the energy management unit 180 can adjust power distribution in real time to meet these demands while optimizing system efficiency and preventing thermal overload. A decentralized power distribution network including localized power nodes, each node being configured to autonomously regulate power distribution to connected components based on realtime operational requirements and data from integrated power sensors, ensures precise and localized power control.

[0105] Moreover, advantageously, predictive algorithms can be relied upon to forecast power surges and proactively adjust power output to prevent overload or inefficient energy consumption by the energy management unit 180, enhancing reliability and operational efficiency. Adaptive load-shedding modules configured to temporarily reduce power to non-essential components during high-energy-demand periods, prioritizing energy allocation to critical systems, including the segmented gain module 140 and the optical system 120, further optimize energy distribution.

[0106] Furthermore, advantageously, redundancy and fail-safe features configured to autonomously redirect power through alternative pathways in response to component malfunctions or unforeseen energy surges ensure continuous operation of the adaptive beam control apparatus 100. These features enable the energy management unit 180 to maintain uninterrupted operation and efficient energy utilization, preventing system overloads even under dynamically changing operational requirements.

[0107] Also illustrated in the figure, in an optional embodiment, the programmable phase plate 130 in the adaptive beam control apparatus 100 is configured to induce phase modulation in the input beam B1, enabling dynamic adjustments to the beam’s profile. This component is operatively connected to the processor 160, which can be configured to control the programmable phase plate 130 in real time based on feedback.

[0108] Advantageously, the processor 160 adjusts the phase distribution of the input beam B1 to optimize its morphology for emission by the beam emitter 150 into the output beam B2.

[0109] In various embodiments compatible with the previously described embodiments, the programmable phase plate 130 can provide the beam profile in configurations selected among a flat-top profile, a Gaussian profile, or a Bessel profile. Each profile serves a distinct purpose: a flat-top beam is configured to achieve uniform energy distribution across a target surface, a Gaussian beam concentrates energy at a central point for focused targeting, and a Bessel beam maintains focus integrity over extended distances while penetrating atmospheric disturbances.Advantageously, the programmable phase plate 130, under the control of the processor 160, enables dynamic beam profile adjustments to adapt to varying engagement scenarios. The real-time control allows the apparatus 100 to alternate rapidly among beam profiles.

[0110] For example, when or if uniform energy distribution is required across a broad target, the processor 160 selects the flat-top beam profile. Alternatively, when high precision is necessary, such as focusing on a specific point, the Gaussian beam profile is applied. In environments with atmospheric disturbances or the need for long-distance focus, the processor 160 directs the programmable phase plate 130 to configure a Bessel beam profile. The versatility of this component enhances the adaptability and precision of the adaptive beam control apparatus 100.

[0111] In a possible embodiment, the programmable phase plate 130 is configured to manipulate the spatial phase distribution of the input beam B1 through precisely controlled adjustments of the phase modulation pattern. This manipulation allows for tailored shaping of the beam's intensity distribution and morphology. For instance, a flat-top beam profile provides even energy delivery across the target area, making it ideal for applications requiring consistent coverage. The Gaussian beam profile focuses energy at the center, ensuring concentrated intensity for high-energy delivery or precision tasks. The Bessel beam profile generates a non-diffracting beam that is particularly effective in maintaining energy density over long distances and mitigating the effects of atmospheric interference. These phase modulation capabilities are achieved through the feedback-driven control of the processor 160, ensuring responsive and reliable performance.

[0112] The integration of the programmable phase plate 130 with the processor 160 and the beam emitter 150 ensures seamless operation of the adaptive beam control apparatus 100. The processor 160 coordinates the phase adjustments of the programmable phase plate 130 with the intensity modulation by the segmented gain module 140 and the focusing adjustments of the adaptive lens array 121.

[0113] Together, these components deliver a finely tuned output beam B2 tailored to the specific demands of the operational environment. The programmable phase plate 130, by enabling rapid and precise phase modulation, plays a key role in achieving this level of control, ensuring that the beam emitter 150 delivers the desired output in terms of profile, intensity, and focus.

[0114] Figure 2 illustrates a sensor system configured to receive an electromagnetic signature from an apparatus according to an example. Specifically, signals emitted from a target 200 are detected by various possible components of a sensor system 10 that the adaptive beam control apparatus 100 can comprise.

[0115] As illustrated, in possible embodiments, the sensor system 10 includes several modules operatively connected to detect, process, and integrate data related to the target 200.

[0116] Optionally, a quantum entanglement module 11 is provided to enhance sensor data fusion and is operatively linked to at least one localized fusion node 15 for advanced signal processing.In an embodiment, the sensor system 10 comprises an infrared sensor module 12 configured to detect thermal radiation, for instance hear emitted by the target 200. The infrared sensor module 12 can be operating across multiple wavelengths such as mid-wave infrared, called MWIR, and long-wave infrared, called LWIR, to identify heat signatures under challenging visibility conditions like fog, smoke, or nighttime.

[0117] Herein, MWIR wavelengths are widely considered to range from approximately 3 to 5 micrometers (pm), and LWIR wavelengths typically range from approximately 8 to 14 pm, offering complementary spectral coverage for detecting thermal signatures under diverse environmental conditions.

[0118] In an embodiment, the sensor system 10 also comprises an optical sensor module 13, configured to detect visual signals, for instance an image of the target 200. The optical sensor module 13 can typically be functioning within the visible and near-infrared, NIR, spectrums and optionally incorporates real-time image stabilization and adaptive filtering for mitigating motion blur and atmospheric disturbances.

[0119] Herein, the visible spectrum is generally defined as spanning wavelengths from approximately 400 to 700 nanometers (nm), while the NIR spectrum extends from approximately 700 nm to 2500 nm. This spectral range enables the optical sensor to detect detailed visual signals and weak infrared reflections, enhancing target identification and tracking capabilities.

[0120] The optical sensor module 13 optionally includes a real-time image stabilization module and / or an adaptive filtering module. These additional features can mitigate motion blur caused by target movement or platform vibrations and reduce atmospheric disturbances such as haze or turbulence in the visual signal detected by the optical sensor module. Such enhancements improve the quality and reliability of the optical data, ensuring robust target recognition and tracking in dynamic operational environments.

[0121] In an embodiment, the sensor system 10 further comprises an electromagnetic, EM, sensor module 14 which is configured to detect radio frequency, RF, emissions and other electromagnetic signals that can be emitted by the target 200. The EM sensor module 14 can further be configured to distinguish target-specific emissions from background noise using spectrum analysis and optionally includes a frequency spectrum analyzer 14a for more precise signal classification.

[0122] In an embodiment, data collected by the sensor modules 12, 13, and 14 is processed initially by localized fusion nodes 15 comprised in the sensor system 10, which are configured to perform data integration and preliminary analysis. The localized fusion nodes 10 can combine data from multiple sensor modalities, including thermal, visual, and electromagnetic, to create a comprehensive dataset for subsequent processing.

[0123] In an embodiment, the sensor system 10 also comprises a centralized fusion hub 16, which is operatively connected to the localized fusion nodes 15, and configured to consolidates the data processed by said localized fusion nodes 15. The centralized fusion hub 16 serves as acentral processing unit to integrate and combined thermal, visual, and electromagnetic data into a unified profile. On this basis, the fusion hub 16 is configured to generate consolidated datasets that provide a detailed characterization of the target 200, including its vulnerabilities, motion patterns, and other critical features, based on cross-referenced sensor inputs.

[0124] Thus, this provides a multi-sensor fusion array configured to collect and integrate data resulting from signals collected from a target, the data being provided to the processor to enhance beam adjustment based on real-time operational requirements for emitting the beam B2 more efficiently towards said target.

[0125] In a possible embodiment, the sensor system 10 optionally comprises an Al decisionmaking engine 17, which is operatively connected to the centralized fusion hub 16 and to the processor 160. As an alternative, if the Al decision-making engine 17 is absent from the sensor system 10, the processor 160 can be operatively connected to the fusion hub 16, to the localized fusion nodes 10 and / or to any of the sensor modules 12, 13, and 14.

[0126] When present, the Al decision-making engine 17 is configured to process any received data or consolidated data and to generate actionable control signals for beam adaptation by the processor 160.

[0127] Advantageously, the Al decision-making engine evaluates the data in real time, which can be used for identifying vulnerabilities of the target 200 and uses this information to direct the processor 160 to adjust the intensity, shape, and focus of the input beam B1 to obtain an output beam B2 enabling optimized engagement. For instance, the processor 160 dynamically integrates data from the sensor modules 12, 13, and 14 to adapt the beam control parameters and enhance the ability of the whole system to hit the target 200 effectively with the output beam B2.

[0128] Infrared data facilitates the generation of a thermal map that highlights areas of interest on the target. The optical sensor module provides visual confirmation and details for real-time tracking, using multi-spectral imaging for improved recognition. The EM sensor module detects RF emissions to locate electronic components, distinguishing them from noise using spectrum analysis. The fusion of these datasets in the centralized hub 16 and their evaluation by the Al decision-making engine 17 enable precise control of beam parameters for reliable and adaptable operation.

[0129] In various embodiments, the processor 160 is configured to generate a comprehensive target profile using integrated sensor data and thermal mapping based on data provided by the sensors of the sensor system 10. The target profile enables precise beam adjustments that focus on the target’s most vulnerable regions, improving reliability and mitigating countermeasures. The system leverages data from the infrared sensor module 12 to identify heat-emitting areas, while visual information from the optical sensor module 13 provides high-resolution imaging for target classification. Electromagnetic emissions detected by the EM sensor module 14 contribute to identifying electronic systems or active countermeasures on the target.Figure 3 illustrates a summary representation of the adaptive beam control apparatus in a possible embodiment of the previously described features, providing a synthesized view of the relationships between the various described modules, for controlling an electromagnetic beam according to an example,

[0130] Specifically, it is illustrated an adaptive beam control apparatus 100 comprising the processor 160, and having components operatively connected to said processor 160, which is configured for adapting an electromagnetic beam.

[0131] The optical system 120 comprises the adaptive lens array 121 configured to receive and adjust the parameters of the input beam B1 , such as focus and divergence, said adjustment being based on real-time instructions from the processor 160. The adaptive lens array121 is integrated into the optical system 120, which serves as the primary beam-shaping mechanism, utilizing dynamic adjustments to modify the input beam's characteristics before further processing.

[0132] A programmable phase plate 130 is optically connected to the optical system 120 and operatively connected to the processor 160. The programmable phase plate 130 is also configured to apply phase modulation to the beam, allowing dynamic alterations for the beam. The processor 160 manages this phase modulation to ensure real-time adjustments of the beam profile to meet operational requirements.

[0133] A segmented gain module 140, positioned downstream of the programmable phase plate 130, is configured to adjust the intensity of the beam selectively. This module 140 preferably includes a segmented structure, enabling independent control of beam intensity across localized regions, optimizing energy distribution for specific operational scenarios.

[0134] A cooling system 170 is thermally connected to key components, including the optical system 120, the segmented gain module 140, and / or the processor 160, ensuring stable operation by preventing overheating. An energy management unit 180 is also operatively connected to the processor 160 and other subsystems, dynamically allocating power based on real-time operational data to maintain system efficiency and prevent thermal overload.

[0135] The processor 160 coordinates the operation of the entire apparatus, manually or automatically controlling the latter. The output beam, with its adjusted intensity, phase, and focus, is emitted in a predetermined direction by the beam emitter, configured to deliver precise and adaptable beam parameters for various operational applications. This integrated design ensures efficient and reliable operation of the adaptive beam control apparatus under dynamic conditions.

[0136] Figure 4 is a sequential block diagram illustrating various sub-steps “T” of a power distribution and cooling method intended to be applied to the adaptive beam control apparatus 100. The logical flow of operations among its components is now described.

[0137] As described previously, the cooling system 170 incorporates TECs thermally connected to possibly high-temperature components of the adaptive beam control apparatus 100, to provide localized cooling or heating, dynamically regulated based on the polarity of the applied electricalcurrent. This integration, optionally combining TECs with phase-change cooling and liquid-metal channels, further optionally enhanced by Al-driven thermal regulation, ensures continuous operation of the adaptive beam control apparatus 100 by preventing overheating and maintaining component stability, even under high-demand scenarios.

[0138] The power distribution and cooling method begins with an allocating step T10, called Adaptive Power Allocation, “APA”, which comprises dynamically distributing power to components of the apparatus 100 based on real-time demands. This step ensures optimal energy delivery, setting the foundation for efficient power management across the system and providing the necessary energy for subsequent operations.

[0139] Building on the allocated power, the method continues with a regulating step T12, called Power Decentralization (with corresponding Nodes), “DPN”, which comprises managing localized power distribution to various subsystems, including cooling and beam control modules. This step ensures that power is delivered precisely to where it is needed most, minimizing energy wastage and preparing the system for efficient cooling and operational adjustments.

[0140] With power distributed, the method further includes a managing step T20, called Phase-Change Cooling (with a corresponding System), “PCC”, which comprises absorbing and dissipating heat generated by high-temperature components. This step introduces the first layer of thermal management by utilizing PCMs to stabilize temperatures and maintain component reliability for continued operation.

[0141] To further support thermal management, the method proceeds with a transferring step T22, called Liquid-Metal Cooling (with corresponding Channels), “LMC”, which comprises facilitating rapid heat transfer from critical areas like the optical system and segmented gain module to external radiators. This step complements the Phase-Change Cooling System by expediting heat removal, ensuring that high-demand components remain operational under elevated thermal loads.

[0142] To refine temperature control at a localized level, the method includes a controlling step T24, called Thermoelectric Cooling (with corresponding Modules), “TC”, which comprises providing targeted cooling or heating based on the polarity of the applied electrical current. This step ensures precise thermal regulation near critical components, bridging the gap between system-wide and localized cooling requirements.

[0143] Coordinating these cooling mechanisms, the method further comprises a forecasting step T30, called Predictive Thermal Management, “PTM”, which includes predicting thermal demands and adjusting cooling systems proactively based on real-time operational data. This step integrates thermal regulation with operational forecasting, ensuring stability and preparing the system for fluctuating thermal conditions.

[0144] To maintain overall functionality, the method proceeds with a coordinating step T32, called Al Decision-Making (with a corresponding Engine), “Al”, which comprises directing the cooling modules and power allocation systems to ensure uninterrupted functionality of criticalcomponents, such as the adaptive beam control system and multi-sensor data fusion modules. This step harmonizes energy and cooling management, optimizing performance across the entire system.

[0145] During elevated thermal loads, the method includes a shedding step T34, called Adaptive Load Shedding, “ALS”, which comprises temporarily reducing power to non-essential components to reallocate energy to high-priority systems. This step prioritizes critical operations, ensuring stable performance while preventing system overloads and maintaining efficiency.

[0146] Finally, the method concludes with a safeguarding step T40, called Safety and Redundancy Features, “OUT” which comprises concluding the various steps of the method after autonomously regulating cooling and power in response to possibly unforeseen surges or malfunctions. By implementing redundant cooling paths and fail-safe mechanisms, this step ensures uninterrupted operation, protecting the apparatus 100 from unexpected failures and securing overall system integrity.

[0147] Figure 5 illustrates a block diagram of steps of a method for controlling an electromagnetic beam source with an adaptive beam control apparatus according to an example.

[0148] As illustrated, the adaptive beam control apparatus 100 and the sensor system 10 are separated and operatively connected to each other, but the sensor system 10 is preferably comprised in the adaptive beam control apparatus 100.

[0149] Specifically, a method performed by the adaptive beam control apparatus 100 is illustrated for processing and emitting an electromagnetic beam B2.

[0150] The method begins with a receiving step S10, “REC_B1”, in which the optical system 120, incorporating an adaptive lens array 121, receives the input beam B1 from an electromagnetic beam source (not represented here). This step S10 initializes the process by directing the incoming electromagnetic energy into the optical system for subsequent adjustments. The adaptive lens array 121 captures the input beam B1 .

[0151] The method proceeds to a modifying step S20, which entails a sequence of adjustments to the input beam B1. This step includes a changing step S21, “CHG_PM”, where the adaptive lens array 121 modifies at least one physical parameter of the beam, such as focus or divergence. At least one processor 160 of the adaptive beam control apparatus 100 is configured to control the adaptive lens array 121 in real time, using feedback mechanisms to dynamically adapt the beam’s parameters based on operational demands. By adjusting the focus, the apparatus can concentrate energy on a specific point, while modifications to the divergence can control the beam’s spread over a wider area.

[0152] A modulating step S22 follows, “MOD_PH”, during which the programmable phase plate 130 applies a phase modulation to the input beam B1. The programmable phase plate 130 is optically connected to the optical system 120 and allows the apparatus to tailor the shape of the beam to match specific application needs, such as producing a flat-top or Gaussian beam profile.The at least one processor 160 controls the programmable phase plate 130 to execute these adjustments.

[0153] The method continues with another changing step S23, “CHGJNT”, distinct from S21, during which the segmented gain module 140 modifies the intensity of the input beam B1. This gain module is configured to selectively amplify or attenuate specific sections of the beam, enabling localized intensity adjustments. The processor 160 directs this process by providing control signals to the gain module 140, allowing the apparatus to adapt the beam’s intensity distribution in real time.

[0154] The processor 160 dynamically adjusts beam parameters based on the predicted target trajectory. The adaptive lens array 121 changes at least one physical parameter of the input beam B1, including focus and divergence, in step S21. The programmable phase plate 130 modifies the phase modulation of the input beam B1 in step S22, while the segmented gain module 140 adjusts the intensity in step S23. These modifications ensure the output beam B2 aligns with one or more predicted locations of the target 200.

[0155] Once the input beam B1 has undergone all these modifications and changes, the method typically concludes with an emitting step S30, “EMM_B2”, during which the beam emitter 150 projects the modified output beam B2 in a predetermined direction toward the target 200.

[0156] The beam emitter 150, optically connected to the segmented gain module 140, ensures that the beam retains the modifications made during the earlier steps. The output beam B2 is now adapted with optimized focus, phase, and intensity to enable precise and effective interaction with the target 200.

[0157] The entire method is coordinated by the processor 160, which dynamically controls the operations of the optical system 120, programmable phase plate 130, segmented gain module 140, and beam emitter 150. By integrating these steps, the adaptive beam control apparatus 100 achieves efficient and precise beam control.

[0158] Herein, dynamically controlling or optimizing refers to iteratively adjusting beam parameters, including intensity, focus, and direction, in real time, e.g., using a genetic algorithm GA. This optimization accounts for changing engagement conditions, such as target behavior and environmental factors. The genetic algorithm evaluates configurations using a fitness function, determining the most effective combination for achieving desired outcomes. The fitness function evaluates criteria such as energy efficiency relative to target neutralization, engagement duration calculated as the time needed to neutralize a target, and target neutralization probability based on beam configurations.

[0159] In possible embodiments, at least one of the processors 160 incorporates feedback loops to continuously monitor and adjust the process, ensuring that the apparatus responds effectively to dynamic operational conditions.

[0160] In embodiments, the method comprises detecting an electromagnetic signature from a target 200 by a sensor system 10, where the target is distinct from the adaptive beam controlapparatus. The detection includes for instance detecting thermal radiation emitted by the target using an infrared sensor module, said infrared sensor module being configured to operate at multiple wavelengths, including mid-wave infrared and long-wave infrared wavelengths. A comprehensive thermal profile can also be captured under various environmental conditions, as well as a visual signal of the target in the visible spectrum and the near-infrared spectrum. This enables the identification of reflective or visible characteristics of the target.

[0161] The method can also comprise controlling the programmable phase plate to adjust the beam profile of the input beam. The beam profile is dynamically selected from a flat-top, Gaussian, or Bessel configuration based on operational requirements. The processor coordinates all operations, ensuring seamless integration of detection, modification, emission, thermal management, and energy distribution to optimize the functionality of the adaptive beam control apparatus. This coordinated process enables efficient and precise beam delivery tailored to various environmental and operational conditions.

[0162] As described previously, the method can incorporate steps corresponding to the processing of the detected data by at least one localized fusion node, which integrates data from optical, electromagnetic, and optionally quantum entanglement modules. This processing step ensures that the multi-sensor inputs are analyzed and combined to generate actionable insights.

[0163] In a possible embodiment, the method comprises a receiving step S40, “REC_TD”, in which the processor 160 acquires real-time sensor data from a sensor system 10 configured to detect electromagnetic signatures and / or operational attributes from the target 200. The sensor system 1 includes the previously described sensors.

[0164] In an embodiment, the method employs a hierarchical data fusion framework, integrating data from multiple sensors 12, 13, and 14 to refine operational attributes of targets. Each sensor transmits data to localized fusion nodes 15, which conduct preliminary analysis before relaying relevant information to the centralized fusion hub 16. This distributed approach enhances the accuracy of target detection and classification by cross-referencing thermal, radar, and visual data, identifying patterns that individual sensors might miss.

[0165] Herein, operational attributes of targets 200 include parameters such as position, velocity, trajectory, and radar cross-section, enabling a comprehensive characterization of the target. These attributes advantageously help determining engagement strategies and ensuring precise beam control when emitting B2.

[0166] The method can further comprise refining the operational attributes of the targets. This refinement includes improving the accuracy of detection, classification, and prioritization based on real-time engagement data. By leveraging advanced sensor inputs and Al-driven analysis, the system enhances its ability to distinguish between multiple targets and adapt to complex scenarios.

[0167] Data received from the sensor system 10 is sent to at least one processor, such as the processor 160, where it is analyzed in the analyzing step S50, “PRIO”, which may rely upon atleast one decision-making engine, which can be an Al decision-making engine. This analysis prioritizes one or more targets based on their operational attributes, ensuring that the most relevant targets are addressed promptly for optimal engagement.

[0168] In an embodiment, the method proceeds to a generating step S60, “GEN_CS”, wherein the Al decision-making engine creates control signals based on the analyzed data. These control signals are intended to be sent to a processor 160 of the apparatus 100 and configured to dynamically adjust the optical system 120 and to modify physical parameters of the input beam, such as focus or divergence.

[0169] Herein, algorithms configured for dynamic (re)configuration of beam parameters include recurrent neural networks for adaptive learning and genetic algorithms for multi-objective optimization. These algorithms allow the system to adapt dynamically to real-time conditions and historical data, optimizing beam control for the prioritized targets.

[0170] The control signals generated in the generating step S60 are transmitted to the processor 160, which coordinates the operation of the optical system 120, programmable phase plate 130, and segmented gain module 140. This coordination ensures seamless integration of the sensor data with beam modification and emission, enabling precise and efficient targeting. **The processor operates in real-time, dynamically adjusting the system’s components to maintain optimal functionality under varying conditions.

[0171] Although not illustrated, the method can also comprise thermally managing the processor 160, the optical system 120, and the segmented gain module using a cooling system 170. The thermal management includes absorbing and storing thermal energy in a phase-change cooling module that employs a phase-change material. Additionally, power can be dynamically allocated at any point during the steps of the controlling method by an energy management unit 180, distributing energy among the processor, optical system, and gain module based on real-time operational data, including power demands from beam modification and emission operations.

[0172] In an embodiment, and subsequently to the steps S40, S50 and S60 when present, a dynamically optimizing step S70 can be carried out involving the processor 160 and an Al decision-making engine 17 to further control changes in beam parameters including intensity, focus, and direction.

[0173] In an embodiment, the optimization can be performed iteratively using a genetic algorithm “GA”, which is an algorithm able to generate and refine configurations of beam parameters in real time.

[0174] Herein, a genetic algorithm is an algorithm inspired by natural selection principles and able to generate, evaluate, and update beam parameter configurations to achieve optimal performance for a given situation. The algorithm includes initialization of a population of beam configurations, evaluation of fitness scores for each configuration, selection of the fittest candidates, crossover to combine configurations, and mutation to introduce variability. Over successive generations of beam parameter configurations, the genetic algorithm converges onan optimal or near-optimal solution based on fitness function metrics. This iterative process ensures adaptability to evolving engagement conditions and enables precise control of beam parameters for effective target engagement.

[0175] This approach adapts to changes in engagement conditions such as target behavior, environmental factors, and internal constraints. The genetic algorithm evaluates configurations using a fitness function that measures technical metrics like energy efficiency, engagement duration, and neutralization probability of different types of targets 200. This iterative process ensures the beam parameters are continuously adjusted for optimal performance in diverse operational scenarios.

[0176] The method can also include various transitioning steps, where the Al decision-making engine 17 switches between engagement modes based on real-time situational data S72. These engagement modes include for instance a high-power single-target mode, concentrating energy on a single target, and a low-power multi-target mode for distributing energy across multiple targets during swarm scenarios. The Al decision-making engine 17 is optionally configured to analyze situational data to determine the appropriate mode, enhancing the system's adaptability to dynamic engagement requirements.

[0177] In an embodiment, the Al decision-making engine 17 incorporates a Bayesian inference network as part of a dynamical optimization step S70. This network contemporaneously updates the probability of a threat's severity based on real-time data S40 received from the sensor system 10. Integrated with a deep learning neural network, the Bayesian inference network can identify patterns in target behavior such as evasive maneuvers or coordinated swarm tactics. Contextual analysis evaluates the engagement environment, considering factors like geography, weather, and electronic countermeasures, ensuring threat prioritization adapts to dynamic conditions.

[0178] Herein, refining operational attributes includes improving the accuracy of detection, classification, and prioritization of targets based on real-time engagement data. The integration of sensor inputs and Al-driven analysis enables the system to adapt to complex scenarios, distinguishing between multiple targets and addressing the most relevant ones with precision.

[0179] The Al employs a real-time feedback mechanism to learn from every interaction, refining its algorithms for improved future performance. After each engagement, the system evaluates outcomes like energy consumption, prediction accuracy, and engagement success rates. Metalearning algorithms adjust the weighting of parameters in prioritization and prediction processes, continuously enhancing the Al’s decision-making capabilities.

[0180] This real-time feedback mechanism ensures continuous learning, allowing the Al to refine its strategies for threat prioritization and target engagement. By adapting its algorithms based on prior experiences, the system becomes more effective in handling dynamic and complex operational environments. The described process aligns with the figure, demonstrating the integration of sensor data from step S40, priority analysis in step S50, control signal generationin step S60, and dynamic optimization in step S70, ensuring precise and efficient target engagement.

[0181] In a possible embodiment, predictions resulting from any of the method steps can be enhanced by applying, during a step S74, a recurrent neural network, RNN, configured with a long short-term memory, LSTM, topology to process sequential data.

[0182] When the step S74 is carried out, the RNN retains temporal information through recurrent connections, enabling the processor 160 to predict trajectories of targets 200 for which sensor data has been received. The training data for the RNN includes trajectories, environmental variables such as wind speed and terrain type, and engagement outcomes. The RNN can also use activation functions like “sigmoid” or “tanh” functions and / or apply backpropagation through time, BPTT, during training.

[0183] In this possible embodiment, further refining steps can be implemented to improve the RNN predictions using real-time sensor inputs received from modules operatively connected to the optical system 120. These inputs dynamically update the predictions of the RNN predictions by providing data on target movement and environmental conditions.

[0184] In light of the previously described modules, the hierarchical data fusion framework can integrate pre-processed outputs from localized nodes 15, which then relay consolidated data to the centralized hub 16. This architecture improves the accuracy of predictions by crossreferencing sensor inputs.

[0185] This mechanism enables comparing forecasted and actual target positions, iteratively refining predictions obtained from the RNN. This adaptive process allows the model to adjust to new scenarios, including evasive maneuvers and coordinated swarm tactics, ensuring reliability across diverse operational environments.

[0186] The real-time feedback loop continuously improves the system's performance. After each engagement, the processor 160 evaluates outcomes, such as prediction accuracy and energy consumption, to refine algorithms. This meta-learning process enhances the ability of the Al decision-making engine 17 to adapt to new operational contexts.

[0187] Although a variety of techniques and examples of such techniques have been described herein, these are provided by way of example only and many variations and modifications on such examples will be apparent to the skilled person and fall within the spirit and scope of the present invention, which is defined by the appended claims and their equivalents.

Claims

CLAIMS1. An adaptive beam control apparatus for controlling an electromagnetic beam source, comprising:• an optical system including an adaptive lens array operatively connected to said electromagnetic beam source, said adaptive lens array being configured to receive a first electromagnetic beam, called input beam, from the electromagnetic beam source, the adaptive lens array being further configured to change at least one physical parameter of the received input beam,• a programmable phase plate optically connected to the optical system, the programmable phase plate being configured to apply a phase modulation to the input beam,• at least one segmented gain module optically connected to the programmable phase plate, said at least one segmented gain module being configured to apply an intensity change of the input beam,• a beam emitter optically connected to the at least one segmented gain module, the beam emitter being configured to output, in a first predetermined direction, called output direction, a second electromagnetic beam, called output beam, based on the input beam received from the at least one segmented gain module, and• a processor operatively connected to the optical system, to the programmable phase plate, to the segmented modulated gain medium and to the beam emitter, the processor being configured to control the at least one physical parameter change by the adaptive lens array, the applied phase modulation by the programmable phase plate, the intensity change by the at least one segmented gain module, and the emission, by the beam emitter, of the output beam in the output direction.

2. The adaptive beam control apparatus according to Claim 1 , further comprising at least one of:a cooling system thermally connected to the processor, to the optical system and to the at least one segmented gain module, said cooling system comprising a phasechange cooling module, said phase-change cooling module comprising a phasechange material, PCM, said PCM being configured to absorb and store thermal energy when undergoing a phase transition from a solid state to a liquid state as a result of heat provided to the phase-change cooling module, the PCM being further configured to release the stored thermal energy when undergoing another phase transition from the liquid state to the solid state, and / oran energy management unit operatively connected to the processor, to the optical system (and to the at least one segmented gain module, said energy management unit being configured to dynamically allocate power distribution to the processor, tothe optical system and to the at least one segmented gain module depending on the real-time operational data received from the processor, said real-time operational data including at least one of a first power demand determined by the processor based on the at least one physical parameter change of the input beam performed by the adaptive lens array of the optical system, a second power demand determined by the processor based on the phase change applied to the input beam by the programmable phase plate, a third power demand determined by the processor based on the intensity change of the input beam performed by the at least one segmented gain module, and a power variation resulting from an operation of the processor to coordinate the optical system, the programmable phase plate, the at least one segmented gain module, and the beam emitter.

3. The adaptive beam control apparatus according to Claim 1, wherein the adaptive lens array of the optical system comprises a plurality of electrically adjustable lenses, at least one of said electrically adjustable lenses being made of electroactive polymer, EAP, embedded glass, the electrically adjustable lenses being distributed in a multi-layered configuration, each lens of the adaptive lens array being adapted for dynamically altering a refractive index and / or a divergence of the input beam in response to a corresponding control signal of the processor.

4. The adaptive beam control apparatus according to Claim 1, wherein the programmable phase plate is configured to induce a phase modulation to a profile of the input beam, the processor being configured to control the programmable phase plate to provide the profile of the input beam, with the provided profile being selected among a flat-top beam profile, a Gaussian beam profile, and a Bessel beam profile.

5. The adaptive beam control apparatus according to Claim 1, further comprising a sensor system configured to receive an electromagnetic signature from another apparatus, said other apparatus being distinct from the adaptive beam control apparatus and being called target, the sensor system comprisingan infrared sensor module configured to detect a thermal radiation emitted by said target, said infrared sensor module being configured to operate at multiple wavelengths including a mid-wave infrared, MWIR, wavelength and / or a long-wave infrared, LWIR, wavelength,an optical sensor module configured to detect a visual signal of the target, said optical sensor module being configured to operate in the visible spectrum and / or in the near-infrared, NIR, spectrum, and / oran electromagnetic, EM, sensor module configured to detect radio frequency, RF, emissions and / or EM signals emitted by the target, the EM sensor module comprising a frequency spectrum analyzer.

6. A method for controlling an adaptive beam control apparatus, the method comprising the steps of:receiving, by an optical system of said adaptive beam control apparatus, said optical system including an adaptive lens array, a first electromagnetic beam, called input beam, from an electromagnetic beam source,modifying the received input beam, the modification comprisingo changing, with the adaptive lens array, at least one physical parameter of the received input beam, the adaptive lens array being configured to receive the input beam and change said at least one physical parameter including a focus or a divergence,o applying, with a programmable phase plate of the adaptive beam control apparatus, said programmable phase plate being optically connected to the optical system, a phase modulation of the input beam,o changing, with at least one segmented gain module of the adaptive beam control apparatus, said at least one segmented gain module being optically connected to the programmable phase plate, an intensity of the input beam, the segmented gain module being configured to selectively amplify or attenuate an intensity of the input beam,emitting, by a beam emitter of the adaptive beam control apparatus, said beam emitter being optically connected to the at least one segmented gain module, in a first predetermined direction, called output direction, a second electromagnetic beam, called output beam, based on the modified input beam,wherein the processor is operatively connected to the adaptive lens array, to the programmable phase plate and to the at least one segmented gain module,wherein, using control signals, the processor is configured to dynamically control the changes of the at least one physical parameter by the adaptive lens array, to dynamically control the changes of the phase modulation by the programmable phase plate, and to dynamically control the changes of the intensity by the at least one segmented gain module, producing the output beam, andwherein the processor is further configured to coordinate real-time operational data received from the optical system, the programmable phase plate, the segmented gain module, and the beam emitter to optimize the modification and emission of the output beam.

7. The method according to Claim 6, further comprising the steps of:• receiving, by the processor, real-time sensor data from a sensor system, said sensor system being configured to receive an electromagnetic signature from another apparatus, called target, the sensor system comprisingan infrared sensor module configured to detect a thermal radiation emitted by said target, said infrared sensor module being configured to operate at multiple wavelengths including a mid-wave infrared, MWIR, wavelength and / or a long-wave infrared, LWIR, wavelength,an optical sensor module configured to detect a visual signal of the target, said optical sensor module being configured to operate in the visible spectrum and / or in the near-infrared, NIR, spectrum, and / oran electromagnetic, EM, sensor module configured to detect radio frequency, RF, emissions and / or EM signals emitted by the target, the EM sensor module comprising a frequency spectrum analyzer,the method further comprising the steps, after receiving said real-time sensor data, of • analyzing, with at least one Al decision-making engine operatively connected to the processor, the received real-time sensor data to prioritize one or more targets based on operational attributes of the targets,• generating, with the at least one Al decision-making engine, control signals configured to be subsequently sent to the processor, said control signals being based on the analyzed real-time sensor data, the control signals being configured to dynamically adjust, with the processor,o the optical system to modify at least one physical parameter of the input beam, o the programmable phase plate to modify the phase distribution of the input beam, ando the at least one segmented gain module to control the intensity of the input beam.

8. The method according to Claim 6, further comprising the steps of dynamically optimizing, with the processor in conjunction with at least one Al decision-making engine, configurations of the processor to dynamically control:the changes of the at least one physical parameter by the adaptive lens array, to dynamically control the changes of the phase modulation by the programmable phase plate,the changes of the intensity by the at least one segmented gain module, producing the output beam,the dynamical optimization comprising a sub-step of employing said at least one Al decision-making engine to refine said changes of the at least one physical parameter ofthe phase modulation and of the intensity based on a Bayesian inference network, a contextual weighting and / or a self-enhancing design, the dynamically optimized configurations of the processor being used during the modification step of the received input beam.

9. The method according to Claim 6, further comprising the steps of dynamically optimizing, with the processor in conjunction with at least one Al decision-making engine, configurations of the processor to dynamically control:the changes of the at least one physical parameter by the adaptive lens array, to dynamically control the changes of the phase modulation by the programmable phase plate,the changes of the intensity by the at least one segmented gain module, producing the output beam,the dynamical optimization comprising a sub-step of applying a recurrent neural network, RNN, trained on historical datasets comprising trajectories and environmental conditions associated to a target, to forecast the most probable future path of said target, the dynamically optimized configurations of the processor being used during the modification step of the received input beam.

10. At least one computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method for controlling an adaptive beam control apparatus, the method comprising:receiving, by an optical system of said adaptive beam control apparatus, said optical system including an adaptive lens array, a first electromagnetic beam, called input beam, from an electromagnetic beam source,modifying the received input beam, the modification comprising■ changing, with the adaptive lens array, at least one physical parameter of the received input beam, the adaptive lens array being configured to receive the input beam and change said at least one physical parameter including a focus or a divergence,■ applying, with a programmable phase plate of the adaptive beam control apparatus, said programmable phase plate being optically connected to the optical system, a phase modulation of the input beam, ■ changing, with at least one segmented gain module of the adaptive beam control apparatus, said at least one segmented gain module being optically connected to the programmable phase plate, an intensity of the input beam, the segmented gain module being configured to selectively amplify or attenuate an intensity of the input beam,■ emitting, by a beam emitter of the adaptive beam control apparatus, said beam emitter being optically connected to the at least one segmented gain module, in a first predetermined direction, called output direction, a second electromagnetic beam, called output beam, based on the modified input beam,wherein the at least one processor is operatively connected to the adaptive lens array, to the programmable phase plate, and to the at least one segmented gain module,wherein, using control signals, the at least one processor is configured to dynamically control the changes of the at least one physical parameter by the adaptive lens array, the phase modulation by the programmable phase plate, and the intensity by the at least one segmented gain module, producing the output beam, andwherein the at least one processor is further configured to coordinate real-time operational data received from the optical system, the programmable phase plate, the segmented gain module, and the beam emitter to optimize the modification and emission of the output beam.

Images