EP4235084A1 - Waffen systemintegration - Google Patents

Waffen systemintegration Download PDF

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Publication number
EP4235084A1
EP4235084A1 EP22275022.6A EP22275022A EP4235084A1 EP 4235084 A1 EP4235084 A1 EP 4235084A1 EP 22275022 A EP22275022 A EP 22275022A EP 4235084 A1 EP4235084 A1 EP 4235084A1
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EP
European Patent Office
Prior art keywords
polynomial
candidate
polynomials
aircraft
coefficients
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22275022.6A
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English (en)
French (fr)
Inventor
designation of the inventor has not yet been filed The
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BAE Systems PLC
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BAE Systems PLC
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Filing date
Publication date
Application filed by BAE Systems PLC filed Critical BAE Systems PLC
Priority to EP22275022.6A priority Critical patent/EP4235084A1/de
Priority to US18/841,130 priority patent/US20250190816A1/en
Priority to JP2024550287A priority patent/JP7841107B2/ja
Priority to EP23707456.2A priority patent/EP4483123A1/de
Priority to PCT/GB2023/050404 priority patent/WO2023161629A1/en
Priority to EP23707787.0A priority patent/EP4483125A1/de
Priority to GB2302532.3A priority patent/GB2617455B/en
Priority to US18/841,142 priority patent/US20250189270A1/en
Priority to JP2024550290A priority patent/JP7841109B2/ja
Priority to PCT/GB2023/050403 priority patent/WO2023161628A1/en
Priority to JP2024550288A priority patent/JP7841108B2/ja
Priority to US18/841,156 priority patent/US20250187749A1/en
Priority to GB2302533.1A priority patent/GB2617684B/en
Priority to GB2302535.6A priority patent/GB2617685B/en
Priority to PCT/GB2023/050402 priority patent/WO2023161627A1/en
Priority to EP23707786.2A priority patent/EP4483124A1/de
Publication of EP4235084A1 publication Critical patent/EP4235084A1/de
Pending legal-status Critical Current

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F41—WEAPONS
    • F41G—WEAPON SIGHTS; AIMING
    • F41G3/00—Aiming or laying means
    • F41G3/22—Aiming or laying means for vehicle-borne armament, e.g. on aircraft
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F41—WEAPONS
    • F41G—WEAPON SIGHTS; AIMING
    • F41G7/00—Direction control systems for self-propelled missiles
    • F41G7/007—Preparatory measures taken before the launching of the guided missiles
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F41—WEAPONS
    • F41G—WEAPON SIGHTS; AIMING
    • F41G9/00—Systems for controlling missiles or projectiles, not provided for elsewhere
    • F41G9/002—Systems for controlling missiles or projectiles, not provided for elsewhere for guiding a craft to a correct firing position

Definitions

  • a Launch Success Zone is calculated, indicative of the probability of successfully engaging a selected air target being above some threshold value.
  • the LSZ is used to provide a cockpit display indicating whether the weapon is capable of successfully engaging the target.
  • calculation of an LSZ is more complicated than the calculation of an LAR because the relative speeds and directions of travel of the launch aircraft and the target are much greater, the effects of ambient conditions are greater, and also the physical properties of the weapons in flight are more significant on the calculation.
  • a computer-implemented method of generating, in an aircraft in flight, a feasibility display indicative of a feasibility of a weapon carried on the aircraft successfully engaging a target and/or a feasibility of a weapon carried on the target successfully engaging the aircraft comprising:
  • an internal representation of the space to be searched is selected and an external function that assigns a fitness value to candidate solutions is defined.
  • the inventors have adapted the genetic algorithm to parallelise over the many polynomial orders (also known as degrees) and input parameters that the genetic algorithm has to run. Each run tests several parameter combinations of polynomial order and inputs, reporting on the execution time, memory and flops operations for the final model requirement to solve the problem for each parameter combination, as described below in more detail.
  • the method can be used for different weapon types, and a respective set of coefficients may be easily determined for each weapon type e.g. for each of a plurality of different firing conditions (i.e. aircraft and target conditions).
  • the aircraft and target conditions may include but are not limited to one or more of their relative positions, distances, directions of movement, speeds and ambient atmospheric conditions.
  • the weapon or aircraft firing condition parameters may include, but are not limited to, parameters such as aircraft velocities, aircraft height, aircraft attitude, slant range to target, target velocities, target height, line of sight azimuth, target pitch and aspect angles, and wind speed.
  • the weapon or aircraft firing condition parameters may include, but are not limited to relative velocities and directions of travel of the launch aircraft and the target and those of the weapon relative to the target.
  • each aircraft within a fleet comprising a plurality of different aircraft is loaded with the same, common generic polynomial.
  • the specific coefficients corresponding to that weapon may also be loaded onto that aircraft. This tends to be in contrast to conventional systems in which, although the tools for generating LAR/LSZs may be common across multiple different aircraft, when a weapon is loaded onto an aircraft, both a polynomial/algorithm and corresponding coefficients for generating LAR/LSZs are generated for that aircraft and weapon load-out.
  • the above aspects provide a generic polynomial/algorithm that may be used (e.g. simultaneously) by multiple different types of aircraft.
  • Different types of aircraft may use the same generic algorithm to calculate LARs/LSZs.
  • the same generic algorithm may be used to calculate LARs/LSZs for different weapon types.
  • aircraft software comprising the generic polynomial and means for allowing loading of coefficients for each weapon loaded on aircraft is produced only once.
  • the software algorithm and coefficients, for any given weapon are the same for any aircraft type.
  • This tends to be different to conventional methodologies in which, although common tools may be used for polynomial and coefficient generation, both the software (including an algorithm/polynomial) and coefficients are generated for every weapon type and every time the weapon performance is changed. This need to rewrite the software and the certification of it tends to be particularly costly.
  • the above described method and system advantageously tend to provide that the aircraft software does not have to be rewritten and hence no new certification is required.
  • iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials comprises iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials on respective processors and/or different threads.
  • iteratively applying the genetic algorithm over the variables of the polynomials for each order and/or type of the respective sub-set thereof comprises:
  • iteratively applying the genetic algorithm over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof comprises iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof.
  • iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof comprises iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof on respective threads and/or processors.
  • iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials comprises conditionally iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials, including iteratively applying the genetic algorithm over the variables of the polynomials for each order and/or type of the respective sub-set thereof and saving the resulting respective coefficients and scores thereof.
  • iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials is conditional, for example upon not meeting a predetermined threshold, thereby accelerating terminating of an iteration, for example.
  • conditionally iteratively applying the genetic algorithm concurrently over the plurality of sub-sets of the defined set of orders and/or types of the candidate polynomials comprises terminating applying the genetic algorithm over the variables of the polynomials for each order and/or type of the respective sub-set thereof if the respective scores do not meet a threshold.
  • the threshold is predetermined.
  • the method comprises determining the threshold based on a previous score.
  • the orders of the candidate polynomials of the set thereof are in a range from 1 to 100, preferably in a range from 2 to 25, more preferably in a range from 3 to 10, most preferably in a range from 5 to 9, for example 5, 6, 7, 8, 9.
  • the order of the generic polynomial is 3 or greater. In one example, the order of the generic polynomial is in a range from 10 to 25, for example 20. Surprisingly, the inventors have found that using a generic algorithm with an order of around 20 adequately describes most air-to-air engagements accurately in an appropriate runtime for on-aircraft implementation. Nevertheless, the generic algorithm may have an order greater than 2.
  • the target comprises and/or is an aircraft.
  • the feasibility display is indicative of a Launch Success Zone of the aircraft and/or the target.
  • a database is generated by: defining the range of conditions for which the weapon may be required to be fired, the range of aircraft conditions for which it is feasible for the aircraft to fire the weapon and the range of weapon conditions for which it is feasible to fire the weapon; generating data indicative of the weapon performance for each weapon firing possibility from within the defined ranges; and creating a database defining the weapon's overall performance envelope.
  • the coefficients may then be determined from this database and the generic polynomial.
  • the database can be generated on a ground-based system, so that the aircraft system needs the capacity only to store the generic polynomial and process the coefficients with the aircraft and target conditions in order to generate the feasibility display.
  • the amount of data storage/processing capacity required on the aircraft tends to be reduced.
  • the step of uploading, to the aircraft, the generated coefficients may be performed when the weapon is loaded as an aircraft store.
  • the coefficients associated with that weapon may be uploaded to the aircraft at the same time as the weapon.
  • the coefficients are stored on a hardware device with the weapon, and the device is connected to the aircraft to upload the coefficient data as the weapon is loaded.
  • a non-transient computer-readable storage medium comprising instructions which, when executed by a computer, comprising a processor and a memory, cause the computer to perform a method according to the first aspect.
  • Figure 1A schematically depicts the LAR in the plane of flight of a launch aircraft 1 flying along a flight path 3 in respect of a target 5 for an air-to-surface weapon (not shown) loaded on the aircraft.
  • the LAR is calculated to provide cockpit displays in the launch aircraft 1 concerning the feasibility and firing opportunities for the situation.
  • Figure 1B schematically depicts the display generated for the LAR of Figure 1A , which is in the form of a down range and cross range display (shaded area), where the weapon flight path 7 coincides with the aircraft flight path 3; to successfully engage the target 5 as shown in the display, the target must fall inside the shaded LAR.
  • the displayed LAR is bounded by the minimum and maximum ranges, R min and R max .
  • a Missile Engagement Zone (MEZ) for the target 5 may be determined and displayed to the pilot of the aircraft 1.
  • This MEZ may indicate a region in which the likelihood of a ground-to-air weapon (e.g. a missile) carried by the target 5 successfully intercepting the aircraft 1 is above a threshold value.
  • a ground-to-air weapon e.g. a missile
  • the LSZ shown in Figure 2 is the region where the probability of an air-to-air weapon hitting an airborne target T is above a threshold level. Calculation of the LSZ is more complicated than for the LAR, because a greater number of factors are involved, such as the relative velocities and directions of travel of the launch aircraft and the target, and those of the weapon relative to the target.
  • DDWI Data Driven Weapon Integration
  • the mission data coefficients uploaded onto the platform are derived from a sophisticated multi-dimensional weapon model. Performing parallel computations on multicore computers, GPUs, and computer clusters let the inventors solve such computationally and data-intensive problems, unlock more performance and reduction in processing time.
  • Figure 4 schematically depicts the system of Figure 3 , in more detail, and is divided between those processes 11 which are carried out on the ground and the processes 13 which are carried out on the launch aircraft 1.
  • the system is for generating in an aircraft in flight, a feasibility display indicative of a feasibility of a weapon carried on the aircraft successfully engaging a target and/or a feasibility of a weapon carried on the target successfully engaging the aircraft, the computer, the system comprising a first computer, comprising a memory and a processor, remote from the aircraft and a second computer, comprising a memory and a processor, onboard the aircraft.
  • the core of the DDWI is the off-line coefficient generator 21.
  • the coefficient generator 21 identifies coefficients for the generic algorithm to make it 'fit' the performance envelope shape.
  • the form of the generic algorithm is usually decided in advance e.g. any polynomial equation of degree (i.e. order) up to n.
  • the coefficient generator 21 receives the true performance envelope and calculates coefficients for the generic algorithm.
  • the estimation and fitting process uses a Genetic Algorithm for self-organising polynomial neural network approach. It calculates the sets of coefficients that would allow the geometric shapes of LAR/LSZ regions to be modelled (and subsequently reconstructed) by standard polynomial "algorithms", see Figure 5 . It uses an evolutionary technique called Genetic Algorithm as the central mechanism for Self-Organising Polynomial Neural Network (GA-SOPNN), and automating the derivation of a number of polynomial model's coefficients within each layer. The process involves the following steps:
  • an internal representation of the space to be searched is selected and an external function that assigns a fitness value to candidate solutions is defined.
  • the inventors have adapted the genetic algorithm to parallelise over the many polynomial orders and input parameters that the genetic algorithm has to run, as shown in Figure 6 . Each run tests several parameter combinations of polynomial order and inputs, reporting on the execution time, memory and flops operations for the final model requirement to solve the problem for each parameter combination, as described below in more detail.
  • the polynomial is parameterized into a string of binary string comprising three parameters (also known as sub-chromosones):
  • iteratively applying the genetic algorithm over the variables of the polynomials for each order and/or type of the respective sub-set thereof comprises:
  • iteratively applying the genetic algorithm over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof comprises iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof.
  • iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof comprises iteratively applying the genetic algorithm concurrently over the selected combinations of the variables of the polynomials for each order and/or type of the respective sub-set thereof on respective threads and/or processors.
  • the performance parameters for each of the aircraft types may include, but are not limited to, a maximum achievable altitude, a maximum achievable g-force, and a maximum achievable climb angle.
  • the values of the performance parameters for different types of aircraft may be different from one another. For example, a first type of aircraft may have a maximum altitude of 45,000ft whereas a second type of aircraft may have a maximum altitude of 55,000ft, and so on.
  • the data space generator 15 further comprises data which describes performance parameters for each of a plurality of different weapon types, e.g. different weapons that may be loaded onto to the launch aircraft or may be expected to be carried by a hostile target. These weapon performance envelopes may be supplied by the weapon manufacturers or through testing.
  • the plurality of different weapon types includes the type of the weapon that is carried by the launch aircraft 1 and, preferably, the target.
  • the performance parameters for each of the weapon types may include, but are not limited to, a maximum altitude at which the weapon may be released, a maximum g-force at which the weapon may be released, and release mechanism of the weapon.
  • the values of the performance parameters for different types of weapon may be different from one another. For example, a first type of weapon may be able to be released up to an altitude of 35,000ft, whereas a second type of weapon may be able to be released up to an altitude of 45,000ft, and so on.
  • the data space generator 15 may define the release, weather and commanded impact conditions for training and verification sets which are run by a truth data generator 17.
  • the truth data generator 17 determines the weapon performance for each firing case in the data space; this depends on the weapon performance model which is usually provided by the weapon manufacturer.
  • the product of the truth data generator 17 is the truth database 19, which is a set of data specifying, for each weapon type, the further weapon performance envelope for each of a plurality of exemplary weapon firings.
  • the truth data generator 17 may produce the training and verification sets which are used by a coefficient generator 21.
  • the coefficient generator 21 receives the further weapon performance envelopes stored by the truth database 19 and calculates, for each weapon type and for each example weapon firing, coefficients according to a generic LAR/LSZ algorithm 23 that "fit" the generic algorithm to the further weapon performance envelope shape.
  • the whole process is then repeated with the outputs of the first layer providing the inputs to create a second layer, Layer 2, of the SOPNN.
  • the new layer has the effect of creating higher-order candidate polynomials and coefficients for consideration.
  • the selection of polynomials in the new layer is again governed and optimised by the Genetic Algorithm.
  • Layers are added to the SOPNN in this way until improvement in the scores of the best candidates ceases or some other criteria are satisfied.
  • a completed network comprising two layers is represented in Figure 5 .
  • the final network is obtained recursively from the path ending at the output node with the best score in the final generation of candidates (the "Optimum Solution"). Any node with no connection to this path is discarded as shown in Figure 5 , where nodes which contribute to the optimal solution are lightly shaded and discarded nodes are black.
  • the inventors have adapted the genetic algorithm to parallelise over the many polynomial orders and input parameters that the genetic algorithm has to run.
  • the aircraft type of the hostile target T may be determined by the pilot of the launch aircraft 1 (or by other means) and input to the reconstructor 25.
  • the reconstructor 25 onboard the launch aircraft 1 may then select, from the uploaded coefficients, those coefficients that correspond to the weapon most likely being carried by the hostile target T and that correspond to the relevant firing conditions.
  • the selected coefficients may then be used to reconstruct the LSZ of the hostile target T for display to the pilot of the launch aircraft 1.
  • the reconstructed LSZ of the hostile target T may also be used by other systems onboard the launch aircraft 1 to recommend actions to the pilot of the launch aircraft 1 (e.g. a recommendation that certain evasive manoeuvres are performed etc.).
  • the type of the ground target 5 may be determined by the pilot of the launch aircraft 1 (or by other means) and input to the reconstructor 25.
  • the reconstructor 25 onboard the launch aircraft 1 may then select, from the uploaded coefficients, those coefficients that correspond to the weapon most likely being carried by the ground target 5 and that correspond to the relevant firing conditions.
  • the selected coefficients may then be used to reconstruct the MEZ of the ground target 5 for display to the pilot of the launch aircraft 1.
  • the reconstructed MEZ of the ground target 5 may also be used by other systems onboard the launch aircraft 1 to recommend actions to the pilot of the launch aircraft 1 (e.g. a recommendation that certain evasive manoeuvres are performed etc.).
  • Apparatus including the any of the above mentioned processors, for implementing the above described arrangement, may be provided by configuring or adapting any suitable apparatus, for example one or more computers or other processing apparatus or processors, and/or providing additional modules.
  • the apparatus may comprise a computer, a network of computers, or one or more processors, for implementing instructions and using data, including instructions and data in the form of a computer program or plurality of computer programs stored in or on a machine readable storage medium such as computer memory, a computer disk, ROM, PROM etc., or any combination of these or other storage media.

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
EP22275022.6A 2022-02-24 2022-02-24 Waffen systemintegration Pending EP4235084A1 (de)

Priority Applications (16)

Application Number Priority Date Filing Date Title
EP22275022.6A EP4235084A1 (de) 2022-02-24 2022-02-24 Waffen systemintegration
US18/841,130 US20250190816A1 (en) 2022-02-24 2023-02-22 System integration
JP2024550287A JP7841107B2 (ja) 2022-02-24 2023-02-22 システム統合
EP23707456.2A EP4483123A1 (de) 2022-02-24 2023-02-22 Systemintegration
PCT/GB2023/050404 WO2023161629A1 (en) 2022-02-24 2023-02-22 System integration
EP23707787.0A EP4483125A1 (de) 2022-02-24 2023-02-22 Systemintegration
GB2302532.3A GB2617455B (en) 2022-02-24 2023-02-22 System integration
US18/841,142 US20250189270A1 (en) 2022-02-24 2023-02-22 System integration
JP2024550290A JP7841109B2 (ja) 2022-02-24 2023-02-22 システム統合
PCT/GB2023/050403 WO2023161628A1 (en) 2022-02-24 2023-02-22 System integration
JP2024550288A JP7841108B2 (ja) 2022-02-24 2023-02-22 システム統合
US18/841,156 US20250187749A1 (en) 2022-02-24 2023-02-22 System integration
GB2302533.1A GB2617684B (en) 2022-02-24 2023-02-22 System integration
GB2302535.6A GB2617685B (en) 2022-02-24 2023-02-22 System integration
PCT/GB2023/050402 WO2023161627A1 (en) 2022-02-24 2023-02-22 System integration
EP23707786.2A EP4483124A1 (de) 2022-02-24 2023-02-22 Systemintegration

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP22275022.6A EP4235084A1 (de) 2022-02-24 2022-02-24 Waffen systemintegration

Publications (1)

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EP4235084A1 true EP4235084A1 (de) 2023-08-30

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2876402A1 (de) * 2013-11-25 2015-05-27 BAE Systems PLC Systemintegration
EP3449203A1 (de) * 2016-04-25 2019-03-06 BAE Systems PLC Systemintegration

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2876402A1 (de) * 2013-11-25 2015-05-27 BAE Systems PLC Systemintegration
EP3449203A1 (de) * 2016-04-25 2019-03-06 BAE Systems PLC Systemintegration

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
PARK ET AL: "Evolutionary design of hybrid self-organizing fuzzy polynomial neural networks with the aid of information granulation", EXPERT SYSTEMS WITH APPLICATIONS, ELSEVIER, AMSTERDAM, NL, vol. 33, no. 4, 30 March 2007 (2007-03-30), pages 830 - 846, XP022067142, ISSN: 0957-4174, DOI: 10.1016/J.ESWA.2006.07.006 *

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