System Integration

Abstract

A computer-implemented method for generating a feasibility indication indicative of the feasibility of a weapon carried on an aircraft in flight to successfully engage a target and / or the feasibility of a weapon carried on the target to successfully engage the aircraft, the method comprising: providing a database describing a performance envelope of the weapon; a) generating candidate polynomials, the polynomial variables being some or all of a group of weapon or aircraft firing condition parameters; and b) for each candidate polynomial, comparing the candidate polynomial to the performance envelope of the weapon using a least squares error criterion. calculating coefficients for the candidate polynomial that best fits the characteristics of the line; c) for each candidate polynomial, generating a candidate score according to the quality of the candidate polynomial's fit to the characteristics of the weapon's performance envelope; d) applying a genetic algorithm to the candidate polynomials and scores, including selecting the best scoring polynomial(s) and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients; and e) repeating the identification process until all required characteristics of the performance envelope have a corresponding polynomial model. d) a method for generating coefficients characteristic of a performance envelope using a generic algorithm, wherein the generic algorithm has the form of a polynomial, uploading coefficients of the identified best candidate polynomial to the aircraft, selecting coefficients for the generic algorithm according to aircraft and target conditions by a reconfigurator on the aircraft containing the same generic algorithm, and generating a feasibility indication by the reconfigurator using the selected coefficients, the method comprising: i) defining a set of orders and / or types of candidate polynomials and dividing the defined set of orders and / or types into a plurality of subsets thereof; ii) iteratively applying the genetic algorithm simultaneously across a plurality of subsets of the defined set of orders and / or types of candidate polynomials, including iteratively applying the genetic algorithm across polynomial variables for each order and / or type of each subset of polynomials and saving the resulting respective coefficients and their scores;iii) using the saved coefficients and scores to select the best scoring polynomial(s) and discard other polynomial(s), thereby identifying the best candidate polynomials and their coefficients.

Description

Technical Field

[0001] The present invention relates to system integration, and more particularly to the integration of weapons on complex and highly integrated aircraft.

Background Art

[0002] Integrating a weapon system with other systems on an aircraft is a complex and redundant task because it affects all major aircraft systems. Therefore, it is necessary to improve the weapon integration time and affordability.

[0003] One of the requirements for weapon integration is to enable the display of information to the aircraft pilot regarding whether the weapon can engage a specific target. For this purpose, weapons are typically grouped into two categories: weapons designed to engage ground targets (air-to-ground weapons) and weapons designed to engage air targets (air-to-air weapons). In the case of air-to-ground weapons, a Launch Acceptability Region (LAR) is calculated, which is an area where the probability of hitting the selected target is above some threshold. The LAR is calculated as a function of weapon performance characteristics, the relative position and motion of the aircraft and the target, and ambient conditions such as often wind speed and direction, in order to provide a cockpit display indicating feasibility in the launching aircraft. Successfully engage in combat In the case of air-to-air weapons, a Launch Success Zone (LSZ) is calculated, which indicates that the probability of hitting the selected air target is above some threshold. Again, the LSZ is used to provide a cockpit display indicating whether the weapon can engage the target. However, the calculation of the LSZ is more complex than the calculation of the LAR because the relative speeds and directions of the progress of the launching aircraft and the target are much greater, the influence of ambient conditions is greater, and the physical characteristics of the weapon in flight are also more important in the calculation. Successfully engage in combat or an area where the probability of hitting is above some threshold. The LAR is calculated to provide a cockpit display indicating feasibility in the launching aircraft and is a function of weapon performance characteristics, the relative position and motion of the aircraft and the target, and ambient conditions such as often wind speed and direction. Successfully engage in combat is calculated to provide a cockpit display indicating feasibility in the launching aircraft and is a function of weapon performance characteristics, the relative position and motion of the aircraft and the target, and ambient conditions such as often wind speed and direction.

[0004] In the case of air-to-air weapons, Successfully engage in combat a Launch Success Zone (LSZ) is calculated, which indicates that the probability of hitting is above some threshold. Again, in this case, the LSZ is used to provide a cockpit display indicating whether the weapon can Successfully engage in combat engage the target. However, the calculation of the LSZ is more complex than the calculation of the LAR because the relative speeds and directions of the progress of the launching aircraft and the target are much greater, the influence of ambient conditions is greater, and the physical characteristics of the weapon in flight are also more important in the calculation.

[0005] The conventional approach involved creating a simple abstract model of the weapon, modified according to launch conditions (taking into account aircraft and target conditions (e.g., range, direction, and speed of travel, etc.) and surrounding conditions). The model was then mounted on the aircraft and used to generate a LAR or LSZ for display to the pilot. The drawback of the conventional approach is that each model is different for each different weapon type. Storing data on several different implicit models consumes considerable memory capacity, and each model must be comprehensively integrated to ensure that it does not negatively impact any of the aircraft systems. Furthermore, if any changes or modifications are made to the weapon (such as performance improvements), or if it is necessary to load an entirely new weapon onto the aircraft, a redundant and costly integration process must be undertaken because the weapon model will be substantially different from any previously integrated with the aircraft system. [Overview of the project]

[0006] According to a first aspect of the present invention, in an aircraft in flight, a weapon carried on the aircraft targets a target Successfully engage in combat The feasibility and / or the weapons that can be delivered on the target are with aircraft and Successfully engage in combat A computer implementation method is provided for generating a feasibility statement indicating feasibility, and this method is To provide a database that describes the performance envelope of weapons, a) Generate candidate polynomials, where the variables of the polynomials are some or all of the group of firing condition parameters for weapons or aircraft. b) For each candidate polynomial, use the least-squares error criterion to calculate the coefficients for the candidate polynomial that best fits the characteristics of the weapon's performance envelope, c) For each candidate polynomial, a candidate score is generated according to the quality of the fit of that candidate polynomial to the characteristics of the weapon's performance envelope, d) Apply a genetic algorithm to the candidate polynomials and scores, including selecting the best-scoring polynomial(s) and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. e) Repeat the identification process until all required properties of the performance envelope have corresponding polynomial models. The steps include creating the coefficient characteristics of its performance envelope using a general algorithm, where the general algorithm has the form of a polynomial, Uploading the coefficients of the best identified candidate polynomial to the aircraft, The reconfigurator on the aircraft, which includes the same general algorithm, selects coefficients for the general algorithm according to the conditions of the aircraft and the target, Using the selected coefficients, the reconstructor generates a feasibility representation. In a method comprising the steps of applying a genetic algorithm to a candidate polynomial and a score, i) Define a set of order and / or type of candidate polynomials, and divide the defined set of order and / or type into multiple subsets, ii) Iteratively apply the genetic algorithm across the polynomial variables for each order and / or type of each subset of the polynomial, and obtain the resulting coefficients and their scores. keep This includes iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of candidate polynomials by order and / or type, iii) keep Using the selected coefficients and scores, select the polynomial(s) with the best score, discard the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. It is characterized by having the following features.

[0007] In this way, the method significantly improves weapon integration time and cost. More specifically, typically, the genetic algorithm proceeds iteratively by generating a new population of strings from a population of old strings. All strings are encoded versions of provisional solutions. An evaluation function associates a goodness-of-fit measure with each string, indicating its suitability for the problem. The algorithm applies probabilistic operators such as selection, crossover, and mutation to a random population to compute the entire generation of new strings. The inventors have identified that these algorithms can be adapted for use on multiple processor workstations or distributed systems with transparent process transitions. All goodness-of-fit evaluations and fitting operations can be performed in separate processes, i.e., simultaneously. For trivial goodness-of-fit functions, due to the level of overhead, little improvement in the rate of evolution is likely to be observed. However, for weapon targeting and many tasks on a particular system, this is clearly time-consuming and reflects the ad-hoc nature of the solution process. The accuracy of the goodness-of-fit depends on the complexity of the performance envelope. To accurately represent the performance envelope and achieve the required accuracy for the entire envelope or a subset thereof, a large dataset may be necessary. As the data size increases, so do the computational complexity and processing time. The benefits of performing goodness-of-fit evaluations in parallel are significant.

[0008] To apply the genetic algorithm to a specific application, an internal representation of the space to be explored is selected, and an external function is defined that assigns goodness-of-fit values ​​to candidate solutions. The inventors have adapted the genetic algorithm to be parallelizable across many polynomial orders (also known as degrees) and input parameters that the genetic algorithm must perform. Each run tests several combinations of polynomial order and input parameters and reports on the execution time, memory, and flop operation for the final model requirements to solve the problem for each parameter combination, as described in more detail below.

[0009] This method can be used for different weapon types, and each set of coefficients can be easily determined for each weapon type, for example, for each of several different firing conditions (i.e., aircraft and target conditions). Aircraft and target conditions may include, but are not limited to, one or more of their relative position, distance, direction of movement, speed, and ambient atmospheric conditions. Weapon or aircraft firing condition parameters may include, but are not limited to, parameters such as aircraft speed, aircraft altitude, aircraft attitude, slant range to target, target speed, target altitude, azimuth angle of line of sight, target pitch and aspect angle, and wind speed. Weapon or aircraft firing condition parameters may include, but are not limited to, the relative speed and direction of the launching aircraft and target, and the relative speed and direction of the weapon's progress relative to the target.

[0010] Advantageously, the general polynomials / algorithms described above can be used by multiple different types of aircraft (e.g., simultaneously). In other words, different types of aircraft can use the same general algorithm to calculate LAR / LSZ. Also, the same general algorithm can be used to calculate LAR / LSZ for different weapon types. This means that aircraft software with general polynomials and means to enable loading coefficients for each weapon loaded on the aircraft is created only once. The software algorithms and coefficients for any given weapon are the same for any aircraft type. This is because common tools can be used for polynomial and coefficient generation, but both the software (including algorithms / polynomials) and coefficients tend to be generated for each weapon type and each time weapon performance changes, unlike the conventional method. This need to rewrite the software and its certification tends to be particularly costly. The methods and systems described above tend to offer the advantage that the aircraft software does not need to be rewritten and therefore does not require new certification.

[0011] In some embodiments, each aircraft in a fleet consisting of a plurality of different aircraft is loaded with the same common general polynomial. When a weapon is loaded onto an aircraft in the fleet, the specific coefficients corresponding to that weapon can also be loaded onto that aircraft. This is in contrast to conventional systems where the tools for generating LAR / LSZ can be common across a plurality of different aircraft, but when a weapon is loaded onto an aircraft, both the polynomial / algorithm and the corresponding coefficients for generating LAR / LSZ are generated for that aircraft and the loading and unloading of the weapon.

[0012] The coefficients can be implemented as loadable data in order to enable accurate and precise weapon behavior to be realized within the weapon system. Also, using only one or a few general algorithms will allow different weapon systems to be approved or certified / authenticated with less effort and more quickly than the extensive testing required by conventional approaches for use with an aircraft. That is, a minimum number of general weapon aiming algorithms can be used to account for all weapon types.

[0013] The use of general algorithms for weapon aiming also enables an increase or significant change in weapon system capabilities to be integrated with the aircraft system with significantly less effort than before.

[0014] The weapon being carried on target with the aircraft Successfully engage in combat By determining feasibility, it is indicated whether or to what extent the aircraft is at risk from the weapons carried by an opposing target. This calculation of the opposing LSZ / MEZ enables a better assessment of the engagement. This can then lead to a confident prediction of the advantages of the engagement and the possible outcomes. Successfully engage in combat

[0015] ​Advantageously, the above aspects provide a general polynomial / algorithm that can be used by a plurality of different types of aircraft (e.g., simultaneously). Different types of aircraft can use the same general algorithm to calculate LAR / LSZ. Also, the same general algorithm can be used to calculate the LAR / LSZ of different weapon types. From this, aircraft software with a general polynomial and means for enabling loading of coefficients for each weapon loaded on the aircraft is created only once. The software algorithm and coefficients for any given weapon are the same for any aircraft type. This is different from the conventional method where common tools for polynomial and coefficient generation can be used, but both software (including algorithms / polynomials) and coefficients are generated for each weapon type and each time the weapon performance is changed. This need to rewrite the software and its certification tend to be particularly costly. The methods and systems described above tend to advantageously provide that aircraft software need not be rewritten and thus no new certification is required.

[0016] In one example, repeatedly applying a genetic algorithm across a plurality of subsets of a defined set of degrees and / or types of candidate polynomials comprises repeatedly applying a genetic algorithm across a plurality of subsets of a defined set of degrees and / or types of candidate polynomials on respective processors and / or different threads.

[0017] In this way, since the genetic algorithm is repeatedly applied on respective threads and / or processors, repeatedly applying the genetic algorithm simultaneously is enhanced.

[0018] In one example, repeatedly applying a genetic algorithm across the variables of a polynomial for each degree and / or type of each subset of the polynomial comprises selecting combinations of variables of the polynomial for each degree and / or type of each subset of the polynomial, and The iterative application of a genetic algorithm across selected combinations of polynomial variables for each order and / or type of each subset of the polynomial, and It is equipped with.

[0019] In this way, the genetic algorithm is accelerated by iteratively applying it to, for example, only to selected combinations of polynomial variables (also known as inputs) across a selection of polynomial variables.

[0020] For example, iteratively applying a genetic algorithm over selected combinations of polynomial variables for each order and / or type of each subset of a polynomial comprises simultaneously iteratively applying a genetic algorithm over selected combinations of polynomial variables for each order and / or type of each subset of a polynomial.

[0021] In this way, the genetic algorithm is iteratively applied across selected combinations of polynomial variables for each order and / or type of each subset of the polynomial, thereby accelerating the method.

[0022] For example, iteratively applying a genetic algorithm simultaneously over selected combinations of polynomial variables for each order and / or type of each subset of a polynomial comprises iteratively applying a genetic algorithm simultaneously over selected combinations of polynomial variables for each order and / or type of each subset of a polynomial on each thread and / or processor.

[0023] In this way, the genetic algorithm is applied iteratively on each thread and / or processor, thus enhancing the iterative application of the genetic algorithm simultaneously.

[0024] In one example, a genetic algorithm is iteratively applied across the polynomial variables for each order and / or type of each subset of the polynomial, and the resulting coefficients and their scores are obtained. keep Including the fact that iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of order and / or type candidate polynomials, iteratively applying the genetic algorithm across the polynomial variables for each order and / or type in each subset of the polynomials, and the resulting coefficients and their scores keep This includes conditionally and iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of candidate polynomials, including the ability to perform the following actions:

[0025] Thus, iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of candidate polynomials by order and / or type can, for example, be conditional on a predetermined threshold being met, thereby accelerating the termination of the iterations.

[0026] For example, conditionally iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of order and / or type candidate polynomials involves terminating the application of the genetic algorithm across the polynomial variables for each order and / or type in each subset of the polynomial if the respective score falls below a threshold.

[0027] In one example, the threshold is predetermined.

[0028] In one example, this method includes determining a threshold based on the previous score.

[0029] In this way, if each score does not satisfy the previous score, i.e., does not improve the fit, the application of the genetic algorithm across the polynomial variables for each order and / or type of each subset of the polynomial is terminated.

[0030] For example, the types of candidate polynomials in a set of candidate polynomials include single-variable polynomials, multi-variable polynomials, and modified forms thereof. Other polynomial types are known.

[0031] For example, the order of the candidate polynomials in the set of candidate polynomials is in the range of 1 to 100, preferably in the range of 2 to 25, more preferably in the range of 3 to 10, and most preferably in the range of 5 to 9, for example, 5, 6, 7, 8, 9.

[0032] For example, a general polynomial (i.e., a general algorithm has the form of a polynomial) is of the following form:

[0033]

number

[0034] Here: α mn This represents the m coefficients required to calculate the output n, {x1…x Ni} represents the normalized input, {y1…y Ni} represents the output, p 1mn This represents the power (exponent) of the m-th term of the n-th polynomial by the x1 variable.

[0035] In one example, the best candidate polynomial is of the following form:

[0036]

number

[0037] Here:

[0038]

number

[0039]

number

[0040]

number

[0041] In one example, the order of a general polynomial is 3 or greater. In another example, the order of a general polynomial is in the range of 10 to 25, for example, 20. Surprisingly, the inventors have found that using a general algorithm with an order of about 20 accurately and sufficiently describes most air-to-air engagements at a suitable runtime for an on-air implementation. Nevertheless, general algorithms can have an order greater than 2.

[0042] In one example, step b) calculates the coefficients for each candidate polynomial that best fits that candidate polynomial to the characteristics of the weapon's performance envelope, using the least-squares error criterion, and comprises: 1) generating an initial population of candidate polynomials; 2) for each candidate polynomial, calculating a set of coefficients that fits that polynomial to the performance envelope according to one or more criteria; 3) for each set of candidate polynomials and coefficients, calculating a score function indicating the quality of the fit of that set of candidate polynomials and coefficients to the performance envelope; and 4) recursively applying a genetic algorithm to the set of candidate polynomials until one or more criteria are met, including retaining at least the best-scoring polynomials and discarding the other polynomials. In one example, the output of the retained polynomials is a layer of a self-organizing polynomial neural network, which is used to provide input for creating higher-order candidate polynomials. In one example, these steps are iterated over the higher-order candidate polynomials. In one example, the final result is obtained from the path that ends with the best candidate score.

[0043] In one example, the target is equipped with and / or is an aircraft. In another example, the feasibility indication shows the successful launch zone for the aircraft and / or the target.

[0044] In one example, the target includes and / or is a ground-based target. In another example, the feasibility indication shows the aircraft's launch area and / or the target's missile engagement zone.

[0045] For example, step b) calculates the coefficients for each candidate polynomial that best fits the characteristics of the weapon's performance envelope, using the least-squares error criterion. The process involves generating an initial population of candidate polynomials, For each candidate polynomial, calculate a set of coefficients that fit the polynomial to the performance envelope according to one or more criteria (e.g., the least squares criterion), For each set of candidate polynomials and coefficients, calculate a score function that indicates the quality of the fit of that set of candidate polynomials and coefficients to the performance envelope. The genetic algorithm is applied recursively to the set of candidate polynomials until one or more criteria are met, including retaining at least the best-scoring polynomial and discarding other polynomials. It is equipped with.

[0046] In one example, the output of the retained polynomial(s) is a layer of a self-organizing polynomial neural network, used to provide input for constructing higher-order candidate polynomials. In one example, these are iterated over until the final result with the best candidate score is obtained.

[0047] In one example, the performance envelope of a weapon is the minimum envelope that defines the performance of the weapon when it is mounted on an aircraft, e.g., the minimum envelope that defines the performance of the weapon. In another example, the performance envelope of a weapon is the performance of the weapon when it is mounted on an aircraft of a different aircraft type. In another example, the method comprises obtaining the respective performance envelopes for one or more different aircraft types, e.g., multiple different aircraft types.

[0048] In one example, the method comprises determining a performance envelope using multiple aircraft performance envelopes, including determining a performance envelope that defines all the performance of different aircraft types (i.e., a "maximum aircraft performance envelope"), and using the performance envelopes and weapon performance envelopes representing all the performance of different aircraft types, determining a performance envelope that defines the performance of a weapon when it is implemented on each of the different aircraft types. In one example, the performance envelope is the smallest-sized envelope that defines the performance of a weapon when it is implemented on each of the different aircraft types.

[0049] In some embodiments, the database is generated by defining the range of conditions under which a weapon may be required to be fired, the range of aircraft conditions under which an aircraft can fire a weapon, and the range of weapon conditions under which a weapon can fire; generating data showing weapon performance for each weapon firing possibility from within the defined ranges; and creating a database that defines the overall performance envelope of the weapon. Coefficients can then be determined from this database and a general polynomial. In this way, the database can be generated on a ground-based system, and therefore the aircraft system only needs capacity to store a general polynomial and process the coefficients using aircraft and target conditions in order to generate the feasibility representation. As a result, the amount of data storage / processing capacity required on the aircraft tends to be reduced.

[0050] The coefficients can be implemented as loadable data to enable accurate and precise weapon behavior within the weapon system. Furthermore, using only one or a few common algorithms would allow different weapon systems to be authorized or certified / approved for use with aircraft with reduced effort and more quickly than the extensive testing required by conventional approaches.

[0051] The step of uploading the generated coefficients to the aircraft may be performed when the weapon is loaded as an aircraft containment. When a new weapon containment is loaded, the coefficients associated with that weapon may be uploaded to the aircraft at the same time as the weapon in order to integrate the weapon with the aircraft targeting system. Preferably, the coefficients are stored on a hardware device along with the weapon, and the device is connected to the aircraft to upload the coefficient data when the weapon is loaded.

[0052] According to a second aspect of the present invention, a weapon carried on an aircraft targets a target Successfully engage in combat The feasibility and / or the weapons that can be delivered on the target are with aircraft and Successfully engage in combat A system is provided for generating a feasibility statement in an aircraft in flight, the system comprising a first computer having memory and a processor and located away from the aircraft, and a second computer having memory and a processor and located on board the aircraft. The first computer was, To provide a database that describes the performance envelope of weapons, a) Generate candidate polynomials, where the variables of the polynomials are some or all of the group of firing condition parameters for weapons or aircraft. b) For each candidate polynomial, use the least-squares error criterion to calculate the coefficients for the candidate polynomial that best fits the characteristics of the weapon's performance envelope, c) For each candidate polynomial, a candidate score is generated according to the quality of the fit of that candidate polynomial to the characteristics of the weapon's performance envelope, d) Apply a genetic algorithm to the candidate polynomials and scores, including selecting the best-scoring polynomial(s) and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. e) Repeat the identification process until all required properties of the performance envelope have corresponding polynomial models. The steps include creating the coefficient characteristics of its performance envelope using a general algorithm, where the general algorithm has the form of a polynomial, Upload the coefficients of the best identified candidate polynomial to the second computer. The second computer is configured to do the following: A reconfigurator containing the same general algorithm selects coefficients for the general algorithm according to the aircraft and target conditions, Using the selected coefficients, the reconstructor generates a feasibility representation. In a system configured to perform the following, step d) applying a genetic algorithm to candidate polynomials and scores is: i) Define a set of order and / or type of candidate polynomials, and divide the defined set of order and / or type into multiple subsets, ii) Iteratively apply the genetic algorithm across the polynomial variables for each order and / or type of each subset of the polynomial, and obtain the resulting coefficients and their scores. keep This includes iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of candidate polynomials by order and / or type, iii) keep Using the selected coefficients and scores, select the polynomial(s) with the best score, discard the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. It is characterized by having the following features.

[0053] In one example, the system includes a display for showing feasibility.

[0054] According to a third aspect of the concept of the present invention, an aircraft is provided which is equipped with a second computer according to the second aspect.

[0055] A fourth aspect of the present invention provides a computer comprising a processor and memory, configured to carry out the method according to the first aspect.

[0056] According to a fifth aspect of the present invention, a computer program is provided which, when executed by a computer having a processor and memory, provides instructions for causing the computer to perform the method according to the first aspect.

[0057] According to a sixth aspect of the present invention, a non-transient computer-readable storage medium is provided which, when executed by a computer having a processor and memory, provides instructions for causing the computer to perform the method according to the first aspect.

[0058] Herein, embodiments of the present invention will be described only by reference to the drawings. [Brief explanation of the drawing]

[0059] [Figure 1A] A schematic diagram of the firing range (LAR) for air-to-ground weapons is shown. [Figure 1B] A schematic diagram of the firing range (LAR) for air-to-ground weapons is shown. [Figure 2] A schematic diagram of the successful launch zone (LSZ) for air-to-air weapons is shown. [Figure 3] A schematic diagram of a system according to an illustrative embodiment is provided. [Figure 4] The system shown in Figure 3 is illustrated in more detail. [Figure 5] Figure 3 provides a more detailed schematic diagram of the system, illustrating the configuration of SOPNN. [Figure 6] Figure 3 provides a more detailed schematic diagram of the system, illustrating the parallelization of GA-SOPNN across many polynomial orders and input parameters. [Figure 7] The system shown in Figure 3 is illustrated in more detail. [Modes for carrying out the invention]

[0060] Figure 1A schematically illustrates the LAR in the flight plane of the launch aircraft 1 flying along the flight path 3 with respect to the target 5 for an air-to-ground weapon (not shown) loaded on the aircraft. The LAR is calculated to provide in the launch aircraft 1 a cockpit display regarding the feasibility of the situation and the firing opportunity. Figure 1B schematically illustrates the display generated for the LAR of Figure 1A, which is in the form of a downrange and crossrange display (shaded area), where the weapon flight path 7 coincides with the aircraft flight path 3 and the target 5 as shown in the display and Successfully engage in combat For this, the target must enter inside the shaded LAR. When the aircraft 1 moves in the downrange direction, the displayed LAR is the minimum range R min and the maximum range R max which is bounded.

[0061] In addition to the LAR for the launch aircraft 1, a missile engagement zone (MEZ) for the target 5 can be determined and displayed to the pilot of the aircraft 1. This MEZ can indicate the area where the surface-to-air weapon (e.g., missile) carried by the target 5 has a probability above the threshold of hitting the aircraft 1 They successfully intercepted it. where the probability exceeds the threshold.

[0062] The LSZ shown in Figure 2 is the area where the probability that an air-to-air weapon hits the airborne target T exceeds the threshold level. The calculation of the LSZ is more complex than that of the LAR because more factors are involved, such as the relative speed and direction of the progress of the launch aircraft and the target, and the relative speed and direction of the progress of the weapon with respect to the target. Also, the shape of the LSZ is more complex than that of the LAR. Similar to the LAR, within it the target T Successfully engage in combat The maximum range R min and the minimum range R max exist, but considering the speed and direction of the progress of the launch aircraft and the target T, there is a zone bounded by R Successfully engage in combat because when the launch aircraft is very close to the target, the target T is outside the range of the ability to operate the weapon to hit the target min There is a zone bounded by. In this example, the LSZ is the so-called "inescapable range" RNE R NE and R min The zone demarcated by is where target T is weaponized. Successfully avoided This is the zone where the probability falls below a threshold probability. This range can be determined using the performance parameters of the weapon, the launch aircraft 1, and the target T. As is known in the art, there are two LSZs, one for the launch aircraft to engage target T and the other for the target to engage the launch aircraft.

[0063] In many cases, it is necessary to calculate the LAR or LSZ for an engagement in order to display information to the launch aircraft crew regarding the feasibility or likelihood of success of the engagement, and to assist in firing control and guidance decisions. The conventional approach is to create a simple abstract model of the weapon with parameters defined by the launch conditions, which is then mounted on the launch aircraft and used to generate the LAR, LSZ, or MEZ and appropriate representations.

[0064] Traditional weapon targeting processes typically involve custom design, implementation, and certification for every weapon / platform combination. This custom approach is extremely costly and time-intensive. Even slight variations in weapon performance trigger complex loops around the custom process. Such processes have also tended to have limited computing capacity. However, today's platforms exceed critical capacity limits. To improve weapon integration time and affordability, the inventors developed a Data Driven Weapon Integration (DDWI) approach that uses a general algorithm that can be "customized" for a specific weapon by using a unique set of data coefficients. In doing so, DDWI eliminates the reliance on custom models for weapon targeting. It reduces the lifecycle cost of avionics equipment and improves the time scale significantly. keepThis brings about flexibility in services for "adjusting" and / or "sanitizing" weapon targeting performance. Mission data coefficients uploaded to the platform are derived from advanced multidimensional weapon models. By performing parallel computing on multi-core computers, GPUs, and computer clusters, the inventors solve such computational and data-intensive problems, freeing up more performance and processing time reductions.

[0065] Figure 3 schematically illustrates the system according to an illustrative embodiment. The DDWI has three elements: • Adaptation toolset, office-based design tools for mission systems engineers to translate weapon truth data into a set of weapon coefficients. • Data load coefficient, a common format which may be software build / theater / mission data loaded in different platforms for the same weapon, and A common onboard algorithm that can represent any weapon on any platform and is weaponized only when a coefficient is loaded.

[0066] Figure 4 provides a more detailed schematic diagram of the system shown in Figure 3, divided into process 11, which is carried out on the ground, and process 13, which is carried out on the launch aircraft 1. The system involves a weapon delivered on an aircraft to a target. Successfully engage in combat The feasibility and / or the weapons that can be delivered on the target are with aircraft and Successfully engage in combat The system is for generating a feasibility statement in an aircraft in flight, and comprises a first computer, which has memory and a processor and is located away from the aircraft, and a second computer, which has memory and a processor and is mounted on the aircraft.

[0067] The first computer 11 provides a database describing the performance envelope of a weapon, a) generates candidate polynomials, where the variables of the polynomials are some or all of a group of firing condition parameters for a weapon or aircraft, b) for each candidate polynomial, calculates the coefficients for the candidate polynomial that best fits the characteristics of the weapon's performance envelope using the least-squares error criterion, c) generates a candidate score for each candidate polynomial according to the quality of its fit to the characteristics of the weapon's performance envelope, and d) selects the polynomial(s) with the best score. Step d) of applying the genetic algorithm to the candidate polynomials and scores, including selecting and discarding other polynomials(s), thereby identifying the best candidate polynomial and its coefficients, and creating the coefficient properties of the performance envelope using a general algorithm, which includes the step of repeating the identification process until all the required properties of the performance envelope have corresponding polynomial models, where the general algorithm has the form of a polynomial and is configured to upload the coefficients of the identified best candidate polynomial to a second computer. Step d) of applying the genetic algorithm to the candidate polynomials and scores is configured to i) define a set of order and / or type of the candidate polynomials and divide the defined set of order and / or type into multiple subsets, and ii) iteratively apply the genetic algorithm over the polynomial variables for each order and / or type of each subset of the polynomials, and the resulting coefficients and their scores keep This includes iteratively applying the genetic algorithm simultaneously across multiple subsets of a defined set of order and / or type of candidate polynomials, and iii) keep The method comprises selecting the polynomial(s) with the best score using the selected coefficients and scores, discarding the other polynomial(s), and thereby identifying the best candidate polynomial and its coefficients.

[0068] The second computer 13 is configured to use a reconfigurator containing the same general algorithm to select coefficients for the general algorithm according to the conditions of the aircraft and the target, and to use the selected coefficients to generate a feasibility representation using the reconfigurator.

[0069] More specifically, the core of DDWI is the offline coefficient generator 21. The coefficient generator 21 identifies coefficients for a general algorithm to "fit" the general algorithm to the performance envelope linearity. The form of the general algorithm is usually predetermined, for example, an arbitrary polynomial of degree (i.e., order) up to n. The coefficient generator 21 receives the true performance envelope and calculates coefficients for the general algorithm. The coefficients "fit" the general algorithm to the performance envelope linearity.

[0070] The estimation and fitting process uses a genetic algorithm for a self-organizing polynomial neural network approach. It computes a set of coefficients that will allow the geometric shape of the LAR / LSZ region to be modeled (and subsequently reconstructed) by a standard polynomial "algorithm" (see Figure 5). It employs an evolutionary technique called a genetic algorithm as the central mechanism for the self-organizing polynomial neural network (GA-SOPNN), automating the derivation of coefficients for several polynomial models within each layer. This process involves the following steps: 1. Create an initial population of candidate polynomials of different orders, each having an input that contains some or all of the firing parameters, where each polynomial function is a unique solution to the problem. 2. Using the least squares error criterion, calculate coefficients for fitting these candidates to the weapon performance envelope for selected LAR / LSZ characteristics. 3. Calculate the score function for each candidate, 4. Recursively improve this population using a genetic algorithm, a) Keep the best score candidates, b) Reject the worst candidate, c) "Grow" a new population with randomly selected combinations of features extracted from the best group. 5. Repeat until improvement stops or the accuracy criteria are met. 6. The result is the first layer of a self-organizing polynomial neural network (SOPNN), 7. Each subsequent layer of SOPNN takes the best output of the previous layer as its input, and then proceeds as described above. 8. The effect is to create higher-order candidate polynomials for consideration, 9. Optimization within the new layer uses the same genetic algorithm as before. 10. Layers will be added until improvement stops or the maximum layer set by the user is reached. 11. Only the single best polynomial and coefficient set of interest, i.e., the output of the final layer with the best score. 12. All other outputs of the final layer are rejected. 13. All nodes in the lower layers that do not contribute to the best polynomial are rejected.

[0071] To apply the genetic algorithm to a specific application, an internal representation of the space to be explored is selected, and an external function is defined that assigns goodness-of-fit values ​​to candidate solutions. As shown in Figure 6, the inventors have adapted the genetic algorithm to be parallelized across many polynomial orders and input parameters that the genetic algorithm must perform. Each run tests several combinations of polynomial order and input parameters and reports on the execution time, memory, and flop operation for the final model requirements to solve the problem for each parameter combination, as described in more detail below.

[0072] In this example, the polynomial is parameterized into a string of binary strings with three parameters (also known as subchromosomes): The first parameter is the order, The second parameter is the number of inputs. The third parameter identifies which input is used by the polynomial.

[0073] In this example, iteratively applying the genetic algorithm across the polynomial variables for each order and / or type of each subset of the polynomial is: Selecting combinations of polynomial variables for each order and / or type of each subset of the polynomial, The iterative application of a genetic algorithm across selected combinations of polynomial variables for each order and / or type of each subset of the polynomial, and It is equipped with.

[0074] In this example, the genetic algorithm starts with an initial population of randomly generated strings, and therefore, a corresponding initial population of randomly generated polynomials (order, input). Each polynomial is scored using least-squares fit, and the best polynomial is selected. These best polynomials are mutated by switching to modify the binary in the corresponding string (order, input). The modified string is used to generate new polynomials in a new layer of the SOPNN. The order of the polynomials increases from layer to layer.

[0075] In this example, iteratively applying a genetic algorithm over selected combinations of polynomial variables for each order and / or type of each subset of the polynomial comprises simultaneously iteratively applying a genetic algorithm over selected combinations of polynomial variables for each order and / or type of each subset of the polynomial.

[0076] In this example, iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of candidate polynomials with order and / or type comprises iteratively applying a genetic algorithm simultaneously across multiple subsets of a defined set of candidate polynomials with order and / or type on each processor and / or different threads.

[0077] In this example, iteratively applying a genetic algorithm simultaneously over selected combinations of polynomial variables for each order and / or type of each subset of the polynomial comprises iteratively applying a genetic algorithm simultaneously over selected combinations of polynomial variables for each order and / or type of each subset of the polynomial on each thread and / or processor.

[0078] In this example, the maximum polynomial order is capped, and the maximum number of inputs is capped. Capping the maximum order and the maximum number of inputs means that randomly generated polynomials during GA training can have any order and inputs up to their maximum values. In this example, each coefficient for each polynomial order is calculated by an individual processor. In this example, each coefficient for each input for each polynomial order is calculated by an individual processor.

[0079] Parallelizing the operation of GA-SOPNN itself will allow / unlock more performance, as shown in Figure 7. By assigning NUM_THREADS to each process, individual solutions within the population can be partitioned among threads. For example, in a GA population of 100 individuals and 4 CPU cores, 100 iterations can be partitioned into 4, so thread 0 performs 1-25 iterations, thread 1 performs 26-50 iterations, and so on, which results in the GA-SOPNN algorithm working approximately four times faster.

[0080] More specifically, processes 11 and 13 begin with the generation of a data space which is the range of conditions under which the weapon performance envelope should be defined, which is done by the data space generator 15 and depends on the range of conditions under which it is necessary to fire the weapon (defined by the weapon user / operator), is feasible to fire according to the launch aircraft capability, and is feasible to fire according to the weapon capability / performance.

[0081] In this example, the data space generator 15 contains data describing performance parameters for each of several different aircraft types. Different types of aircraft may have different capabilities from one another; for example, aircraft with the same or similar capabilities may be considered the same “aircraft type”. Different types of aircraft may be different models or variants and / or may have different manufacturers. Different types of aircraft may have different operating parameters (maximum speed, maximum altitude, g limit, etc.). Different types of aircraft may be configured for different purposes or functions (e.g., bombers, fighters, refueling aircraft, etc.). These aircraft performance envelopes may be supplied by the aircraft manufacturer or through testing. Several different aircraft types include the type of launch aircraft 1 and preferably the type of target aircraft T. Performance parameters for each aircraft type may include, but are not limited to, the maximum achievable altitude, the maximum achievable g force, and the maximum achievable climb angle. The values ​​of the performance parameters for different types of aircraft may differ from one another. For example, a first type of aircraft may have a maximum altitude of 45,000 feet, while a second type of aircraft may have a maximum altitude of 55,000 feet.

[0082] In this example, the data space generator 15 further includes data describing performance parameters for each of several different weapon types, for example, different weapons that may be loaded onto a launch aircraft or that are expected to be carried by an adversarial target. These weapon performance envelopes may be supplied by the weapon manufacturer or through testing. The several different weapon types include the type of weapon carried by the launch aircraft 1, and preferably the target. The performance parameters for each weapon type may include, but are not limited to, the maximum altitude from which the weapon can be launched, the maximum g-force from which the weapon can be launched, and the weapon's release mechanism. The values ​​of the performance parameters for different types of weapons may differ from one another. For example, a first type of weapon may be capable of being launched to an altitude of 35,000 feet, while a second type of weapon may be capable of being launched to an altitude of 45,000 feet, and so on.

[0083] The data space generator 15 can define emission, weather, and commanded impact conditions for training and validation sets performed by the truth data generator 17.

[0084] The truth data generator 17 determines the weapon performance for each firing case in the data space, which depends on the weapon performance model typically provided by the weapon manufacturer.

[0085] The output of the truth data generator 17 is the truth database 19, which is a set of data specifying further weapon performance envelopes for each of several exemplary weapon firings for each weapon type. The truth data generator 17 can produce training and validation sets used by the coefficient generator 21.

[0086] Traditionally, the Truth Database has been used as a model that can be mounted on a launching aircraft to generate the feasibility of engagement indications (LAR or LSZ, as appropriate).

[0087] In this example, the coefficient generator 21 receives additional weapon performance envelopes stored in the truth database 19 and calculates coefficients for each weapon type and each exemplary weapon firing according to a general LAR / LSZ algorithm 23 that "fits" a general algorithm to the additional weapon performance envelope linearity.

[0088] This describes a method for determining coefficient values ​​that fit a general algorithm to the performance envelope of a specific weapon type and a specific example of weapon firing. In practice, it will be recognized that a set of coefficients is determined for each example of weapon firing and for each weapon type.

[0089] In this method, the coefficient generator 21 begins by creating an initial set of candidate polynomials whose variables are some or all of the weapon or aircraft firing condition parameters. Each candidate polynomial is a unique solution to the fitting problem. Some or all of the candidate polynomials may have a different order or dimension than some or all of the other candidate polynomials. For each candidate polynomial, the set of coefficients that best "fit" that candidate polynomial to the weapon performance envelope is then calculated. This can be done using the least-squares error criterion or any other fitting method. For each candidate polynomial, a score indicating the quality of this fit is then calculated.

[0090] The number of inputs (27) and the format of each polynomial descriptor, as well as the PD layer nodes, are determined by an optimization method known as the genetic algorithm. The genetic algorithm is applied to candidate polynomials and scores. In this example, the polynomial with the best score is retained, and other polynomials (i.e., those with the worst score) are rejected. New candidate polynomials with similar characteristics to the retained candidate polynomials are then created and replaced (for example, by "multiplying" or "mutating" the retained candidate polynomials). For the generation of these new candidates, a set of coefficients and score values ​​is then calculated, and so on.

[0091] The genetic algorithm is repeated until the best candidate's score is improved or some other criterion is met. The result is the first layer of the self-organizing polynomial neural network (SOPNN), i.e., layer 1.

[0092] The entire process is then repeated, with the output of the first layer providing the input to create the second layer of the SOPNN, i.e., layer 2. The new layer has the effect of generating higher-order candidate polynomials and coefficients for consideration. The selection of polynomials in the new layer is again governed and optimized by a genetic algorithm.

[0093] Layers are added to the SOPNN in this way until the score of the best candidate is improved or some other criterion is met. The completed network with two layers is shown in Figure 5. The final network is obtained recursively from paths ending with the output node that has the best score in the final generation of candidates ("optimal solution"). Any node that does not have a connection to this path is discarded as shown in Figure 5, where nodes that contribute to the optimal solution are lightly shaded and discarded nodes are black.

[0094] As described above, the inventors have adapted the genetic algorithm to be parallelized across the many polynomial orders and input parameters that the genetic algorithm must perform.

[0095] The best single candidate polynomial and coefficient set is identified and stored. This process is repeated until all required properties of the LAR / LSZ have corresponding polynomial models. In other words, this process is repeated for each firing condition and each weapon type until a polynomial model is generated that fits further weapon performance envelopes for that weapon type and firing condition.

[0096] The general LAR / LSZ algorithm is predetermined, and in this example, the general polynomial is of the following form, as previously described:

[0097]

number

[0098] In this example, the best candidate polynomial is of the following form, as previously explained:

[0099]

number

[0100] In this example, the order of a general polynomial is in the range of 10 to 25, for example, 20.

[0101] Referring again to Figure 4, the output of the coefficient generator 21 is a set of coefficients that are loaded into the launching aircraft by the data uploader. Following this step, the onboard process 13 includes a reconfigurator 25, which combines the uploaded coefficients with a general LAR / LSZ algorithm 23 (held in the aircraft system) to reconfigure the LAR, LSZ, or MEZ for a particular engagement by selecting appropriate algorithms and coefficients for the current firing conditions (i.e., weapon or aircraft firing conditions).

[0102] When a LAR, LSZ, or MEZ is reconfigured for a specific engagement by a system mounted on an aircraft, the LAR, LSZ, or MEZ is displayed by conventional means mounted on the aircraft. In this example, when launch aircraft 1 engages an adversarial target aircraft T during operation, the reconfigurator 25 mounted on launch aircraft 1 may select from uploaded coefficients that correspond to the weapon carried by launch aircraft 1 and to the relevant firing conditions (experienced altitude, angle of attack, environmental conditions, g-force, etc.). The selected coefficients may then be used to reconfigure the LSZ of launch aircraft 1 and display it to the pilot of launch aircraft 1. The reconfigured LSZ of launch aircraft 1 may also be used by other systems mounted on launch aircraft 1 to recommend action to the pilot of launch aircraft 1 (e.g., a recommendation to fire the weapon, etc.).

[0103] When launch aircraft 1 engages an adversarial target aircraft T, the aircraft type of the adversarial target T may be determined by the pilot of launch aircraft 1 (or by other means) and entered into the reconfigurator 25. The reconfigurator 25 mounted on launch aircraft 1 may then select from the uploaded coefficients the coefficients corresponding to the weapon most likely to be carried by the adversarial target T and the associated firing conditions. The selected coefficients may then be used to reconfigure the LSZ of the adversarial target T and display it to the pilot of launch aircraft 1. The reconfigured LSZ of the adversarial target T may also be used by other systems mounted on launch aircraft 1 to recommend actions to the pilot of launch aircraft 1 (e.g., a recommendation that a particular evasive maneuver be performed).

[0104] In this example, when the launch aircraft 1 engages a hostile ground target 5 during operation, the reconfigurator 25 mounted on the launch aircraft 1 may select from uploaded coefficients that correspond to the weapon carried by the launch aircraft 1 and to the relevant firing conditions (experienced altitude, angle of attack, environmental conditions, g-force, etc.). The selected coefficients may then be used to reconfigure the LAR of the launch aircraft 1 and display it to the pilot of the launch aircraft 1. The reconfigured LAR of the launch aircraft 1 may also be used by other systems mounted on the launch aircraft 1 to recommend actions to the pilot of the launch aircraft 1 (e.g., a recommendation to fire the weapon, etc.).

[0105] When launch aircraft 1 engages a hostile ground target 5, the type of ground target 5 may be determined by the pilot of launch aircraft 1 (or by other means) and entered into the reconfigurator 25. The reconfigurator 25 mounted on launch aircraft 1 may then select from the uploaded coefficients the coefficient corresponding to the weapon most likely to be carried by ground target 5 and the associated firing conditions. The selected coefficient may then be used to reconfigure the MEZ of ground target 5 and display it to the pilot of launch aircraft 1. The reconfigured MEZ of ground target 5 may also be used by other systems mounted on launch aircraft 1 to recommend actions to the pilot of launch aircraft 1 (e.g., a recommendation that a particular evasive maneuver be performed).

[0106] An apparatus for implementing the configurations described above, including any of the processors mentioned above, may be provided by configuring or adapting any suitable apparatus, for example, one or more computers or other processing units or processors, and / or by 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 computer programs or multiple computer programs stored in or on machine-readable storage media such as computer memory, computer disks, ROM, PROM, etc., or any combination thereof or other storage media. The following is a direct reproduction of the claims as originally filed. [1] A computer implementation method for generating a feasibility representation indicating the feasibility of a weapon carried on an aircraft in flight successfully engaging a target and / or the feasibility of a weapon carried on a target successfully engaging the aircraft, the method comprising providing a database describing the performance envelope of the weapon, a) Generate a candidate polynomial, where the variables of the polynomial are some or all of the group of firing condition parameters for a weapon or aircraft. b) For each candidate polynomial, calculate the coefficients for the candidate polynomial that best fits the characteristics of the performance envelope of the weapon, using the least squares error criterion. c) For each candidate polynomial, a candidate score is generated according to the quality of the fit of that candidate polynomial to the characteristics of the performance envelope of the weapon, d) Applying a genetic algorithm to the candidate polynomials and scores, including selecting the polynomial(s) with the best score and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. e) Repeat the identification process until all the required characteristics of the performance envelope have corresponding polynomial models. The steps include creating a coefficient characteristic of its performance envelope using a general algorithm, wherein the general algorithm has the form of a polynomial, uploading the coefficients of the identified best candidate polynomial to the aircraft, and selecting the coefficients for the general algorithm according to the conditions of the aircraft and the target by a reconstructor on the aircraft which includes the same general algorithm. Using the selected coefficients, the reconstructor generates the feasibility representation. In a method comprising, step d) applying the genetic algorithm to the candidate polynomial and score, i) Define a set of order and / or type of the candidate polynomial, and divide the set with defined order and / or type into multiple subsets thereof, ii) Iteratively applying the genetic algorithm simultaneously across multiple subsets of the defined set of candidate polynomials, including iteratively applying the genetic algorithm across the variables of the polynomial for each order and / or type of each subset of the polynomial, and saving the resulting coefficients and their scores, iii) Using the saved coefficients and scores, select the best-scoring polynomial(s), discard the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. A method characterized by comprising: [2] The method according to [1], wherein the genetic algorithm is applied iteratively over a plurality of subsets of the set of candidate polynomials with defined order and / or type, the genetic algorithm being applied iteratively over a plurality of subsets of the set of candidate polynomials with defined order and / or type on each processor. [3] Iteratively applying the genetic algorithm over the variables of the polynomial for each order and / or type of each subset of the polynomial is Selecting combinations of variables for the polynomial for each order and / or type of each subset of the polynomial, The genetic algorithm is iteratively applied over selected combinations of the variables of the polynomial for each order and / or type of each subset of the polynomial. The method according to [1] or [2], comprising: [4] The method according to [3], wherein iteratively applying the genetic algorithm over selected combinations of the variables of the polynomial for each order and / or type of each subset of the polynomial is simultaneously iteratively applying the genetic algorithm over selected combinations of the variables of the polynomial for each order and / or type of each subset of the polynomial. [5] The method according to [4], wherein the genetic algorithm is applied simultaneously and iteratively over selected combinations of the variables of the polynomial for each order and / or type of each subset of the polynomial, the genetic algorithm is applied simultaneously and iteratively over selected combinations of the variables of the polynomial for each order and / or type of each subset of the polynomial on each thread. [6] The method of any one of [1] to [5], comprising iteratively applying the genetic algorithm over the variables of the polynomial for each order and / or type of each subset of the polynomial, including saving the resulting coefficients and scores, and iteratively applying the genetic algorithm over the variables of the polynomial for each order and / or type of each subset of the polynomial, and conditionally iteratively applying the genetic algorithm over the multiple subsets of the set of defined orders and / or types of the candidate polynomial, including iteratively applying the genetic algorithm over the variables of the polynomial for each order and / or type of each subset of the polynomial, including saving the resulting coefficients and scores, [7] The method of [6], wherein the genetic algorithm is applied conditionally and iteratively simultaneously across a plurality of subsets of the set of defined order and / or type candidate polynomials, and the application of the genetic algorithm across the polynomial variables for each order and / or type of each subset of the polynomial is terminated if the respective score is below a threshold. [8] The threshold is determined in advance by the method of [7]. [9] The method according to [7] or [8], further comprising determining the threshold based on the previous score.

[10] The type of candidate polynomial in the set of candidate polynomials is the method described in any one of [1] to [9], including a single-variable polynomial, a multivariable polynomial, and modified forms thereof.

[11] The general polynomial is of the following form:

number

[10] .

[12] A system for generating feasibility indicators in flight of an aircraft indicating the feasibility of a weapon carried on an aircraft successfully engaging a target and / or the feasibility of a weapon carried on the target successfully engaging the aircraft, the system comprising a first computer having a computer, i.e., memory and a processor, and being located away from the aircraft, and a second computer having a computer having a computer, i.e., memory and a processor, and being mounted on the aircraft, The aforementioned first computer, To provide a database describing the performance envelope of the aforementioned weapon, a) Generate a candidate polynomial, where the variables of the polynomial are some or all of the group of firing condition parameters for a weapon or aircraft. b) For each candidate polynomial, calculate the coefficients for the candidate polynomial that best fits the characteristics of the performance envelope of the weapon, using the least squares error criterion. c) For each candidate polynomial, a candidate score is generated according to the quality of the fit of that candidate polynomial to the characteristics of the performance envelope of the weapon, d) Applying a genetic algorithm to the candidate polynomials and scores, including selecting the polynomial(s) with the best score and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. e) Repeat the identification process until all the required characteristics of the performance envelope have corresponding polynomial models. The steps include creating the coefficient characteristics of its performance envelope using a general algorithm, wherein the general algorithm has the form of a polynomial, and uploading the coefficients of the identified best candidate polynomial to the second computer. The second computer is configured to perform the following: A reconfigurator containing the same general algorithm selects coefficients for the general algorithm according to the conditions of the aircraft and the target, Using the selected coefficients, the reconstructor generates the feasibility representation. In a system configured to perform the following, step d) applying the genetic algorithm to the candidate polynomial and score is: i) Define a set of order and / or type of the candidate polynomial, and divide the set with defined order and / or type into multiple subsets thereof, ii) Iteratively applying the genetic algorithm simultaneously across multiple subsets of the defined set of candidate polynomials, including iteratively applying the genetic algorithm across the variables of the polynomial for each order and / or type of each subset of the polynomial, and saving the resulting coefficients and their scores, iii) Using the saved coefficients and scores, select the best-scoring polynomial(s), discard the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. A system characterized by comprising the following features.

[13] The system according to

[12] , further comprising a display for displaying the feasibility indication.

[14] An aircraft equipped with a second computer as described in

[12] or

[13] .

[15] A computer having a processor and memory configured to perform the method described in any one of [1] to

[11] , a computer program having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in any one of [1] to

[11] , or a non-transient computer-readable storage medium having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in any one of [1] to

[11] .

Claims

1. A computer implementation method for generating a feasibility display indicating the feasibility of a weapon carried on an aircraft successfully engaging a target and / or the feasibility of a weapon carried on a target successfully engaging the aircraft, wherein the method is: To provide a database describing the performance envelope of the aforementioned weapon, Creating the coefficient characteristics of its performance envelope using a general algorithm, wherein the general algorithm has the form of a polynomial, and the creation is: a) To generate a candidate polynomial, where the variables of the polynomial are some or all of the group of firing condition parameters for a weapon or aircraft. b) For each candidate polynomial, calculate the coefficients for the candidate polynomial that best fits the characteristics of the performance envelope of the weapon, using the least squares error criterion. c) For each candidate polynomial, a candidate score is generated according to the quality of the fit of that candidate polynomial to the characteristics of the performance envelope of the weapon, d) Applying a genetic algorithm to the candidate polynomials and scores, including selecting the best-scoring polynomial(s) and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. e) Repeating the identification process until all the required characteristics of the performance envelope have corresponding polynomial models. A step including, Uploading the coefficients of the identified best candidate polynomial to the aircraft, A reconfigurator on the aircraft, which includes the same general algorithm, selects coefficients for the general algorithm according to the conditions of the aircraft and the target, Using the selected coefficients, the reconstructor generates the feasibility representation. Step d), which includes applying the genetic algorithm to the candidate polynomial and score, i) Define a set of degree and / or type of the candidate polynomial, and divide the set with defined degree and / or type into multiple subsets, wherein the degree is in the range of 5 to 9. ii) Iteratively applying the genetic algorithm over the variables of the polynomial for each degree and / or type of each subset of the polynomial, including storing the resulting coefficients and their scores, and simultaneously iteratively applying the genetic algorithm over the defined subset of the candidate polynomials. iii) Using the saved coefficients and scores, select the polynomial(s) with the best score, discard the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. A method that includes [a certain feature].

2. The method according to claim 1, wherein iteratively applying the genetic algorithm simultaneously across a plurality of subsets of the set of candidate polynomials with defined degrees and / or types comprises iteratively applying the genetic algorithm simultaneously across a plurality of subsets of the set of candidate polynomials with defined degrees and / or types on each processor.

3. Applying the genetic algorithm iteratively across the variables of the polynomial for each degree and / or type of each subset of the polynomial is: Selecting combinations of variables for the polynomial for each degree and / or type of each subset of the polynomial, The genetic algorithm is applied iteratively across selected combinations of the variables of the polynomial for each degree and / or type of each subset of the polynomial. The method according to claim 1, comprising:

4. The method according to claim 3, wherein iteratively applying the genetic algorithm over selected combinations of the variables of the polynomial for each degree and / or type of each subset of the polynomial comprises simultaneously iteratively applying the genetic algorithm over selected combinations of the variables of the polynomial for each degree and / or type of each subset of the polynomial.

5. The method according to claim 4, wherein iteratively applying the genetic algorithm simultaneously over selected combinations of the variables of the polynomial for each degree and / or type of each subset of the polynomial comprises iteratively applying the genetic algorithm simultaneously over selected combinations of the variables of the polynomial for each degree and / or type of each subset of the polynomial on each thread.

6. The method according to claim 1, comprising iteratively applying the genetic algorithm over the variables of the polynomial for each degree and / or type of each subset of the polynomial, and storing the resulting coefficients and their scores, to simultaneously iteratively applying the genetic algorithm over the plurality of subsets of the set of defined degree and / or type of the candidate polynomial, to conditionally iteratively applying the genetic algorithm over the variables of the polynomial for each degree and / or type of each subset of the polynomial, and storing the resulting coefficients and their scores, to simultaneously iteratively applying the genetic algorithm over the plurality of subsets of the set of defined degree and / or type of the candidate polynomial, to simultaneously iteratively applying the genetic algorithm over the variables of the polynomial for each degree and / or type of each subset of the polynomial, and storing the resulting coefficients and their scores.

7. The method according to claim 6, wherein the conditional iterative application of the genetic algorithm simultaneously across a plurality of subsets of the set of defined degree and / or type candidate polynomials is terminated when the respective scores fall below a threshold, by applying the genetic algorithm across the variables of the polynomial for each degree and / or type of each subset of the polynomial.

8. The method according to claim 7, wherein the threshold is predetermined.

9. The method according to claim 7, further comprising determining the threshold based on the previous score.

10. The method according to claim 1, wherein the type of candidate polynomial in the set of candidate polynomials includes a single-variable polynomial, a multivariable polynomial, and modified forms thereof.

11. The aforementioned polynomial is of the following form: 【Number 1】 Here: α mn This represents the m coefficients required to calculate the output n, {x 1 ...x Ni } represents the normalized input, {y 1 ...y Ni } represents the output, p 1mn is the x of the mth term of the nth polynomial. 1 The method according to claim 1, which represents the exponent of a variable.

12. A system for generating a feasibility indicator in a flying aircraft showing the feasibility of a weapon carried on the aircraft successfully engaging a target and / or the feasibility of a weapon carried on the target successfully engaging the aircraft, the system comprising a first computer having a computer, i.e., memory and a processor, located away from the aircraft, and a second computer having a computer having a computer and a processor, mounted on the aircraft, The first computer is, To provide a database describing the performance envelope of the aforementioned weapon, Creating the coefficient characteristics of its performance envelope using a general algorithm, wherein the general algorithm has the form of a polynomial, and the creation is: a) To generate a candidate polynomial, where the variables of the polynomial are some or all of the group of firing condition parameters for a weapon or aircraft. b) For each candidate polynomial, calculate the coefficients for the candidate polynomial that best fits the characteristics of the performance envelope of the weapon, using the least squares error criterion. c) For each candidate polynomial, a candidate score is generated according to the quality of the fit of that candidate polynomial to the characteristics of the performance envelope of the weapon, d) Applying a genetic algorithm to the candidate polynomials and scores, including selecting the best-scoring polynomial(s) and discarding the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. e) Repeating the identification process until all the required characteristics of the performance envelope have corresponding polynomial models. A step including, Uploading the coefficients of the identified best candidate polynomial to the second computer. It is configured to do the following: The second computer described above is A reconfigurator containing the same general algorithm selects coefficients for the general algorithm according to the conditions of the aircraft and the target, Using the selected coefficients, the reconstructor generates the feasibility representation. Step d), which is configured to perform the following, and applies the genetic algorithm to the candidate polynomial and score, i) Define a set of degree and / or type of the candidate polynomial, and divide the set with defined degree and / or type into multiple subsets, wherein the degree is in the range of 5 to 9. ii) Iteratively applying the genetic algorithm over the variables of the polynomial for each degree and / or type of each subset of the polynomial, including storing the resulting coefficients and their scores, and simultaneously iteratively applying the genetic algorithm over the defined subset of the candidate polynomials. iii) Using the saved coefficients and scores, select the polynomial(s) with the best score, discard the other polynomial(s), thereby identifying the best candidate polynomial and its coefficients. A system that includes these features.

13. The system according to claim 12, further comprising a display for displaying the aforementioned feasibility indication.

14. A computer comprising a processor and memory configured to carry out the method described in claim 1.

15. A computer program that, when executed by a computer having a processor and memory, comprises instructions causing the computer to perform the method according to claim 1.

16. A non-transient computer-readable storage medium comprising an instruction, which, when executed by a computer having a processor and memory, causes the computer to perform the method according to claim 1.

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