Guidance situation assessment method based on data driving

Abstract

The invention discloses a guidance situation assessment method based on data driving, which comprises the following steps: in an inertial coordinate system, taking an aircraft as a mass point, and establishing a reachable set and an isotimeline model of missile and target parties in a guidance process; according to the reachable set and an isotimeline model, calculating an isotimepoint set for fitting to obtain an isotimeline curve meeting fitting indexes, and taking the isotimeline curve as sample data; establishing a neural network model, training the neural network model through the sample data, and generating a dynamic isotimeline by using the trained neural network model according to the performance parameters and the position information of the two parties under the current situation; and evaluating the guidance situation according to the generated isotimeline. Through the data driving technology, the model can be continuously optimized through continuous learning, and the model trained through supervised learning can more accurately reflect the complex and changeable combat situation, so that the accuracy of situation assessment is improved, and the response speed and combat effectiveness of a command and control system are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the field of guidance and control of aerospace vehicles and relates to a guidance situation assessment method based on data driving. Background Art

[0002] The equal-time line (ETL) is a continuous curve formed by the intersection points of the two aircraft when they fly along the Dubins trajectory. It describes the set of all possible positions that take the same time to reach the target position when flying along the Dubins path from the starting point under a given speed limit and aircraft maneuverability. The equal-time line is of great significance for situation assessment and the formulation of interception / penetration strategies, and can also provide important reference information for the design of collaborative guidance laws. In complex interception / penetration scenarios, the use of equal-time lines can provide accurate predictions of the areas that the attacker may pass through in a certain period of time in the future, which can effectively predict the future position of the attacker and the possible intersection points of the two parties, so that the defender can make arrangements in advance, thereby formulating more effective guidance strategies, optimizing interception paths, and even implementing pre-judgment strikes. By comparing the overlapping areas of the equal-time lines of both parties, the relative maneuverability advantages and disadvantages of the warring parties can be evaluated, providing a scientific basis for command decisions.

[0003] In practical applications, the calculation of isotimelines needs to be highly real-time in order to cope with the ever-changing situation on the battlefield. However, the current calculation of isotimelines usually uses a numerical iteration method, which has high computational cost and low efficiency, making it difficult to apply in modern air combat scenarios. Therefore, in-depth research and optimization of isotimelines can overcome the limitations of isotimeline applications and help improve the guidance performance of existing guidance strategies under complex geometric situations. Summary of the invention

[0004] The purpose of the present invention is to provide a guidance situation assessment method based on data-driven, so as to solve the problem that the existing equal time line calculation efficiency is low and thus difficult to apply in practice.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A data-driven guidance situation assessment method, comprising: In the inertial coordinate system, the aircraft is taken as a mass point, and the reachable set and equal time line model of both the missile and the target are established during the guidance process. According to the reachable set and the isotimeline model, the isotime point set is calculated for fitting, and the isotime curve that meets the fitting index is obtained as the sample data; Establish a neural network model and train it with sample data. Based on the performance parameters and location information of both parties under the current situation, use the trained neural network model to generate dynamic timelines. Based on the generated isotimeline, the guidance situation is evaluated.

[0006] Furthermore, the kinematic models of both the missile and the target during the guidance process are:

[0007]

[0008]

[0009] Among them, the subscript Indicates aircraft T or aircraft M, Indicates speed, represents the heading angle, represents the normal acceleration, represents the maximum normal acceleration of the aircraft T, represents the maximum normal acceleration of the aircraft M.

[0010] Furthermore, the dynamic model of both the missile and the target during the guidance process is:

[0011] in, and Represents the state vector and control input, then The state vector at the moment is: .

[0012] Furthermore, the reachable set is

[0013] in, represents the initial position, Indicated in The state vector at time, Indicates the time range [ ] within the continuous control instructions, represents all feasible control instructions within the constraints.

[0014] Furthermore, the isotimeline model is

[0015] in, represents the speed of the aircraft M, represents the speed of the aircraft T, represents the maximum normal acceleration of the aircraft M, represents the maximum normal acceleration of the aircraft T, express The position of aircraft M at time express The position of the aircraft T at time instant, Indicates the earliest interception time.

[0016] Furthermore, the fitting method of the isochronous curve is: Taking the earliest equal time point of the missile and the target as the starting time, calculate the intersection of the frontiers of the reachable sets of the missile and the target as the equal time point set in the guidance scenario at this time, and obtain the equal time curve by polynomial fitting of the equal time point set; The polynomial fitting includes quadratic polynomial fitting and linear polynomial fitting.

[0017] Furthermore, the neural network model is

[0018] in, represents the sight angle of the aircraft T, represents the velocity lead angle of the aircraft T, represents the velocity lead angle of the aircraft M, Indicates the relative distance between the two aircrafts. represents the speed of the aircraft T, and Represents the network structure.

[0019] Furthermore, the neural network model is a multi-layer perceptron structure, which includes an input layer, an output layer and several hidden layers.

[0020] Furthermore, each of the input layer, the output layer and the plurality of hidden layers includes a plurality of neurons, each of the neurons is connected to each neuron in the next layer, and the connections between the neurons are weighted.

[0021] Furthermore, the performance parameters and position information of both parties under the current situation include the relative distance between the two aircrafts, the positions of the two aircrafts, the speed lead angles of the two aircrafts, the sight angles of the two aircrafts and the heading angles of the two aircrafts.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a guidance situation assessment method based on data-driven. Based on the equal timeline calculation method, data-driven technology is used to obtain data samples by extracting features from the equal timeline, and then supervised learning is performed on the data samples. The nonlinear relationship between multiple characteristic parameters of the equal timeline and the performance parameters of both parties is obtained by the data-driven method, and then polynomial fitting is performed on the geometric shape of the equal timeline according to a large amount of sample data. In actual application, by obtaining the situation information of both parties, the equal timeline can be quickly fitted through a trained model, thereby realizing fast and accurate guidance situation assessment. The present invention can automatically learn and extract the nonlinear relationship between multiple characteristic parameters of the equal timeline and the performance parameters of both parties from a large amount of data through data-driven technology. With the continuous accumulation and updating of data, the model can be continuously optimized through continuous learning and automatically adapt to changes in combat situation. Using the model trained by supervised learning, in actual application, only the situation information of both parties needs to be input, and the equal timeline can be quickly fitted through the model, which can more accurately reflect the complex and changeable combat situation, thereby improving the accuracy of situation assessment and significantly improving the response speed and combat effectiveness of the command and control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a schematic diagram of the situation parameters of both the missile and the target during the guidance process of the present invention.

[0025] Figure 2 It is a schematic diagram of the reachable set of the uniform motion aircraft of the present invention.

[0026] Figure 3 It is a schematic diagram of the earliest time point of the present invention.

[0027] Figure 4 It is a schematic diagram of the isochronous timeline of the present invention.

[0028] Figure 5 A flow chart was generated for the sample of the present invention.

[0029] Figure 6 It is a schematic diagram of the neural network model structure of the present invention.

[0030] Figure 7 The present invention is a flow chart of the data-driven guidance situation assessment method.

[0031] Figure 8It is a fitting effect diagram of the training set and the test set in Example 1 of the present invention.

[0032] Fig. 9 Root mean square error diagram of the training set and the test set in Example 1 of the present invention.

[0033] Fig.10 This is a graph of the network fitting mean square error of the training set and the test set in Example 1 of the present invention.

[0034] Fig.11 This is a schematic diagram of the isochronous timeline generated in Example 1 of the present invention.

[0035] Fig.12 This is a comparison diagram of the isotimelines generated by the data-driven method and the isotimelines generated by the numerical method in Example 1 of the present invention.

[0036] Fig.13 This is a schematic diagram of iso-timelines generated in a two-to-one interception scenario in Example 1 of the present invention.

[0037] Fig.14 This is a diagram of the interception effect when flying at an initial heading angle of 95° in Example 1 of the present invention.

[0038] Fig.15 This is a diagram of the interception effect when flying at an initial heading angle of 90° in Example 1 of the present invention.

[0039] Fig.16 It is a schematic diagram of the structure of a guidance situation assessment system based on data-driven according to a preferred embodiment of the present invention.

[0040] Fig.17 It is a schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0044] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0045] It should be noted that the terminals involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers, personal computers (PCs), MP3 players, MP4 players, wearable devices (for example, smart glasses, smart watches, smart bracelets, etc.), smart home devices and other smart devices.

[0046] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0047] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 The present invention provides a data-driven guidance situation assessment method, comprising the following steps: Step 1: Assume that in an inertial coordinate system In the figure, aircraft M and aircraft T are particles, and their reachable sets and equal timeline models are established.

[0048] During the guidance process, the situation parameters of both the missile and the target are as follows: Figure 1 shown.

[0049] The kinematic model of both parties is shown as follows:

[0050]

[0051]

[0052] Among them, the subscript Indicates aircraft T or aircraft M, Indicates speed, represents the heading angle, represents the normal acceleration, and They represent the maximum normal acceleration of aircraft T and aircraft M respectively. This value determines the minimum turning radius. The speed and maneuverability of aircraft T are better than those of aircraft M.

[0053] The dynamic model of both parties can be summarized as:

[0054] in, and Represents the state vector and control input, then The state vector at the moment is:

[0055] According to the kinematic equation, a certain aircraft M or aircraft T The reachable set at time is defined as Next, in The set of all possible locations that can be reached at a certain time:

[0056] in, Indicates the time range [ ] within the continuous control instructions, represents all feasible control instructions within the constraints.

[0057] Analyzing the reachable set from the Dubins shortest path trajectory, the Dubins shortest path can be divided into the following three categories: straight path, minimum turning radius turning path, minimum turning radius turning path plus straight path. The shortest path trajectory is equivalent to the shortest time trajectory. If both parties use the Dubins shortest path trajectory to move, the guidance strategy of both parties can be defined as the minimum time guidance strategy (MTG).

[0058] The reachable set front (RSF) is a curve consisting of the farthest distance points that can be reached within a period of time. The reachable set of a uniformly moving aircraft can be obtained through the involute of its left and right turning radius circles, such as Figure 2 shown.

[0059] The equal-time line (ETL) is a continuous boundary that both parties can reach at the same time when using the MTG guidance strategy. The points on the equal-time line are called equal-time points (ETP), which are obtained by calculating the intersection of the frontiers of the reachable sets of both parties. When the flight time of both parties increases from zero, there is a moment when the frontiers of the reachable sets of both parties are tangent, such as Figure 3 As shown, the earliest intersection point is the earliest equal time point.

[0060] Figure 3 middle and are the centers of the minimum turning radii of aircraft M and aircraft T respectively, and the length of the fastest interception trajectory corresponding to the earliest equal time point is:

[0061] in, and Indicates aircraft and aircraft The minimum turning radius is equal to and , and Indicates the center angle of the turning trajectory corresponding to the minimum turning radius circle, represents the length of the common tangent of the two turning circles, The value of varies depending on whether the two circles are inscribed or circumscribed:

[0062] in,( , )and( , ) indicates a point and The coordinates of and Indicates the corresponding turning radius. The earliest interception time is recorded as , given by the total trajectory length and the sum of the velocities of the two encountering parties Calculated, the earliest equal time point is when both parties The reachable set tangent point corresponding to the moment.

[0063] Starting from the earliest equal time point, the equal time line can be established through the intersection of the frontiers of the equal time reachable sets of both parties, as shown in the following formula. The equal time line calculation example is Figure 4 shown.

[0064]

[0065] Step 2: Obtain training samples and parameterize the situation information.

[0066] There are many feature parameters that can be selected to describe the battle scene of generating equal time lines, but for the sake of simplicity and from the perspective of describing the collision triangle, the relative distance is selected , the velocity lead angle of the aircraft T , the velocity lead angle of the aircraft M , the sight angle of the aircraft T Four characteristic parameters.

[0067] By setting different parameters in the interception scenario, calculating the set of equal time points, and then fitting according to the calculated equal time points, the equal time curve that meets the fitting index is obtained as sample data. The interception scenario generated by the sample is in a two-dimensional inertial coordinate system. The range of the initial missile-target distance R is set according to the requirements, and the sight angle range is set according to the interception scenario. rad, aircraft speed Set to a constant value according to requirements, aircraft T speed It can be set to a certain range, where the heading angle coincides with the sight angle. Since the premise of generating samples of equal time lines is to form a collision triangle, and the leading angle range meets the requirements for forming a collision triangle, the sample leading angle range is .

[0068] The earliest equal-time point for both the missile and the target is taken as the starting time, and the intersection of the frontiers of the reachable sets of both the missile and the target is calculated and recorded as the set of equal-time points in the guidance scenario at this time. The set of equal-time points is fitted by polynomial fitting to obtain the equal-time line curve. In order to conform to the law of shape change of the equal-time line, the fitting of the equal-time line is divided into two sections. The equal-time curve is fitted by a quadratic polynomial when the distance is far, and by a linear polynomial when the distance is close. The equal-time curve is fitted by a polynomial, and the fitting index requires the fitting degree to be higher than a certain set value, such as 0.99. Therefore, the geometric characteristic parameters of the equal-time line are the quadratic polynomial coefficients. The sample generation process is as follows: Figure 5 shown.

[0069] Step 3: Build a neural network model and set the training method.

[0070] The four parameters of sight angle, aircraft T lead angle, aircraft M lead angle, relative distance, and aircraft T speed are used as input variables, and the fitting coefficient of the isochronous curve is used as the output variable. The network model is established as follows:

[0071] The network adopts a multilayer perceptron (MLP) structure. Multilayer perceptron (MLP) is an artificial neural network and the most basic feedforward neural network. The network structure is shown in the figure below. Figure 6 As shown in Figure 1. MLP consists of an input layer, one or more hidden layers, and an output layer, each layer containing multiple neurons (also called nodes or units). Each neuron in the MLP is connected to every neuron in the next layer, and each connection has a weight.

[0072] Figure 6 middle, The network has 5 layers, 5 input parameters, 3 output parameters, and the hidden layer is divided into 3 layers. Hidden layer 1 has 16 neurons, hidden layer 2 has 24 neurons, and hidden layer 3 has 16 neurons. Network structure and The network is similar and only outputs 2 network parameters.

[0073] The network training method adopts the Levenberg-Marquardt (LM) algorithm. After normalizing the output data, the network weights are initialized by a random method, and hyperparameters are adjusted and the model is optimized as needed.

[0074] Step 4: Generate dynamic isotimes based on the trained model, and evaluate the guidance situation based on the generated isotimelines.

[0075] Taking the performance parameters and position information of both parties under the current situation as input, the trained network model function is called to generate the timeline.

[0076] The inputs of the function include: aircraft T sight angle, aircraft T lead angle, aircraft M lead angle, relative distance, aircraft T position, aircraft M position and aircraft T heading angle.

[0077] The output of the function includes: equal time point coordinates and equal time line fitting polynomial coefficients.

[0078] By connecting all equal time points, an equal time line can be obtained, and the generated equal time line can be used to evaluate the guidance situation.

[0079] The present invention is further described in detail below in conjunction with specific embodiments: Embodiment 1: This embodiment takes one-to-one and two-to-one interception scenarios at a constant speed with a performance advantage of the attacking party as examples to verify the effectiveness and superiority of the equal timeline generation method proposed in the present invention.

[0080] First, training samples need to be obtained and trained to facilitate the subsequent dynamic generation of reachable sets.

[0081] Given different situation parameters in the combat scenario, the equal time points are calculated, and the calculated equal time points are fitted into equal time lines to obtain equal time curves that meet the fitting index, which are the sample data. According to the commonly used aircraft performance parameters and environmental parameters in the interception scenario, the relative distance range of the sample is set as:

[0082] The sight angle range is set to:

[0083] In order to satisfy the formation of the collision triangle, the lead angles and lead angle ranges of the sample's aircraft M and aircraft T are:

[0084] The earliest equal time point for both the missile and the target is taken as the starting time, and the intersection of the frontiers of the reachable sets of both the missile and the target is calculated and recorded as the equal time point collection under the guidance scenario at this time. The equal time point collection is fitted by polynomial to obtain the equal time line curve. The fitting of the equal time line is divided into two sections. The equal time curve is fitted by quadratic polynomial when the distance is far, and by linear polynomial when the distance is close. The fitting index requires a fitting degree of 0.99.

[0085] After obtaining the sample, define the network model:

[0086] Multilayer Perceptron (MLP) is used for training. According to the defined network model, the network parameters are set as follows: , The network has 5 layers, namely input layer, hidden layer 1, hidden layer 2, hidden layer 3 and output layer. The number of neurons or input and output numbers in each layer are shown in Table 1 below: Table 1 Network parameters

[0087] The LM algorithm is used for training. After the output data is normalized, the network weights are initialized using a random method. Hyperparameters are adjusted and the model is optimized as needed. The training strategy uses the adam optimizer, the minimum batch is set to 32, and the learning rate is set to 0.001. The trained network is tested using the sample set to verify the training effect of the neural network. After 3000 iterations, the fitting effect diagram, root mean square error, and network fitting mean square error diagram of the training set and test set are shown in the figure below. Figure 8 , Fig. 9 , Fig.10As shown in the figure, the training effect is good, and the best training effect is achieved when the training reaches the 46th round.

[0088] After training is completed, verify the accuracy of dynamically generated timelines in a one-to-one scenario.

[0089] In this interception scenario, since aircraft T is faster, aircraft M cannot intercept it and cannot generate equal timelines, so it is set that both aircraft fly towards each other. The performance parameters of both aircraft are set as follows: Table 2 Performance parameters of both parties in one-to-one scenario

[0090] First, the performance parameters and position information of both parties under the current situation are imported, and then the trained network model function is called to generate the isotimeline. At a certain position, the input value and output value of the function are shown in Table 3 and Table 4, and the isotimeline is generated as shown in Fig.11 shown.

[0091] Table 3 Input parameters of equal timeline function

[0092] Table 4 Input and output parameters of equal timeline function

[0093] The isotimelines generated by the data-driven approach are compared with those generated by the numerical approach.

[0094] It can be seen that the data-driven generated isochronous timeline fitting effect is good. The simulation time and maximum error under the experimental equipment are shown in the following table.

[0095] Table 5 Input and output parameters of equal timeline function

[0096] It can be seen from the table analysis that the equal timeline generation method proposed in the present invention is faster in simulation time than the method calculated by numerical iteration, and the error is controlled within a smaller range, which reflects the superiority of the present invention.

[0097] The following is an application example of dynamic generation of equal timelines in a two-to-one interception scenario.

[0098] Import the performance parameters and position information of both parties under the current situation, and then call the trained network model function to generate the isotimeline. At a certain position, the performance parameters and situation information of both parties are shown in Table 6, and the isotimeline is generated as shown in Table 6. Fig.13 shown.

[0099] Table 6 Performance parameters and situation information of both parties

[0100] like Fig.13 As shown in the figure, there is a gap between the equal time lines generated by aircraft M1 and aircraft M2 for the incoming missile. When aircraft T flies at initial heading angles of 95° and 90° respectively, aircraft M1 and aircraft M2 use proportional guidance law for interception, and the guidance law coefficient is 4. The interception effect is as follows: Fig.14 , Fig.15 shown.

[0101] When aircraft T flies at a heading angle of 95°, the flight path is within the coverage of the isotimeline, and aircraft T is eventually intercepted by aircraft M1. When aircraft T flies at a heading angle of 90°, the flight path passes through the gap of the isotimeline and is outside the coverage of the isotimeline. During the interception process, the minimum relative distance between aircraft T and aircraft M is 25.58m, which is greater than the killing radius of successful interception by aircraft M, and aircraft T successfully penetrates. It can be seen that the interception / penetration strategy in air combat scenarios can be formulated based on the isotimeline generated in real time, thereby improving the success rate of aircraft interception / penetration in complex geometric situations.

[0102] Embodiment 2: Embodiment 2 provided by the present invention is an embodiment of the guidance situation assessment system based on data drive provided by the present invention, such as Fig.16 As shown, an embodiment of the system includes: a modeling module, a fitting module, a training module and an evaluation module.

[0103] A modeling module, wherein the modeling module is used to establish a reachable set and an equal timeline model of both the missile and the target during the guidance process in an inertial coordinate system with the aircraft as a mass point; A fitting module, wherein the fitting module is used to calculate a set of equal time points for fitting based on a reachable set and an equal time line model, and obtain an equal time curve that meets the fitting index as sample data; A training module, wherein the training module is used to establish a neural network model and perform training through sample data, and to generate a dynamic isotimeline using the trained neural network model according to the performance parameters and position information of both parties under the current situation; An evaluation module is used to evaluate the guidance situation according to the generated isotimeline.

[0104] It can be understood that the data-driven guidance situation assessment system provided by the present invention corresponds to the data-driven guidance situation assessment method provided in the aforementioned embodiments. The relevant technical features of the data-driven guidance situation assessment system can refer to the relevant technical features of the data-driven guidance situation assessment method, which will not be repeated here.

[0105] Another object of the present invention is to provide an electronic device, such as Fig.17As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the steps of the data-driven guidance situation assessment method.

[0106] The data-driven guidance situation assessment method comprises the following steps: In the inertial coordinate system, the aircraft is taken as a mass point, and the reachable set and equal time line model of both the missile and the target are established during the guidance process. According to the reachable set and the isotimeline model, the isotime point set is calculated for fitting, and the isotime curve that meets the fitting index is obtained as the sample data; Establish a neural network model and train it with sample data. Based on the performance parameters and location information of both parties under the current situation, use the trained neural network model to generate dynamic timelines. Based on the generated isotimeline, the guidance situation is evaluated.

[0107] A fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data-driven guidance situation assessment method are implemented.

[0108] The data-driven guidance situation assessment method comprises the following steps: In the inertial coordinate system, the aircraft is taken as a mass point, and the reachable set and equal timeline model of both the missile and the target are established during the guidance process. According to the reachable set and the isotimeline model, the isotime point set is calculated for fitting, and the isotime curve that meets the fitting index is obtained as the sample data; Establish a neural network model and train it with sample data. Based on the performance parameters and location information of both parties under the current situation, use the trained neural network model to generate dynamic timelines. Based on the generated isotimeline, the guidance situation is evaluated.

[0109] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data-driven guidance situation assessment method, characterized in that: include: In the inertial coordinate system, the aircraft is taken as a mass point, and the reachable set and equal timeline model of both the missile and the target are established during the guidance process. According to the reachable set and the isotimeline model, the isotime point set is calculated for fitting, and the isotime curve that meets the fitting index is obtained as the sample data; Establish a neural network model and train it with sample data. Based on the performance parameters and location information of both parties under the current situation, use the trained neural network model to generate dynamic timelines. Based on the generated isotimeline, the guidance situation is evaluated.

2. The data-driven guidance situation assessment method according to claim 1, characterized in that: The kinematic model of both the missile and the target during the guidance process is: Among them, the subscript Indicates aircraft T or aircraft M, Indicates speed, represents the heading angle, represents the normal acceleration, represents the maximum normal acceleration of the aircraft T, represents the maximum normal acceleration of the aircraft M.

3. The data-driven guidance situation assessment method according to claim 1, characterized in that: The dynamic model of both the missile and the target in the guidance process is: in, and Represents the state vector and control input, then The state vector at the moment is: 。 4. The data-driven guidance situation assessment method according to claim 1, characterized in that: The reachable set is in, represents the initial position, Indicated in The state vector at time, Indicates the time range [ ] within the continuous control instructions, represents all feasible control instructions within the constraints.

5. The data-driven guidance situation assessment method according to claim 1, characterized in that: The isotimeline model is in, represents the speed of the aircraft M, represents the speed of the aircraft T, represents the maximum normal acceleration of the aircraft M, represents the maximum normal acceleration of the aircraft T, express The position of aircraft M at time express The position of the aircraft T at time instant, Indicates the earliest interception time.

6. The data-driven guidance situation assessment method according to claim 1, characterized in that: The fitting method of the isochronous curve is: Taking the earliest equal time point of the missile and the target as the starting time, calculate the intersection of the frontiers of the reachable sets of the missile and the target as the equal time point set in the guidance scenario at this time, and obtain the equal time curve by polynomial fitting of the equal time point set; The polynomial fitting includes quadratic polynomial fitting and linear polynomial fitting.

7. The data-driven guidance situation assessment method according to claim 1, characterized in that: The neural network model is in, represents the sight angle of the aircraft T, represents the velocity lead angle of the aircraft T, represents the velocity lead angle of the aircraft M, Indicates the relative distance between the two aircrafts. represents the speed of the aircraft T, and Represents the network structure.

8. The data-driven guidance situation assessment method according to claim 1, characterized in that: The neural network model is a multi-layer perceptron structure, which includes an input layer, an output layer and several hidden layers.

9. The data-driven guidance situation assessment method according to claim 8, characterized in that: Each of the input layer, output layer and several hidden layers includes several neurons, each of the neurons is connected to each neuron in the next layer, and the connections between the neurons are weighted.

10. The data-driven guidance situation assessment method according to claim 1, characterized in that: The performance parameters and position information of both parties under the current situation include the relative distance between the two aircrafts, the positions of the two aircrafts, the speed lead angles of the two aircrafts, the sight angles of the two aircrafts and the heading angles of the two aircrafts.

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