Air target interception intention inference method and system based on track prediction

Abstract

The invention belongs to the field of air target threat estimation, and discloses an air target interception intention inference method and system based on flight path prediction, and the method comprises the steps: obtaining historical state estimation information of an aircraft and an interceptor, carrying out the short-time flight path prediction, calculating the dynamic threat degree of a prediction segment, and obtaining an interception intention matrix; and finally, carrying out long-time prediction on the interceptor with the interception intention and the track of the corresponding aircraft to obtain the position and the size of an interception encountering area. According to the method, pseudo measurement data is generated through bidirectional smooth filtering and polynomial fitting, a relatively accurate predicted track is obtained, the dynamic threat degree is calculated more accurately by using an improved nearest encounter point function, then an interception encounter area is predicted, and the dynamic threat degree is calculated more accurately. Therefore, the inference of the air target interception intention and the prediction of the interception encountering area can obtain more accurate results.

Description

Technical Field

[0001] The invention belongs to the field of aerial target threat estimation, and in particular relates to an aerial target interception intention inference method and system based on track prediction. Background Art

[0002] In the ever-changing battlefield environment of information warfare, it is a very important task to quickly identify the original data of enemy targets obtained from numerous and complex sensors, obtain key information such as target type, position, speed, etc. through data preprocessing, and judge the threat level of the incoming target to us, so as to provide data support for battlefield commanders to make corresponding combat deployment decisions. With the rapid deployment of new weapons and equipment and the innovative use of theoretical tactics, the air combat environment is becoming increasingly complex. Inferring interception intentions through online identification of dynamic threats through track prediction is a prerequisite for target allocation and maneuvering decisions. Especially in the combat scenario of aircraft breakout, accurate and reliable interception intention inference methods can judge the enemy interceptor with the greatest threat in real time, provide support for subsequent countermeasures or other decisions, and thus ensure the smooth progress of aircraft breakout missions.

[0003] The methods currently used to solve the target threat estimation problem in complex environments at home and abroad mainly include: Bayesian network reasoning, analytic hierarchy process (AHP), multi-attribute decision-making theory, fuzzy comprehensive evaluation method, grey correlation method and deep learning method. The collaborative form of each terminal platform is still relatively simple, and the degree of self-organization and intelligence is not high enough. According to the existing research results, we can find that the main problem of the existing methods is that the output results of the algorithm are all discrete results, and directly using them for multi-target threat sorting itself will lead to other more difficult problems. For example, insufficient discretization of threat levels will make it difficult to compare the threat levels between targets, and if multiple targets belonging to different categories and different states are at the same level of threat level, it is easy to generate unreasonable sorting results that obviously violate normal cognition in the subsequent threat sorting process due to information loss. In addition, how to further process the threat estimation results and use them in related specific intention inference methods is also an important research direction at present.

[0004] The track prediction problem refers to making the most accurate prediction of the target's track in the future by making full use of the known information of the target, including the target's historical track, motion characteristics, and even target attribute categories, behavioral intentions and other information. It is essentially an estimation problem of using the "known" to predict the "unknown". At present, the methods used to solve the target track prediction problem at home and abroad are mainly divided into two categories: model-based track prediction methods and deep learning-based track prediction methods. Through the analysis and comparison of existing track prediction methods, it can be seen that although the model-based track prediction method has the advantages of simple structure and strong robustness, it is difficult to guarantee high prediction accuracy when the model is complex, easy to mismatch, or the statistical characteristics of noise are unclear. Although the track prediction method based on deep learning can improve the track prediction accuracy by combining different neural networks, its interpretability is poor, and the high computational complexity makes it difficult for the algorithm to meet real-time requirements.

[0005] There are few existing research methods on the interception intention and interception encounter zone prediction of aerial targets. Most of them are deep learning methods, which have poor interpretability and lack of real-time performance. How to use trajectory prediction and threat estimation in interception intention inference and interception encounter zone prediction is an important issue that needs to be solved urgently. Summary of the invention

[0006] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a method and system for inferring the interception intention of an aerial target based on track prediction, which can accurately infer and predict the interception intention and interception encounter area of ​​the interceptor.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for inferring an aerial target interception intention based on track prediction, comprising the following steps: Get historical status information of interceptors and aircraft; The historical state information of the interceptor and the aircraft is subjected to two-way filtering and smoothing respectively, and the state estimation result and covariance matrix after two-way filtering and smoothing are obtained; According to the state estimation result and covariance matrix after bilateral filtering and smoothing, pseudo measurement data is generated based on polynomial fitting; Performing augmented interactive multi-model-volumetric Kalman filtering on the pseudo-measurement data, using the filtered pseudo-measurement data to update the historical state information of the interceptor and the aircraft, and obtaining the predicted trajectory of the interceptor and the predicted trajectory of the aircraft; The predicted trajectory of the interceptor and the predicted trajectory of the aircraft are processed by using the improved closest encounter point function to calculate the dynamic threat degree of the predicted section. According to the dynamic threat degree of the predicted segment, the interception intention is judged in combination with the intention inference rules to obtain the interception intention matrix at each moment; According to the interception intention matrix at each moment, the predicted trajectory of the interceptor and the predicted trajectory of the aircraft are combined to obtain the shortest distance between the interceptor and the aircraft; According to the shortest distance between the interceptor and the aircraft, the center point and radius of the interception encounter area are determined.

[0008] A further improvement of the present invention is that the historical status information of the interceptor includes:

[0009] in, is the interceptor number, is the interceptor position estimate x Axis component, is the interceptor position estimate y Axis component, is the interceptor position estimate z Axis component, is the interceptor velocity estimate x Axis component, is the interceptor velocity estimate y Axis component, is the interceptor velocity estimate z Axis component.

[0010] A further improvement of the present invention is that the historical status information of the aircraft includes:

[0011] in, is the estimated value of the aircraft position x Axis component, is the estimated value of the aircraft position y Axis component, is the estimated value of the aircraft position z Axis component, is the estimated value of the aircraft speed x Axis component, is the estimated value of the aircraft speed y Axis component, is the estimated value of the aircraft speed z Axis component.

[0012] A further improvement of the present invention is that the specific method of performing two-way filtering and smoothing processing on the historical status information of the interceptor and the aircraft is as follows: Set the prediction time, divide the prediction interval, use augmented IMM-CKF filtering on the interceptor or aircraft historical state information, perform state estimation from the start time to the end time of measurement, and obtain the forward filtering result and forward filtering covariance matrix; The interceptor or aircraft historical status information is filtered using EKF, and the filtering is performed in reverse from the end of the measurement to the start of the vector measurement to obtain the backward filtering result and the backward filtering covariance matrix; Combine the forward filtering result, the forward filtering covariance matrix, the backward filtering result and the backward filtering covariance matrix to obtain the bilateral filtering smoothing result and the bilateral filtering covariance matrix A further improvement of the present invention is that, according to the state estimation result and the covariance matrix after bilateral filtering and smoothing, a specific method for generating pseudo measurement data based on polynomial fitting is as follows: Extract the interceptor's state estimation vector from the start time to the end time of tracking, along the ECEF coordinate system In the three-axis direction, the interceptor's state estimation vector is split into three groups of position data numbered in time sequence; Get the ECEF coordinate system Axis direction data polynomial of order; Based on the least squares fitting, according to the time The obtained polynomial coefficients are used to obtain the residual sum of squares between the time-varying position prediction data and the actual data; According to the necessary conditions for finding the extreme value of multivariate functions, combined with the residual sum of squares between the time-varying position prediction data and the actual data, the analytical solution expressions of the relevant parameters are obtained; ECEF coordinate system The data in the axial direction are brought into the analytical solution expressions of the relevant parameters to obtain the values ​​of the relevant parameters, thus obtaining the interceptor in the ECEF coordinate system. Axis direction data corresponding to polynomial of order; Similarly, according to the ECEF coordinate system Axis direction data and Axis direction data, get Axis and Axis direction data corresponding to polynomial of order; according to axis, Axis and Axis direction data corresponding to The order polynomial, combined with the required prediction duration, obtains the interceptor in the ECEF coordinate system within the required duration. Coordinate information; According to the required time, the interceptor is located in the ECEF coordinate system The coordinate information is combined with the position information of the aircraft in the predicted time period, and the measurement equation is used to perform pseudo-measurement calculation to obtain pseudo-measurement data.

[0013] A further improvement of the present invention is that an improved closest encounter point function is used to process the predicted track of the interceptor and the predicted track of the aircraft. The specific method for calculating the dynamic threat degree of the predicted section is as follows: Calculate the relative position and relative speed of the interceptor to the aircraft based on the predicted trajectory of the interceptor and the predicted trajectory of the aircraft; According to the relative position and relative speed of the interceptor to the aircraft, the closest encounter point distance and the closest encounter point time are calculated; The closest encounter point time is normalized, and the interceptor time threat level value is obtained according to the normalized closest encounter point time; The closest encounter point distance is normalized, and the interceptor distance threat level value is obtained according to the normalized closest encounter point distance; The interceptor time threat level value and the interceptor distance threat level value are corrected by using the interceptor speed and the angle between the interceptor and the aircraft; According to the corrected interceptor time threat level value and interceptor distance threat level value, the dynamic threat level of the predicted section is obtained.

[0014] A further improvement of the present invention is that the interception intention is determined according to the dynamic threat degree of the predicted segment and in combination with the intention inference rule, and the specific method for obtaining the interception intention matrix at each moment is as follows: Preset intention inference rules and establish a judgment model of dynamic threat level and intention inference rules for prediction segments; Input the predicted segment dynamic threat level into the judgment model to obtain the interception intention at each moment; According to the interception intention at each moment, the interception intention matrix at each moment is obtained.

[0015] A further improvement of the present invention is that, according to the interception intention matrix at each moment, the center point of the interception encounter area is determined, the radius of the interception encounter area is calculated, and the specific method for obtaining the closest distance between the interceptor and the aircraft is as follows: According to the interception intention matrix at each moment, the corresponding interceptor and aircraft are extracted, and the interception encounter area is predicted according to the interception intention matrix of the interceptor and the aircraft. The interception encounter area includes the set of points where the interceptor can hit the aircraft; According to the predicted track of the interceptor and the predicted track of the aircraft, the shortest distance between the interceptor and the aircraft is obtained.

[0016] A further improvement of the present invention is that, according to the shortest distance between the interceptor and the aircraft, a specific method for determining the center point of the interception encounter area and the radius of the interception encounter area is as follows: At the moment when the interceptor and the aircraft are closest, the midpoint of the line connecting the predicted track of the interceptor and the predicted track of the aircraft is the center point of the interception encounter area; The radius of the interception encounter zone is obtained based on the closest distance between the interceptor and the aircraft and the predicted time for the interceptor and the aircraft to reach the closest distance.

[0017] In a second aspect, the present invention provides an air target interception intention inference system based on track prediction, comprising the following steps: An information acquisition module, used to obtain historical status information of interceptors and aircraft; A filtering module is used to perform two-way filtering and smoothing processing on the historical state information of the interceptor and the aircraft, respectively, to obtain the state estimation result and covariance matrix after two-way filtering and smoothing; A pseudo two-sided data acquisition module is used to generate pseudo measurement data based on polynomial fitting according to the state estimation result and covariance matrix after bilateral filtering and smoothing; The predicted track acquisition module is used to perform augmented interactive multi-model-volumetric Kalman filtering on the pseudo-measurement data, and use the filtered pseudo-measurement data to update the historical state information of the interceptor and the aircraft to obtain the predicted track of the interceptor and the predicted track of the aircraft; A threat degree acquisition module is used to process the predicted trajectory of the interceptor and the predicted trajectory of the aircraft using an improved closest encounter point function to calculate the dynamic threat degree of the predicted section; The interception intention matrix acquisition module is used to determine the interception intention based on the dynamic threat level of the predicted segment and the intention inference rules to obtain the interception intention matrix at each moment; A closest distance acquisition module is used to obtain the closest distance between the interceptor and the aircraft based on the interception intention matrix at each moment and the predicted trajectory of the interceptor and the predicted trajectory of the aircraft; The interception encounter area acquisition module is used to determine the center point and radius of the interception encounter area according to the shortest distance between the interceptor and the aircraft.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention first obtains the historical state estimation information of the aircraft and the interceptor, then performs short-term track prediction, then calculates the dynamic threat degree of the prediction section to obtain the interception intention matrix, and finally performs long-term prediction on the track of the interceptor with interception intention and the corresponding aircraft to obtain the position and size of the interception encounter area. The present invention generates pseudo-measurement data through bidirectional smoothing filtering and polynomial fitting to obtain a more accurate predicted track, uses an improved closest encounter point function to more accurately calculate the dynamic threat degree, and then predicts the interception encounter area, so that the inference of the interception intention of the air target and the prediction of the interception encounter area can obtain more accurate results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the present invention; Figure 2 This is the track prediction result diagram; Figure 3 The simulation scene and track prediction result diagram at the 4th second; Figure 4 This is the threat prediction and interception intention result diagram of aircraft 1 at the 4th second; Figure 5 This is the threat prediction and interception intention result diagram of aircraft 2 at the 4th second; Figure 6 This is the threat prediction and interception intention result diagram of aircraft 3 at the 4th second; Figure 7 This is the interception encounter area prediction result diagram of aircraft 1 and interceptor 2 at the 3rd second moment; Figure 8 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0020] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0021] Embodiment 1: See also Figure 1 , an air target interception intention inference method based on trajectory prediction includes the following steps: S1, obtain the historical status information of the interceptor and the aircraft.

[0022] S2, perform two-way filtering and smoothing processing on the historical state information of the interceptor and the aircraft respectively, and obtain the state estimation result and covariance matrix after two-way filtering and smoothing.

[0023] S3, generating pseudo measurement data based on polynomial fitting according to the state estimation result and covariance matrix after bilateral filtering and smoothing.

[0024] S4, performing augmented interactive multi-model-volumetric Kalman filtering on the pseudo-measurement data, using the filtered pseudo-measurement data to update the historical state information of the interceptor and the aircraft, and obtaining the predicted trajectory of the interceptor and the predicted trajectory of the aircraft.

[0025] S5, using the improved closest encounter point function to process the predicted trajectory of the interceptor and the predicted trajectory of the aircraft, and calculate the dynamic threat degree of the predicted section.

[0026] S5, according to the dynamic threat degree of the predicted segment, combined with the intention inference rules, the interception intention is judged to obtain the interception intention matrix at each moment.

[0027] S7, according to the interception intention matrix at each moment, combined with the predicted trajectory of the interceptor and the predicted trajectory of the aircraft, the shortest distance between the interceptor and the aircraft is obtained.

[0028] S8, determining the center point and radius of the interception encounter area according to the shortest distance between the interceptor and the aircraft.

[0029] Embodiment 2: This embodiment includes the following steps: Step 1, obtaining historical status information of the interceptor and the aircraft; Step 2: Two-Filter Smoothing (TFS) is performed on the interceptor's historical state estimation information, and then pseudo-measurement data is generated based on polynomial fitting, combined with augmented interactive multi-model-volumetric Kalman (IMM-CKF) filtering to obtain the predicted track; Step 3: Use the improved closest encounter point function (ICPA function) to calculate the dynamic threat level of the predicted segment, and combine the intention inference rules to determine the interception intention, and obtain the interception intention matrix at each moment; Step 4: Make a long-term prediction of the trajectory of the interceptor with interception intention and the corresponding aircraft, determine the center point of the interception encounter area, calculate the radius of the interception encounter area, and obtain the position and size (spherical) of the interception encounter area.

[0030] Embodiment 3: In step 1, the number is The interceptor to k The state estimation information at a certain moment is expressed as follows, containing 10-12 dimensions: Formula 1 The first six dimensions are motion state information, which are expressed as follows in the ECEF coordinate system: Formula 2 in, is the interceptor position estimate x Axis component, is the interceptor position estimate y Axis component, is the interceptor position estimate z Axis component, is the interceptor velocity estimate x Axis component, is the interceptor velocity estimate y Axis component, is the interceptor velocity estimate z Axis component.

[0031] Number The aircraft status information is shown as follows: Formula 3 in, is the estimated value of the aircraft position x Axis component, is the estimated value of the aircraft position y Axis component, is the estimated value of the aircraft position z Axis component, is the estimated value of the aircraft speed x Axis component, is the estimated value of the aircraft speed y Axis component, is the estimated value of the aircraft speed z Axis component.

[0032] Embodiment 4: In the step of performing bidirectional filtering and smoothing on the interceptor's historical state estimation information, and then generating pseudo-measurement data based on polynomial fitting, combined with augmented IMM-CKF filtering, the predicted track is obtained. The specific process is as follows: In the first step, the prediction duration is set, the prediction interval is divided, and the TFS algorithm is used to perform reverse filtering and smooth estimation on the measurement values ​​of the final segment of the interceptor's track.

[0033] The forward filtering uses augmented IMM-CKF filtering to perform state estimation from the start time to the end time of measurement to obtain the forward filtering result. and the covariance matrix .

[0034] Backward filtering uses EKF (Extended Kalman Filter) filtering, which performs filtering in reverse from the end of the measurement to the start of the measurement to obtain the backward filtering result. and the covariance matrix .

[0035] The second step is to perform data fusion on the results to obtain the bidirectional filtering and smoothing results. and the covariance matrix . Formula 4 Formula 5 The third step is to find the optimal polynomial function based on the smoothed historical state information of the interceptor .

[0036] For the selected interceptor , extract the time from the start of tracking to the end of tracking k The state estimation vector is m group, along the ECEF coordinate system Three-axis direction, The state estimation vector is split into three sets of position data numbered in time sequence, as shown below: Formula 6 In the ECEF coordinate system For example, assuming that there is Order polynomial: Formula 7 Approximate satisfaction The overall distribution of the data set that changes in time series along the axis direction.

[0037] According to the least squares (LS) fitting requirements, it is necessary to calculate the time Obtain The polynomial coefficients can make the residual sum of squares between the time-varying position prediction data calculated by the polynomial and the actual data The minimum mathematical form is as follows: Formula 8 According to the necessary conditions for finding the extreme value of a multivariate function, Find the partial derivatives and set each equation equal to 0, then solve to get the parameters The analytical solution expression is as follows: Formula 9 ECEF coordinate system Data in the axis direction Substitute into formula 9 to solve the parameters At this point, the interceptor's old track in the ECEF coordinate system is obtained Axis direction data corresponding to Polynomial .

[0038] Similarly, the ECEF coordinate system Axis direction data and Axis direction data Substitute them into formula 9 respectively, and we can get Axis and Axis direction data corresponding to Order polynomial.

[0039] In order to obtain the three coordinate axes Based on the order polynomial, according to the required prediction time , brought into In the above example, we can directly calculate the ~ The interceptor in the ECEF coordinate system within the time interval Coordinate information. Combined with the aircraft in the prediction interval ~ The position information is used to perform pseudo-measurement calculation using the measurement equation: Formula 10 in, is the pseudo relative distance between the sensor and the target, is the pseudo target line of sight azimuth, is the elevation angle of the pseudo target line of sight, The variance is Gaussian white noise.

[0040] In the fourth step, after obtaining the pseudo-measurement, the interceptor state in the prediction period is updated based on the augmented IMM-CKF filtering technology to obtain the predicted track.

[0041] Embodiment 5: In the step of using the ICPA function to calculate the dynamic threat level of the predicted segment, combining the intention inference rules to determine the interception intention, and obtaining the interception intention matrix at each moment, the specific process is as follows: In the first step, the ICPA function is used to calculate the dynamic threat degree of the predicted segment based on the trajectory status information of the interceptor and the aircraft.

[0042] Consider the distance between the interceptor's current trajectory and the closest point to our aircraft object and the time of closest arrival , namely the two indicators of the closest encounter point time and the closest encounter point distance. The following formulas are all based on a certain moment, so the time parameter is omitted.

[0043] (1) Calculation number is The enemy interceptor numbered The relative position of our aircraft and relative speed for: Formula 11 Formula 12 Calculate the distance to the nearest encounter point for: Formula 13 Calculate the nearest meeting time for: Formula 14 in, The minimum distance from the interceptor's point on the straight-line trajectory to our aircraft according to its current speed and direction. It is the straight-line motion time of the interceptor from its current position to the closest point to our aircraft at its current speed.

[0044] (2) Yes After normalization, the interceptor threat level value defined by the nearest encounter time is for: Formula 15 In the formula, The time required for the enemy interceptor and our aircraft to reach the closest encounter when the threat level is 1. The time required for the two to reach the closest encounter when the threat level is 0.7. The time required for the two to reach the closest encounter when the threat level is 0.3. The time required for the two to reach the closest encounter when the threat level is 0.

[0045] Similarly, the interceptor threat level is defined based on the distance for: Formula 16 In the formula, is the closest encounter distance between the enemy interceptor and our aircraft, is the closest encounter distance between the enemy interceptor and our aircraft when the threat level is 1, is the closest encounter distance between the two when the threat level is 0.7, is the closest encounter distance between the two when the threat level is 0.3, It is the closest encounter distance between the two when the threat level is 0.

[0046] Introduce the interceptor speed and the angle between the interceptor and the aircraft , to indicate the impact of the interceptor's approach and retreat on the threat level. The processing method is as follows: Formula 17 In the formula, is the original CPA threat level, For the improved ICPA threat level, It is a set coefficient, and its value will affect the sensitivity of the threat level to the change of target speed direction.

[0047] (3) Calculate the ICPA threat level as follows: Formula 18 It should be noted that the above method and formula only give a dynamic threat estimation model at a certain moment. , , , and All of these factors change with time, and the threat estimation result needs to be calculated in real time.

[0048] The second step is to make an inference based on the intention inference rules. If there is an interception intention, set the corresponding item in the intention matrix to 1, and evaluate the intention level according to the average threat level of the predicted segment as follows: if the average threat level is <0.6, the interception intention level = 0; if the average threat level is >0.9, the interception intention level = 3. If neither is satisfied, calculate the difference between the threat level at the next moment and the current threat level. If the number of moments when the difference is greater than 0 accounts for 70% of the predicted number of moments, then if 0.6 is Average threat level < 0.7, then interception intention level = 1; if 0.7 If the average threat level is <0.8, the interception intention level = 2; if neither condition is met, the interception intention level = 3. If there is no interception intention, the corresponding item in the intention matrix is ​​set to 0, and finally the interception intention matrix at each moment is obtained.

[0049] Embodiment 6: In the steps of making long-term predictions on the tracks of the interceptor with interception intention and the corresponding aircraft, determining the center point of the interception encounter area, calculating the radius of the interception encounter area, and obtaining the position and size of the interception encounter area, the specific process is as follows: The first step is to extract the interceptor and the corresponding aircraft with interception intention from the intention matrix, and predict the interception encounter area for this interception pair. The interception area includes the points around the aircraft that the interceptor's track may hit. Through track prediction, the moment when the predicted tracks of the two are closest is found. The midpoint of the line connecting the two at this moment is the center of the interception encounter area.

[0050] Step 2: Calculate the radius of the interception encounter zone R : Formula 19 in, is the shortest distance between the enemy interceptor and our aircraft within the prediction time period, To predict the time from the start to the closest distance between the enemy interceptor and our aircraft, , and are the standard deviations of the measurement noise in the adversarial scene (the sensor measurement noise is zero-mean Gaussian white noise).

[0051] Embodiment 7: See also Figure 8 The air target interception intention inference system based on trajectory prediction includes the following steps: The information acquisition module is used to obtain the historical status information of the interceptor and the aircraft.

[0052] The filtering module is used to perform two-way filtering and smoothing processing on the historical state information of the interceptor and the aircraft respectively, and obtain the state estimation result and covariance matrix after two-way filtering and smoothing.

[0053] The pseudo two-sided data acquisition module is used to generate pseudo measurement data based on polynomial fitting according to the state estimation results and covariance matrix after bilateral filtering and smoothing.

[0054] The predicted track acquisition module is used to perform augmented interactive multi-model-volumetric Kalman filtering on the pseudo-measurement data, and use the filtered pseudo-measurement data to update the historical state information of the interceptor and the aircraft to obtain the predicted track of the interceptor and the predicted track of the aircraft.

[0055] The threat degree acquisition module is used to process the predicted trajectory of the interceptor and the predicted trajectory of the aircraft using an improved closest encounter point function to calculate the dynamic threat degree of the predicted section.

[0056] The interception intention matrix acquisition module is used to determine the interception intention according to the dynamic threat degree of the predicted segment and the intention inference rules to obtain the interception intention matrix at each moment.

[0057] The shortest distance acquisition module is used to obtain the shortest distance between the interceptor and the aircraft according to the interception intention matrix at each moment, combined with the predicted trajectory of the interceptor and the predicted trajectory of the aircraft.

[0058] The interception encounter area acquisition module is used to determine the center point and radius of the interception encounter area according to the shortest distance between the interceptor and the aircraft.

[0059] Embodiment 8: See also Figure 2,If the prediction time is set to 0.5s, that is, 10 sampling periods, the thick solid line represents the smoothing result of bilateral filtering, the thin solid line in the first half represents the historical track, and the thin solid line in the second half represents the track prediction result. It can be seen that after bilateral filtering and smoothing, the original interceptor's initial track with large fluctuations has been well smoothed, which improves the accuracy of polynomial fitting, the accuracy of pseudo-measurement generation, and the rationality of track prediction.

[0060] See also Figure 3 , the current state estimation time is the 4th second, the simulation scenario sets three aircraft and four interceptors, aircraft 1 is guided by interceptors 1 and 2, and aircraft 3 is guided by interceptors 3 and 4. Figure 4 , Figure 5 , Figure 6 For aircraft 1, the average threat level of interceptors 1 and 2 is greater than that of interceptors 3 and 4, so interceptors 1 and 2 have high interception intentions to aircraft 2. For aircraft 2, since the average threat level is less than the threshold of interception intention 0.6, it is determined that the four interceptors have no interception intention. For aircraft 3, although the average threat level of the four interceptors in the threat prediction part is high, since interceptors 1 and 2 skip aircraft 3, there are many parts of threat level that decrease, so it can be directly determined as no logical intention. The threat level of interceptors 3 and 4 in the prediction section continues to rise, and they are determined as high interception intentions. In summary, the simulation results are in line with the simulation scenario settings.

[0061] See also Figure 7 , the simulation parameters remain unchanged, considering the simulation results of a single moment, if the current state estimation moment is the 3rd second, which is the mid-term moment when the interception intention appears, select aircraft 1 and interceptor 2 for interception encounter area prediction. From the above simulation results, it can be seen that the proposed method for inferring the interception intention of air targets based on trajectory prediction can better predict and mark the possible encounter areas in the interception confrontation scenario.

[0062] 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 method for inferring air target interception intention based on trajectory prediction, characterized in that: The following steps are involved: Get historical status information of interceptors and aircraft; The historical state information of the interceptor and the aircraft is subjected to two-way filtering and smoothing respectively, and the state estimation result and covariance matrix after two-way filtering and smoothing are obtained; According to the state estimation result and covariance matrix after bilateral filtering and smoothing, pseudo measurement data is generated based on polynomial fitting; Performing augmented interactive multi-model-volumetric Kalman filtering on the pseudo-measurement data, using the filtered pseudo-measurement data to update the historical state information of the interceptor and the aircraft, and obtaining the predicted trajectory of the interceptor and the predicted trajectory of the aircraft; The predicted trajectory of the interceptor and the predicted trajectory of the aircraft are processed by using the improved closest encounter point function to calculate the dynamic threat degree of the predicted section. According to the dynamic threat degree of the predicted segment, the interception intention is judged in combination with the intention inference rule to obtain the interception intention matrix at each moment; According to the interception intention matrix at each moment, the predicted trajectory of the interceptor and the predicted trajectory of the aircraft are combined to obtain the shortest distance between the interceptor and the aircraft; According to the shortest distance between the interceptor and the aircraft, the center point and radius of the interception encounter area are determined.

2. The method for inferring air target interception intention based on track prediction according to claim 1 is characterized in that: The interceptor's historical status information includes: in, is the interceptor number, is the interceptor position estimate x Axis component, is the interceptor position estimate y Axis component, is the interceptor position estimate z Axis component, is the interceptor velocity estimate x Axis component, is the interceptor velocity estimate y Axis component, is the interceptor velocity estimate z Axis component.

3. The method for inferring air target interception intention based on track prediction according to claim 1 is characterized in that: The historical status information of the aircraft includes: in, is the estimated value of the aircraft position x Axis component, is the estimated value of the aircraft position y Axis component, is the estimated value of the aircraft position z Axis component, is the estimated value of the aircraft speed x Axis component, is the estimated value of the aircraft speed y Axis component, is the estimated value of the aircraft speed z Axis component.

4. The method for inferring air target interception intention based on track prediction according to claim 1 is characterized in that: The specific method of performing two-way filtering and smoothing on the historical status information of the interceptor and the aircraft is as follows: Set the prediction time, divide the prediction interval, use augmented IMM-CKF filtering on the interceptor or aircraft historical state information, perform state estimation from the start time to the end time of measurement, and obtain the forward filtering result and forward filtering covariance matrix; The interceptor or aircraft historical status information is filtered using EKF, and the filtering is performed in reverse from the end of the measurement to the start of the vector measurement to obtain the backward filtering result and the backward filtering covariance matrix; The forward filtering result, the forward filtering covariance matrix, the backward filtering result and the backward filtering covariance matrix are combined to obtain a bilateral filtering smoothing result and a bilateral filtering covariance matrix.

5. The method for inferring air target interception intention based on track prediction according to claim 1, characterized in that: According to the state estimation results and covariance matrix after bilateral filtering and smoothing, the specific method of generating pseudo measurement data based on polynomial fitting is as follows: Extract the interceptor's state estimation vector from the start time to the end time of tracking, along the ECEF coordinate system In the three-axis direction, the interceptor's state estimation vector is split into three groups of position data numbered in time sequence; Get the ECEF coordinate system Axis direction data polynomial of order; Based on the least squares fitting, according to the time The obtained polynomial coefficients are used to obtain the residual sum of squares between the time-varying position prediction data and the actual data; According to the necessary conditions for finding the extreme value of multivariate functions, combined with the residual sum of squares between the time-varying position prediction data and the actual data, the analytical solution expressions of the relevant parameters are obtained; ECEF coordinate system The data in the axial direction are brought into the analytical solution expressions of the relevant parameters to obtain the values ​​of the relevant parameters, thus obtaining the interceptor in the ECEF coordinate system. Axis direction data corresponding to polynomial of order; Similarly, according to the ECEF coordinate system Axis direction data and Axis direction data, get Axis and Axis direction data corresponding to polynomial of order; according to axis, Axis and Axis direction data corresponding to The order polynomial, combined with the required prediction duration, obtains the interceptor in the ECEF coordinate system within the required duration. Coordinate information; According to the required time, the interceptor is located in the ECEF coordinate system The coordinate information is combined with the position information of the aircraft in the predicted time period, and the measurement equation is used to perform pseudo-measurement calculation to obtain pseudo-measurement data.

6. The method for inferring air target interception intention based on track prediction according to claim 1, characterized in that: The predicted trajectory of the interceptor and the predicted trajectory of the aircraft are processed by using the improved closest encounter point function. The specific method for calculating the dynamic threat degree of the predicted section is as follows: Calculate the relative position and relative speed of the interceptor to the aircraft based on the predicted trajectory of the interceptor and the predicted trajectory of the aircraft; According to the relative position and relative speed of the interceptor to the aircraft, the closest encounter point distance and the closest encounter point time are calculated; The closest encounter point time is normalized, and the interceptor time threat level value is obtained according to the normalized closest encounter point time; The closest encounter point distance is normalized, and the interceptor distance threat level value is obtained according to the normalized closest encounter point distance; The interceptor time threat level value and the interceptor distance threat level value are corrected by using the interceptor speed and the angle between the interceptor and the aircraft; According to the corrected interceptor time threat level value and interceptor distance threat level value, the dynamic threat level of the predicted section is obtained.

7. The method for inferring air target interception intention based on track prediction according to claim 1, characterized in that: According to the dynamic threat degree of the predicted segment, combined with the intention inference rules, the interception intention is judged and the specific method of obtaining the interception intention matrix at each moment is as follows: Preset intention inference rules and establish a judgment model of dynamic threat level and intention inference rules for prediction segments; Input the predicted segment dynamic threat level into the judgment model to obtain the interception intention at each moment; According to the interception intention at each moment, the interception intention matrix at each moment is obtained.

8. The method for inferring air target interception intention based on track prediction according to claim 1, characterized in that: According to the interception intention matrix at each moment, the specific method of determining the center point of the interception encounter area, calculating the radius of the interception encounter area, and obtaining the closest distance between the interceptor and the aircraft is as follows: According to the interception intention matrix at each moment, the corresponding interceptor and aircraft are extracted, and the interception encounter area is predicted according to the interception intention matrix of the interceptor and the aircraft. The interception encounter area includes the set of points where the interceptor can hit the aircraft; According to the predicted track of the interceptor and the predicted track of the aircraft, the shortest distance between the interceptor and the aircraft is obtained.

9. The method for inferring air target interception intention based on track prediction according to claim 1, characterized in that: According to the shortest distance between the interceptor and the aircraft, the specific method for determining the center point and radius of the interception encounter area is as follows: At the moment when the interceptor and the aircraft are closest, the midpoint of the line connecting the predicted track of the interceptor and the predicted track of the aircraft is the center point of the interception encounter area; The radius of the interception encounter zone is obtained based on the closest distance between the interceptor and the aircraft and the predicted time for the interceptor and the aircraft to reach the closest distance.

10. The air target interception intention inference system based on trajectory prediction is characterized by: The following steps are involved: An information acquisition module, used to obtain historical status information of interceptors and aircraft; A filtering module is used to perform two-way filtering and smoothing processing on the historical state information of the interceptor and the aircraft, respectively, to obtain the state estimation result and covariance matrix after two-way filtering and smoothing; A pseudo two-sided data acquisition module is used to generate pseudo measurement data based on polynomial fitting according to the state estimation result and covariance matrix after bilateral filtering and smoothing; The predicted track acquisition module is used to perform augmented interactive multi-model-volumetric Kalman filtering on the pseudo-measurement data, and use the filtered pseudo-measurement data to update the historical state information of the interceptor and the aircraft to obtain the predicted track of the interceptor and the predicted track of the aircraft; A threat degree acquisition module is used to process the predicted trajectory of the interceptor and the predicted trajectory of the aircraft using an improved closest encounter point function to calculate the dynamic threat degree of the predicted section; The interception intention matrix acquisition module is used to determine the interception intention based on the dynamic threat level of the predicted segment and the intention inference rules to obtain the interception intention matrix at each moment; A closest distance acquisition module is used to obtain the closest distance between the interceptor and the aircraft based on the interception intention matrix at each moment and the predicted trajectory of the interceptor and the predicted trajectory of the aircraft; The interception encounter area acquisition module is used to determine the center point and radius of the interception encounter area according to the shortest distance between the interceptor and the aircraft.

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