Integrated method and system for target cooperative detection and guidance based on traceless transformation

Abstract

The present invention belongs to the field of space infrastructure application in the high-end equipment manufacturing industry, and particularly relates to a method and system for integrated target collaborative detection and guidance based on traceless transformation, which uses an air-to-ground missile mathematical model as an aircraft dynamics model, constructs state equations and observation equations for aircraft and targets based on the aircraft dynamics model, and obtains observation data of sensors in each aircraft; performs data fusion on the observation data of each aircraft sensor, and uses state equations and observation equations to obtain target positioning estimation data; for target positioning estimation data, regards the guidance process as a random vector nonlinear propagation process, calculates the aircraft flight control angle through traceless transformation, and modifies the aircraft flight trajectory and angle based on the flight control ratio. The present invention can ensure that the aircraft maintains a good flight attitude in the process of detecting and striking targets, effectively reduces overload, improves positioning accuracy, and is convenient for application in actual scenarios.

Description

Technical Field

[0001] The present invention belongs to the field of space infrastructure application in high-end equipment manufacturing industry, and in particular relates to an integrated method and system for target collaborative detection and guidance based on traceless transformation. Background Art

[0002] At present, in the space battlefield where aircraft are used to detect ground targets in the mobile measurement and control of spacecraft, it is difficult for a single aircraft to achieve effective detection, positioning and precise strikes in the face of complex environments on the battlefield, and aircraft cooperative detection can effectively solve this problem. In the process of aircraft cooperative detection and strikes on ground targets, positioning accuracy is the main factor to be considered. After determining the launch configuration of the aircraft, the information fusion method is used to realize information transmission and signal processing between aircraft. At the same time, in order to achieve accurate detection and strikes of aircraft during flight, it is necessary to better control the flight attitude of the aircraft. First of all, the initialization design of positioning, filtering algorithm and other aspects should be designed. Secondly, it is necessary to plan the types and quantities of sensors carried by aircraft and the data transmission methods between each other to complete the information fusion between each other. Therefore, it is very important to select the appropriate sensor type and information fusion method to process the data to improve the accuracy and detection capability of cooperative detection.

[0003] In terms of collaborative detection data processing, most of the information fusion technologies involved in collaborative detection are studies on the model establishment of maneuvering targets, and there is little quantitative analysis of the specific methods of information fusion. In terms of the integrated design of collaborative detection and guidance, most of the research on guidance laws is based on theoretical derivation when the target is known, and there are few studies on convenient and real-time methods for designing guidance laws using detection data. Since it is difficult to obtain the precise position of the target in actual scenarios, most guidance methods are not suitable for the detection and attack of unknown targets. In addition, when detecting and attacking targets, the aircraft needs to meet certain angle constraints and avoid large overloads. Therefore, it is necessary to study the guidance law for speed direction control. In addition, the estimated value of the ground target position is different at every moment. When designing the flight trajectory and flight attitude of the aircraft, it is necessary to consider the estimated position of the target point in real time. The original guidance law method under the condition of known targets cannot meet the actual application conditions. Summary of the invention

[0004] To this end, the present invention provides an integrated method and system for collaborative target detection and guidance based on traceless transformation, which introduces the target position estimation information obtained from the detection data into the guidance law of speed direction control to achieve the integration of collaborative detection and guidance, ensuring that the aircraft maintains a good flight attitude during the process of detecting and striking targets, effectively reducing overload, improving positioning accuracy, and facilitating application in actual scenarios.

[0005] According to the design scheme provided by the present invention, a target cooperative detection and guidance integrated method based on traceless transformation is provided for planning the flight path of an aircraft, which includes the following contents:

[0006] The mathematical model of air-to-ground missile is used as the aircraft dynamics model, and the state equations and observation equations of the aircraft and the target are constructed according to the aircraft dynamics model, and the observation data of the sensors in each aircraft are obtained;

[0007] By fusing the observation data of each aircraft sensor, and using the state equation and the observation equation to obtain the target positioning estimation data, wherein the target positioning estimation data at least includes: the target state estimation value and the covariance estimation;

[0008] According to the target positioning estimation data, the guidance process is regarded as a random vector nonlinear propagation process. The flight control angle of the aircraft is calculated through unscented transformation, and the flight trajectory and angle of the aircraft are modified according to the flight control angle.

[0009] As an integrated target collaborative detection and guidance method based on traceless transformation of the present invention, further, when acquiring target positioning estimation data, for the constructed ground target state equation and the observation equation of each sensor corresponding to the aircraft, first, the aircraft attitude data is determined using the observation information of the radar seeker at the initial moment, and the attitude data at least includes: position, speed and attitude angle; then, the observation data of each aircraft sensor is fused.

[0010] As the target cooperative detection and guidance integration method based on unscented transformation of the present invention, further, in the data fusion process, each sensor sends the observation data to the main filter respectively, and the main filter uses the unscented Kalman filter UKF algorithm for fusion. The fusion process is expressed as: in, represents the estimated state value at time k+1, represents the covariance estimate at time k+1, represents one-step state estimation, represents the one-step covariance estimate, represents the sensor one-step covariance prediction autocorrelation, represents the one-step covariance prediction cross-correlation, Y k+1represents the observation vector at time k+1, represents the one-step prediction value based on time k, and m represents the sensor.

[0011] As the target cooperative detection and guidance integrated method based on unscented transformation of the present invention, further, the main filter uses the unscented Kalman filter UKF algorithm to perform filtering processing, which includes the following contents: using and P k+1 / k =I 3×3 P k / k I 3×3 To predict the state and covariance at time k+1, where I 3×3 It is a 3×3 unit matrix; Sigma points and corresponding weights are constructed based on the predicted state and covariance, and the target state estimate and covariance estimate at time k+1 are predicted based on the solution of the nonlinear propagation process of the observation equation.

[0012] As an integrated target collaborative detection and guidance method based on traceless transformation of the present invention, further, for the target positioning estimation data acquired in real time, the terminal guidance process is utilized to obtain the flight control angle through the guidance law of the falling speed direction control, and the flight trajectory and angle of the aircraft are corrected in real time using the flight control angle.

[0013] As the integrated target collaborative detection and guidance method based on traceless transformation of the present invention, further, the guidance law of falling speed direction control is used to calculate the flight control angle corresponding to each Sigma point, wherein the flight control angle includes: angle of attack and sideslip angle state vectors.

[0014] As an integrated target collaborative detection and guidance method based on traceless transformation of the present invention, further, the position and velocity sight line vector coordinates of each aircraft in the guidance coordinate system are obtained in combination with the terminal guidance process; according to the position and velocity sight line vector coordinates, the gravitational acceleration vector and aerodynamic parameters in the semi-velocity system are combined, and the center of mass dynamics equation and the guidance equation are used to obtain the angle of attack and sideslip angle state vectors, and the Sigma points and corresponding weights are used to calculate the corresponding mean and covariance to determine the flight control angle.

[0015] Furthermore, the present invention also provides an integrated method for target cooperative detection and guidance based on traceless transformation, comprising: a model building module, a data fusion module and a flight control module, wherein:

[0016] A model building module is used to use the mathematical model of the air-to-ground missile as the aircraft dynamics model, to build the state equations and observation equations of the aircraft and the target according to the aircraft dynamics model, and to obtain the observation data of the sensors in each aircraft;

[0017] A data fusion module is used to fuse the observation data of each aircraft sensor and obtain target positioning estimation data by using the state equation and the observation equation, wherein the target positioning estimation data at least includes: target state estimation value and covariance estimation;

[0018] The flight control module is used to estimate the data for target positioning, use the guidance algorithm to regard the guidance process as a random vector nonlinear propagation process, calculate the aircraft flight control angle through untraceable transformation, and modify the aircraft flight trajectory and angle based on the flight control angle.

[0019] Beneficial effects of the present invention:

[0020] The present invention introduces a cooperative detection target positioning algorithm into the guidance law, uses the fused target position estimation mean and variance as the input of the guidance algorithm, and regards the guidance process as a nonlinear propagation process of a random vector. The mean and variance of the flight control angle are obtained by using traceless transformation. The obtained flight control angle and overload are different from those when the target is known, and are more in line with the actual application scenario conditions. This also provides a theoretical basis for the aircraft guidance design in practical applications. At the same time, compared with single aircraft detection, cooperative detection can not only improve the detection accuracy, but also meet the lower overload requirements, and can better control the speed direction and control angle of the aircraft. Compared with the traditional decoupled positioning and guidance design, this solution can change the aircraft's own attitude in real time according to the estimated target position, which is closer to reality, has better applicability and higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the integrated process of target cooperative detection and guidance based on traceless transformation in the embodiment;

[0022] Figure 2 A diagram showing the geometric relationship between the aircraft and the target in the embodiment;

[0023] Figure 3 This is a schematic diagram of a centralized fusion structure in an embodiment;

[0024] Figure 4 It is a schematic diagram of the communication requirements between centralized fusion aircraft in the embodiment;

[0025] Figure 5 It is a schematic diagram of the integrated process of target positioning estimation data and speed direction control guidance in the embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0027] In the traditional guidance control design process, the target positioning tracking and guidance algorithm are usually decoupled. However, in fact, the guidance algorithm needs parameters such as the real-time estimated target position as input, and the guidance algorithm will also affect the construction of the target tracking state and measurement equations and the observation conditions. There is a high degree of coupling between the two. Decoupling and designing them separately will cause certain information loss and reduce the estimation and guidance accuracy. To this end, an embodiment of the present invention provides an integrated method of target cooperative detection and guidance based on traceless transformation for aircraft path planning, see Figure 1 As shown, it contains the following contents:

[0028] S101, using the air-to-ground missile mathematical model as the aircraft dynamics model, constructing the state equation and observation equation of the aircraft and the target according to the aircraft dynamics model, and obtaining the observation data of the sensors in each aircraft;

[0029] S102, fusing the observation data of each aircraft sensor and obtaining target positioning estimation data by using the state equation and the observation equation, wherein the target positioning estimation data at least includes: a target state estimation value and a covariance estimation;

[0030] S103. With respect to the target positioning estimation data, the guidance process is regarded as a random vector nonlinear propagation process, the flight control angle of the aircraft is calculated through untraceable transformation, and the flight trajectory and angle of the aircraft are modified according to the flight control angle.

[0031] By using the detection data of ground targets from multiple aircraft and information fusion technology for signal processing, the flight path of the aircraft is planned through the integration of collaborative detection and guidance based on traceless transformation. This can not only improve the detection accuracy, but also better control the flight attitude, effectively reduce overload, and facilitate application in actual scenarios.

[0032] As an integrated target collaborative detection and guidance method based on traceless transformation of the present invention, further, when acquiring target positioning estimation data, for the constructed ground target state equation and the observation equation of each sensor corresponding to the aircraft, first, the aircraft attitude data is determined using the observation information of the radar seeker at the initial moment, and the attitude data at least includes: position, speed and attitude angle; then, the observation data of each aircraft sensor is fused.

[0033] Furthermore, during the data fusion process, each sensor sends the observation data to the main filter, and the main filter uses the unscented Kalman filter UKF algorithm for fusion. For the target positioning estimation data acquired in real time, the terminal guidance process is used to obtain the flight control angle through the guidance law of the falling speed direction control, and the flight trajectory and angle of the aircraft are corrected in real time using the flight control angle. The terminal guidance process is combined to obtain the position and velocity line of sight vector coordinates of each aircraft in the guidance coordinate system; based on the position and velocity line of sight vector coordinates, combined with the gravitational acceleration vector and aerodynamic parameters in the semi-velocity system, and using the center of mass dynamics equation and guidance equation to obtain the angle of attack and sideslip angle state vectors, the Sigma point and the corresponding weights are used to calculate the corresponding mean and covariance to determine the flight control angle.

[0034] Take two aircraft as an example, one of which carries an infrared sensor and the other carries a radar sensor. The dynamic model of the aircraft is set to the dynamic model of an air-to-ground missile. Specifically, the design includes the following steps:

[0035] Step 1: Establish the observation equations and state equations of the aircraft and ground targets.

[0036] Step 2: Describe the initialization positioning algorithm and filtering algorithm.

[0037] Step 3: Introduce and describe the centralized fusion algorithm.

[0038] Step 4: Use the unscented transformation method to integrate the target location estimation data with the speed and direction control guidance.

[0039] Among them, in step 1, in establishing the aircraft state equation and observation equation,

[0040] State equation: The state vector is set to the position vector of the target in the ground-fixed system

[0041] X=[X T Y T Z T ] T (1)

[0042] Since the target is a stationary target, the state equation can be established as

[0043] X k+1 =I 3×3 X k (2)

[0044] That is, the state transfer matrix is ​​a third-order unit matrix.

[0045] Observation equation:

[0046] (1) For infrared sensors, only pitch angle and azimuth angle information is measured, that is, it is necessary to establish a functional relationship between the observation vector Z(A, E) and the state vector X. Assume that the position vector of the aircraft in the ground fixed system is

[0047] X M =[X M Y M Z M ] T (3)

[0048] The line of sight vector between the aircraft and the target can be expressed in the ground-fixed system as

[0049]

[0050] The line of sight vector of the earth-fixed system is converted to the field of view (geocentric inertial) coordinate system, which can be expressed as:

[0051]

[0052] in, is the transformation matrix from L system to AB system, is the conversion matrix from GF to L system.

[0053] Calculate the observation vector using the sight vector in the field of view coordinate system

[0054]

[0055] (2) For radar sensors, the measurement quantities include pitch angle, azimuth angle, and distance. Compared with infrared sensors, the distance quantity is added. Therefore, the observation vector can be expressed as:

[0056]

[0057] In the initialization algorithm and filtering algorithm of step 2,

[0058] (1) Initialization positioning method

[0059] Since the radar sensor can measure the distance, pitch angle, and azimuth information, the observation information of the radar seeker at the initial moment can be used for initialization design.

[0060] The target position and velocity estimation initialization method can be expressed as follows:

[0061] Given the initial radar measurement data (R0, A0, E0), the corresponding standard deviations are σ R σ A σ E . Then the covariance matrix of the measured data is

[0062]

[0063] Convert the sight vector from RS coordinate system to ECF system

[0064]

[0065] and Represent the corresponding coordinate transformation matrices respectively.

[0066] Then the position vector of the target in the ground fixed system is

[0067]

[0068] With the function equation from the measured data to the state vector, as well as the mean and variance of the measured data, the error propagation analysis can be performed using unscented transformation. Since the seeker does not add terminal guidance in the initial process, its position, velocity, attitude angle and other information can be determined based on the initial information emitted.

[0069] Since the infrared sensor can only measure the two angles of the line of sight vector, it cannot measure the distance and cannot form a three-dimensional positioning of the target. It is necessary to introduce other constraints for positioning. Since the positioning target is a ground target, it is possible to consider using two sensors to coordinate initialization, predict the distance between one of the sensors and the target, and further determine the position of the target.

[0070] The position relationship between the aircraft and the target is as follows: Figure 2 As shown in the figure, M1 and M2 represent the initial position of the aircraft, the dotted line represents the detected sight vector, T is the target position, and O-XYZ is the ground-fixed coordinate system. According to the positional relationship of ΔM1M2T and the detection vector information, the distance between any missile and the target can be obtained.

[0071] Knowing the positions of M1 and M2 under the ground fixed system, the position vector can be expressed as: a =, O, M-1.. and The lengths of the sides of ΔM1M2T are R, L, and N respectively. In the AB system of M1, L can be expressed as:

[0072]

[0073] in, is the coordinate transformation matrix from ECF system to L system, It is the coordinate transformation matrix from L system to AB system.

[0074] The magnitude of the vector L can be expressed as:

[0075] c=||ba|| (12)

[0076] Take any point e1 and assume its value is 1×106 m, then it can be expressed in the projectile coordinate system as

[0077]

[0078] Angle M1 can be expressed as:

[0079]

[0080] Similarly, we can find the angle M2

[0081]

[0082] The positional relationship of the triangles can be expressed as follows:

[0083]

[0084] The value of R can be obtained from formula (16), which can be expressed in the aircraft body coordinate system as:

[0085]

[0086] The vector R can be transformed into the ground fixed system as follows:

[0087]

[0088] Then the coordinates of the target in the ground fixed system can be determined

[0089] Τ ECF =R ECF +M ECF (19)

[0090] In this way, the distance between any aircraft and the target can be determined. With the function equation from the measurement data to the state vector, as well as the mean and variance of the measurement data, the unscented transform (UT) can be used for error propagation analysis.

[0091] (2) Filtering algorithm

[0092] Regarding the position estimation of stationary targets, the state equation can be considered to be linearized, but the observation equation also has strong nonlinear properties, and the observation matrix (the first-order partial derivative matrix of the observation equation) is difficult to solve. Therefore, the state equation can use the standard KF method, while the observation equation needs to use the UT method. This combination of linearization method and UT method can not only simplify the calculation, but also minimize the prediction error. The complete process of filtering once is as follows:

[0093] Status prediction

[0094]

[0095] Covariance prediction

[0096] P k+1 / k =I 3×3 P k / k I 3×3 (twenty one)

[0097] Construct Sigma points and corresponding weights based on state prediction values

[0098]

[0099] Nonlinear Propagation Based on Observation Equation

[0100] Z k+1 / k (i) = h(χ k+1 / k (i)),i=0,1,···,2n (23)

[0101] Compute estimates and covariance matrices for observations

[0102]

[0103] Solve for innovation and innovation covariance

[0104]

[0105] Target estimation vector and covariance prediction

[0106]

[0107] In the centralized fusion method of step 3, the centralized fusion is to send the observation data of different sensors to the main filter, complete the filter fusion processing uniformly, and then track and classify the target. Figure 3 As shown, this method can achieve higher accuracy. However, a large amount of data needs to be transmitted through the main filter, which further increases the amount of calculation. The requirements for both communication links and data processors are extremely high, and the reliability of data processing is relatively low.

[0108] Each sensor transmits the measured data to the main filter in the fusion center, and the main filter fuses all the measured data through the unscented (lossy) Kalman filter (UKF) algorithm. The output formula after fusion is:

[0109]

[0110] represents the estimated state value at time k+1, represents the covariance estimate at time k+1, represents one-step state estimation, represents the one-step covariance estimate, represents the sensor one-step covariance prediction autocorrelation, represents the one-step covariance prediction cross-correlation, Y k+1 represents the observation vector at time k+1, Represents the one-step prediction value based on time k (all the following algorithms are expressed in the same way).

[0111] If centralized data fusion is used, the main filter will be placed on one of the aircraft, and the communication transmission requirements between aircraft Figure 4 As shown. The data that the secondary aircraft needs to transmit to the main aircraft include two categories: one is the aircraft's own parameters required to build the measurement equation, and the other is its own measurement data. The data transmitted by the main missile to the secondary aircraft is the target's motion characteristic information, which is used for the secondary aircraft's line of sight turn rate estimation and guidance control.

[0112] Step 4 uses the traceless transformation method to integrate the target positioning estimation data with the speed direction control guidance. The process is as follows: Figure 5 As shown in the figure, the target position estimation data at the new moment obtained in the collaborative detection filter estimation is substituted into the terminal guidance equation, and the flight control angle and coordinates in the dynamic model are calculated using the UT method to correct the flight trajectory and angle in real time. It can avoid the complex calculation of angular rate extraction through communication between aircraft, and can better control the attitude angle of the aircraft, improve positioning accuracy, and reduce flight overload.

[0113] Assume that there are i different types of seekers (radar and infrared) to coordinately locate the target. Through information fusion technology, they get the estimated position of the target, introduce the guidance law of falling speed direction control, and solve the guidance parameters. At the same time, in order to eliminate the error caused by filtering, the traceless transformation method is adopted. By inputting the estimated position and covariance matrix of the target, the angle of attack, sideslip angle and their corresponding covariance matrix are output, which further improves the estimation accuracy of the two attitude angles and reduces overload. The specific design is as follows:

[0114] (1) First, 2n+1 sigma points and their weights W are constructed, where the covariance matrix decomposed by the Cholesky method is the covariance matrix of the target position estimation.

[0115] (2) Use the velocity direction control method to calculate the state vector of the angle of attack and sideslip angle corresponding to each point:

[0116] [alpha(i),beta(i)]=f(sigma(i)),i=0,1,L 2n (28)

[0117] Among them, alpha and beta represent the angle of attack and sideslip angle of the aircraft respectively, and f(·) is the velocity direction control guidance function. The specific expression method is as follows:

[0118] The position estimate of the target in the ground fixed system can be solved by using the collaborative detection algorithm And input it into the guidance law design module in real time:

[0119]

[0120] Among them, L T B T H T are the geodetic longitude, latitude and altitude of the target’s estimated position at time k+1, is the transformation matrix from ECF system to G system.

[0121] Establish the guidance coordinate system (TC, i.e., the north celestial east coordinate system with the estimated position of the target at time k as the origin), then the sight vector coordinates of the position and velocity of the i-th aircraft in the TC system can be expressed as

[0122]

[0123]

[0124] Among them, r i,ECF is the position of the i-th aircraft in the ECF system at time k, v i,ECF The velocity of the i-th aircraft in the ground-fixed system, is the transformation matrix from ECF system to TC system.

[0125] The velocity inclination in the TC system is

[0126]

[0127] The velocity yaw angle σ in the TC frame i , sight height angle λ D,i , line of sight azimuth λ T,i , the line of sight vector length r Los,i They can be expressed as

[0128]

[0129] The velocity vector in the line of sight coordinate system (SC) is

[0130]

[0131] is the transformation matrix of TC and SC coordinate systems at different positions

[0132] Aircraft predicted flight time T g , the rate of change of sight height angle The rate of change of the sight angle The projection of the absolute angular velocity of rotation in the velocity direction on the ζ axis of the SC system Projection of the absolute angular velocity of the bullet speed direction on the η axis of the SC system They are respectively expressed as follows:

[0133]

[0134] K GD =-4, K LD =-2, K GT =3 is the optimal guidance coefficient, γ DF It is the landing angle constraint of the aircraft.

[0135] Define the half velocity system (HVC), the gravitational acceleration vector in the half velocity system is

[0136]

[0137] where g ECF is the gravitational acceleration of the earth, is the transformation matrix between TC and HVC. Let g i,yh and g z,yh are the gravitational acceleration components in the half-velocity system respectively.

[0138] Aerodynamic acceleration components a of the y-axis and z-axis of the half-speed system i,yh a i,zh for

[0139]

[0140] The required aerodynamic parameters of the y-axis and z-axis of the half-speed system can be calculated based on the aerodynamic acceleration.

[0141]

[0142] The angle of attack α and the sideslip angle β can be determined by the above-mentioned maneuvering reentry center of mass dynamics equation and guidance equation.

[0143] (3) Use the sigma point to calculate the corresponding mean E[alphabeta] and covariance

[0144]

[0145] Among them, W i is the weight value.

[0146] Furthermore, based on the above method, an embodiment of the present invention also provides an integrated method of target cooperative detection and guidance based on traceless transformation, comprising: a model building module, a data fusion module and a flight control module, wherein:

[0147] A model building module is used to use the mathematical model of the air-to-ground missile as the aircraft dynamics model, to build the state equations and observation equations of the aircraft and the target according to the aircraft dynamics model, and to obtain the observation data of the sensors in each aircraft;

[0148] A data fusion module is used to fuse the observation data of each aircraft sensor and obtain target positioning estimation data by using the state equation and the observation equation, wherein the target positioning estimation data at least includes: target state estimation value and covariance estimation;

[0149] The flight control module is used to estimate the data for target positioning, use the guidance algorithm to regard the guidance process as a random vector nonlinear propagation process, calculate the aircraft flight control angle through untraceable transformation, and modify the aircraft flight trajectory and angle based on the flight control angle.

[0150] The aircraft control angle and overload obtained using the solution in this case are different from those when the target is known, and are more in line with the actual application scenario conditions, which can provide a theoretical basis for the aircraft guidance design in practical applications.

[0151] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0152] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A target cooperative detection and guidance integrated method based on traceless transformation for planning the flight path of an aircraft, characterized in that: Contains the following: The mathematical model of air-to-ground missile is used as the aircraft dynamics model, and the state equations and observation equations of the aircraft and the target are constructed according to the aircraft dynamics model, and the observation data of the sensors in each aircraft are obtained; By fusing the observation data of each aircraft sensor, and using the state equation and observation equation to obtain the target positioning estimation data, the target positioning estimation data at least includes: target state estimation value and covariance estimation; during the data fusion process, each sensor sends the observation data to the main filter respectively, and the main filter uses the unscented Kalman filter UKF algorithm for fusion. The fusion process is expressed as: represents the estimated value of the state at time k+1, represents the covariance estimate at time k+1, represents one-step state estimation, represents the one-step covariance estimate, represents the sensor one-step covariance prediction autocorrelation, represents the one-step covariance prediction cross-correlation, Y k+1 represents the observation vector at time k+1, represents the one-step prediction value based on time k, and m represents the sensor; the main filter uses the unscented Kalman filter UKF algorithm to filter the processing process, including: using and P k+1 / k = / 3×3 P k / k I 3×3 To predict the state and covariance at time k+1, I 3×3 is a 3×3 unit matrix; construct Sigma points and corresponding weights based on the predicted state and covariance, and predict the target state estimate and covariance estimate at time k+1 based on the solution of the nonlinear propagation process of the observation equation; Based on the target positioning estimation data acquired in real time, the guidance process is regarded as a random vector nonlinear propagation process. The terminal guidance process is used to calculate the aircraft flight control angle through the guidance law of falling speed direction control and traceless transformation. The aircraft flight trajectory and angle are corrected in real time according to the flight control angle.

2. The target cooperative detection and guidance integrated method based on traceless transformation according to claim 1 is characterized in that: When acquiring target positioning estimation data, based on the constructed ground target state equation and the observation equations of each sensor corresponding to the aircraft, first, the aircraft attitude data is determined using the observation information of the radar seeker at the initial moment. The attitude data includes at least: position, velocity and attitude angle; then, the observation data of each aircraft sensor is fused.

3. The integrated target cooperative detection and guidance method based on traceless transformation according to claim 1 is characterized in that: The guidance law of falling speed direction control is used to calculate the flight control angle corresponding to each Sigma point, where the flight control angle includes: angle of attack and sideslip angle state vectors.

4. The target cooperative detection and guidance integrated method based on traceless transformation according to claim 3 is characterized in that: The terminal guidance process is combined to obtain the position and velocity sight vector coordinates of each aircraft in the guidance coordinate system; based on the position and velocity sight vector coordinates, combined with the gravitational acceleration vector and aerodynamic parameters in the semi-velocity system, and using the center of mass dynamics equation and guidance equation to obtain the angle of attack and sideslip angle state vectors, the flight control angle is determined by calculating the corresponding mean and covariance using Sigma points and corresponding weights.

5. A target cooperative detection and guidance integrated system based on traceless transformation, characterized in that: The method according to claim 1 is implemented, comprising: a model building module, a data fusion module and a flight control module, wherein: A model building module is used to use the mathematical model of the air-to-ground missile as the aircraft dynamics model, to build the state equations and observation equations of the aircraft and the target according to the aircraft dynamics model, and to obtain the observation data of the sensors in each aircraft; A data fusion module is used to fuse the observation data of each aircraft sensor and obtain target positioning estimation data by using the state equation and the observation equation, wherein the target positioning estimation data at least includes: target state estimation value and covariance estimation; The flight control module is used to estimate the data for target positioning, use the guidance algorithm to regard the guidance process as a random vector nonlinear propagation process, calculate the aircraft flight control angle through untraceable transformation, and modify the aircraft flight trajectory and angle based on the flight control angle.

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