Method for calculating the speed of a moving terrestrial target based on optical sighting and ranging

Abstract

The application relates to a method for calculating the moving speed of a ground moving target based on optical aiming and ranging, belonging to the field of design of a guidance control system. Optical aiming and ranging information collected by an optical-electric aiming system of an unmanned aerial vehicle platform and navigation information collected by a navigation system are utilized, a corresponding transfer matrix and a CKF filter model are constructed according to the information, then the final target moving speed information is obtained according to the output result of the filter model and the speed information of the unmanned platform, so that the moving speed of the target is accurately calculated, and the guidance precision is effectively improved.

Description

Technical Field

[0001] This invention relates to a method for calculating the velocity of a ground-moving target based on optical aiming and ranging, belonging to the field of guidance and control system design. Background Technology

[0002] With the development of the integrated reconnaissance and strike UAV combat mode, various short-range air-to-ground guided weapons have emerged to meet the requirements of payload capacity, accuracy, and cost-effectiveness. In particular, semi-active laser-guided weapons have become one of the standard weapons for UAVs due to their high accuracy in short-range situations.

[0003] Semi-active laser-guided weapons currently employ two operational modes: pre-launch lock-on and post-launch lock-on. Pre-launch lock-on is limited by the interception range of the semi-active laser seeker, resulting in a shorter range for semi-active laser-guided weapons, far shorter than the laser illumination range of UAV platforms. Post-launch lock-on significantly increases the range of semi-active laser-guided weapons, making it comparable to the laser illumination range of UAV platforms. Therefore, post-launch lock-on is the preferred operational mode for semi-active laser-guided weapons. Most of these weapons employ a composite guidance system of inertial mid-course guidance and terminal guidance. Inertial mid-course guidance largely uses the target's position at the last moment before launch for calculation. Therefore, when the target is moving, the accuracy of mid-course guidance will be severely affected, even impacting the probability of interception by terminal guidance. Summary of the Invention

[0004] In view of this, the present invention proposes a method for calculating the speed of a ground-based moving target based on optical aiming and ranging. By utilizing the optical aiming and ranging information collected by the optoelectronic aiming system and navigation system of the UAV platform, the speed of the moving target can be accurately calculated, effectively improving guidance accuracy.

[0005] The technical solution for implementing the present invention is as follows:

[0006] A method for calculating the velocity of a ground-moving target based on optical aiming and ranging includes the following steps:

[0007] Step 1: Electro-optical aiming system of the UAV platform. The electro-optical aiming system acquires the slant range R and line-of-sight elevation angle q between the machine and the target. e and azimuth q b The navigation system acquires attitude angle information, UAV platform speed, UAV-target relative position information (x, y, z), and UAV-target relative velocity information, which are respectively V x V y V z ;

[0008] Step 2: Construct the transition matrix C based on the attitude angle information;

[0009] Step 3: Construct a CKF filter model based on the information obtained from the electro-optical aiming system and the navigation system. Input the relative position information between the aircraft and the target, the relative velocity information between the aircraft and the target, and the transfer matrix C into the model to obtain the result X. k Recorded as

[0010] Step 4: Based on the results The final target's speed information is obtained by combining the transfer matrix C and the UAV platform speed.

[0011] Furthermore, the construction of the CKF filter model based on the information obtained from the optoelectronic aiming system and the navigation system includes the following:

[0012] The relative position information x, y, z of the aircraft and the relative velocity information of the aircraft and the eye are respectively V x V y V z The state equation is set as follows:

[0013] [x1 x2 x3 x4 x5 x6] T =[xyz V x V y V z ] T

[0014] Based on the above state equations, the CKF filter model is constructed by the following discretized state-space equations and observation equations:

[0015]

[0016]

[0017] Furthermore, the relative position information between the aircraft and the target, the relative velocity information between the aircraft and the target, and the transfer matrix C are input into the model to obtain the result X. k Recorded as Includes the following steps:

[0018] The relative position information x, y, z of the aircraft and the relative velocity information of the aircraft and the eye are respectively V x V y V z During the calculation process, V x V y V z The value is a set value;

[0019] [x1 x2 x3 x4 x5 x6] T =[xyz V x V y Vz ] T Substituting [x1(k-1)x2(k-1)x3(k-1)x4(k-1)x5(k-1)x6(k-1)] into the CKF filter model T The transition matrix C is directly substituted into the filter model to obtain the result X. k Recorded as

[0020] Furthermore, step two includes the following steps:

[0021] Based on the attitude angle information: pitch angle θ, yaw angle ψ, and roll angle γ, the following transformation matrix C is constructed:

[0022]

[0023] Furthermore, step four includes the following:

[0024] The filter calculation results are recorded as follows: Drone platform speed information v xa v ya v za Then, we can construct the following equation to obtain:

[0025]

[0026] Among them, v xt v yt v zt The final velocity information of the target.

[0027] Beneficial effects:

[0028] First, this invention addresses the problem of reduced guidance accuracy in short-range semi-active laser-guided weapons during the post-launch lock-on combat mode caused by target movement. When using this method for calculation, the CKF filter model is constructed to take into account the three-dimensional velocity of the moving target, thereby improving the calculation accuracy and effectively enhancing guidance accuracy.

[0029] Secondly, the present invention uses the CKF filter model. Compared with other commonly used methods in the field, the CKF filter model is applicable to nonlinear systems and can overcome the divergence or accuracy degradation of the system.

[0030] Third, this invention addresses the current practice of using the target position at the last moment before launch for calculation. The optical aiming and ranging information obtained is collected in real time from the UAV platform, ensuring higher accuracy of this system. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the operation of the present invention.

[0033] Figure 2 This is a schematic diagram of the body coordinate system designed for this invention. Detailed Implementation

[0034] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0035] This embodiment presents a method for calculating the velocity of a ground-moving target based on optical aiming and ranging (as shown in the attached figure). Figure 1 As shown), it includes the following steps:

[0036] Step 1: The electro-optical aiming system of the UAV platform acquires ranging information including: the slant distance R between the UAV and the eye, and the line-of-sight elevation angle q. e and azimuth q b The navigation system acquires optical aiming information including: attitude angle information, UAV platform speed, UAV-eye relative position information (x, y, z), and UAV-eye relative velocity information (V). x V y V z ;

[0037] In this embodiment, the aircraft coordinate system is set as the front-upper-right coordinate system. According to the definition of the aircraft coordinate system, the attitude angles of the aircraft and the navigation system (north-sky-east) are as follows: θ - pitch angle, pointing north to the navigation system; ψ - yaw angle, pointing skyward to the navigation system; γ - roll angle, pointing eastward to the navigation system.

[0038] Step 2: Construct the transition matrix C based on the attitude angle information;

[0039] Based on the attitude angle information, the following transformation matrix C is constructed:

[0040]

[0041] Step 3: Construct a CKF filter model based on the information obtained from the electro-optical aiming system and navigation system. Input the relative position information between the aircraft and the target, the relative velocity information between the aircraft and the target, and the transfer matrix C into the model to obtain the result X. k Recorded as

[0042] In this embodiment, the position x of the UAV under the navigation system is recorded.A y A z A Target position x T y T z T The relative position A between the machine and the target in the machine system, the slant distance R between the machine and the target output from the pod, and the elevation angle q of the line of sight. e and azimuth q b The following relationship exists:

[0043] A = C·[x T -x A y T -y A z T -z A ] T

[0044]

[0045] q e =arcsin(A(2,1) / R)

[0046] q b =arctan(A(3,1) / A(1,1))

[0047] The relative position information x, y, z of the aircraft and the relative velocity information of the aircraft and the eye are respectively V x V y V z The state equation is set as follows:

[0048] [x1 x2 x3 x4 x5 x6] T =[xyz V x V y V z ] T

[0049] This implementation details the specific solution process, including X. k Record as the final result

[0050] ① Based on the above state equations, the CKF filter model is constructed, which includes the following discretized state-space equations and observation equations:

[0051]

[0052]

[0053] ②State parameter initialization:

[0054] (x0, y0, and z0 are the calculation results of A in step (2))

[0055] The above equation is equivalent to resolving the state equation [x1 x2 x3 x4 x5 x6] T =[xyz V x V y V z ] T Substituting [x1(k-1) x2(k-1) x3(k-1) x4(k-1) x5(k-1) x6(k-1)] into the CKF filter model T ;

[0056] P0 = diag([100 100 100 100])

[0057] Q k =diag([1 1 1 100 100 100]) T / 100000

[0058] R k =diag([1 1 1]) T / 100000

[0059] ③Time update:

[0060] (Weight matrix, n=6)

[0061]

[0062] ④ Calculate the volume point:

[0063] S k-1 =chol(P k_1 ) T (UT decomposition)

[0064] (ζ i Let ζ be the i-th column vector, where i takes values ​​from 1 to 2n.

[0065] (t is the filtering period)

[0066] ⑤ Calculation of predicted values ​​and covariance matrix:

[0067]

[0068]

[0069] ⑥ Measurement Update:

[0070] (UT decomposition)

[0071]

[0072] Z i,k =H(X) i,k )

[0073]

[0074]

[0075]

[0076] ⑦ Status Update:

[0077] K k =P xz,k / k-1 ·(P zz,k / k-1 ) -1

[0078]

[0079]

[0080] Step 4: Based on the results The final target's speed information is obtained by combining the transfer matrix C and the UAV platform speed.

[0081] The filter calculation results are recorded as follows: Drone platform speed information v xa v ya v za Then, we can construct the following equation to obtain:

[0082]

[0083] Among them, v xt v yt v zt The final velocity information of the target.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating the speed of a moving ground target based on optical aiming and ranging, characterized in that, It comprises the following steps: Step one, the photoelectric aiming system of the UAV platform acquires the slant range R and the line-of-sight angle q e and the azimuth angle q b , the navigation system acquires the attitude angle information, the UAV platform speed, the relative position information x, y, z and the relative speed information V x , V y , V z ; Step two, constructing a transfer matrix C according to the attitude angle information; Step three, constructing CKF filter model according to information acquired by the photoelectric sighting system and the navigation system, inputting the machine-eye relative position information, the machine-eye relative velocity information and the transfer matrix C into the CKF filter model, and obtaining a result X k denoted as Step four, according to the results The transfer matrix C and the UAV platform velocity, obtain the final target motion velocity information.

2. The method of claim 1, wherein, The CKF filter model is constructed according to the information obtained by the photoelectric aiming system and the navigation system, and comprises the following contents: From the machine-eye relative position information x, y, z and the machine-eye relative velocity information V x , V y , V z , the state equation is set as: [x1 x2 x3 x4 x5 x6] T = [x y z V x V y V z ] T According to the above state equation, the CKF filter model is constructed, comprising the following discrete state space equation and observation equation:

3. The method of claim 1 or 2, wherein, The machine-target relative position information, the machine-target relative speed information and the transfer matrix C are input into a CKF filter model to obtain a result X k denoted as comprising the steps of: The machine-eye relative position information x, y, z and the machine-eye relative speed information V x , V y , V z , the value of V x , V y , V z during the calculation is a set value; [x1 x2 x3 x4 x5 x6] T = [x y z V x V y V z ] T Substitute [x1(k-1) x2(k-1)x3(k-1) x4(k-1) x5(k-1) x6(k-1)] T into the CKF filter model, the transition matrix C is directly substituted into the CKF filter model, and the result X k is recorded as 4. The method of claim 1, wherein, The step two comprises the following steps: According to the attitude angle information: pitch angle θ, yaw angle ψ, roll angle γ, the following conversion matrix C is constructed:

5. The method of claim 1, wherein, The step four comprises the following contents: According to the filter calculation result and denoted as UAV platform velocity information v xa , v ya , v za Then, the following equation is constructed: where v xt , v yt , v zt are the final motion velocity information of the target.

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