Two-step filtering design method for estimating missile-target sight angular rate

Abstract

The invention provides a two-step filtering design method for estimating the missile-target sight angular rate. According to the method, missile target information obtained by ground tracking and target maneuvering acceleration information obtained by passive tracking are comprehensively applied to carry out two-step filtering so as to realize line-of-sight angular rate extraction. According to the method, for a large maneuvering target, the target acceleration is estimated through a Jerk model, and then the target acceleration is substituted into an established six-dimensional model for line-of-sight angular rate estimation. A simulation result shows that the method can realize high-precision extraction of the line-of-sight angular rate, and the interception performance of an interception system on a maneuvering target is greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of tracker guidance control, and in particular relates to a "two-step" filter design method for estimating the angular velocity of a missile-target line of sight. Background Art

[0002] The line-of-sight angular rate is used to describe the rate of change of the missile-target line relative to the inertial space, and its accuracy can determine the success or failure of the interception mission. Therefore, extracting high-precision missile-target line-of-sight angular rate based on the relevant measurement information that the tracker can obtain is a key part of the interception mission. The tracker seeker can only measure the line-of-sight angle information of the target relative to the missile body, which is not only coupled with the attitude information of the missile body, but also has the corresponding complex dynamic coupling effect caused by the attitude and orbit control engine of the seeker itself, which brings great difficulties to the extraction of the line-of-sight angular rate. Summary of the invention

[0003] The purpose of the present invention is to solve the problems in the prior art and propose a "two-step" filter design method for estimating the missile-target line-of-sight angular rate.

[0004] The present invention is implemented by the following technical scheme. The present invention proposes a "two-step" filter design method for estimating the missile-target line-of-sight angular rate, and the method comprises the following steps:

[0005] Step 1: The first step is filtering, combining the Jerk model and the extended Kalman filter to estimate the target acceleration through the measured target position and velocity information;

[0006] Step 2: Based on the positional relationship between the target and the tracker, derive the relative motion dynamics equation between the tracker and the target in the line of sight coordinate system;

[0007] Step 3: Combine the filtering results of the first step with the dynamic equations and select appropriate state variables to establish the 6th-order maneuver model of the target;

[0008] Step 4: 'Second step' filtering, based on the 6th order maneuver model, establish a tracking filter model, and design an extended Kalman filter through the relative distance, relative speed and line of sight angle information measured by the seeker to estimate the line of sight angle rate;

[0009] Step 5: Combine the “two-step” filtering and use the estimated line-of-sight angular rate to perform guidance simulation.

[0010] Furthermore, in step 1, the Jerk model is used to describe the changes of the target jerk components on the three axes of the inertial coordinate system of the launch point; the dynamic model of the interception system is

[0011]

[0012] in,

[0013]

[0014] x(t), y(t), z(t) represent the three components of the relative position between the target and the tracker in the rectangular coordinate system; v x (t),v y (t),v z (t) represents the three components of the relative velocity between the target and the tracker in the rectangular coordinate system; and a tx (t),a ty (t),a tz (t) represents the three components of target acceleration in the rectangular coordinate system; Represents the three components of target acceleration in the rectangular coordinate system;

[0015]

[0016] Each part in A and B represents a 3×3 matrix; λ t Given by the following formula

[0017]

[0018] λ is the inverse of the target acceleration time constant in the Jerk model;

[0019] After discretizing equation (1), we can get

[0020] x(t+1)=Φx(t)+u(t) (2)

[0021] in,

[0022]

[0023] Where Δt represents the measurement period;

[0024] The position and velocity information in three directions can be measured by the seeker; the measurement equation is:

[0025] η x =[xv x yv y z z ]

[0026] The measurement matrix is:

[0027]

[0028] According to the linear state prediction equation (2) and the linear measurement equation, the Kalman filter can be constructed.

[0029] Its prediction equation is

[0030]

[0031] Among them, Q is the model prediction error covariance matrix; the measurement correction equation of the filter is

[0032]

[0033] The acceleration of the target is estimated from this.

[0034] Furthermore, in step 2, the target-tracker sight line vector is expressed as

[0035]

[0036] in, and Represent the position vectors of the target and the tracker in the inertial coordinate system respectively; Differentiating the above formula with respect to time, we get

[0037]

[0038] in, and Respectively represent the time derivative of the sight vector in the inertial coordinate system and the sight coordinate system, represents the angular velocity of the line of sight coordinate system relative to the inertial coordinate system, and Represent the target and tracker velocity vectors respectively; the above formula is written in the projection form in the line of sight coordinate system:

[0039]

[0040] in,

[0041]

[0042] but

[0043]

[0044]

[0045] Put the formula

[0046]

[0047] Taking the derivative relative to time, we get

[0048]

[0049] Projected to the sight coordinate system, we get

[0050]

[0051] Right now

[0052]

[0053] Among them, a r 、a ε and a β is the component of the tracker acceleration on the three axes of the line of sight coordinate system, a tr 、a tε and a tβ It is the component of target acceleration on the three axes of the line of sight coordinate system.

[0054] Furthermore, in step 3, by substituting equation (10) into equation (13), we can obtain

[0055]

[0056] Among them, v r 、v ε and v β are the components of the tracker's relative velocity vector on the three axes of the line of sight coordinate system; select the state variable:

[0057] X=[rv r q ε v ε q β v β ] T (15)

[0058] In summary, we can get the 6th-order maneuver model:

[0059]

[0060] Select relative distance r, relative speed High and low angle q ε and azimuth q β As a measurement quantity; a tr 、a tε and a tβ It is obtained by coordinate transformation from the acceleration estimated by the Jerk model.

[0061] Furthermore, in step 4, from formula (5) we can know According to the measurement information of the seeker, the relative distance r and relative speed are measured. High and low angle q ε and azimuth q β , thus, the measurement matrix is:

[0062]

[0063] Based on the above, the model of the tracking filter can be obtained as follows:

[0064]

[0065] Where X∈R 6 and Y∈R 4 is the measured output of the filter, W and V are the system noise and measurement noise, and both are Gaussian white noise.

[0066] Furthermore, the steps of designing an extended Kalman filter according to the tracking filter model are specifically as follows:

[0067] The first step is to linearize the tracking filter model and obtain the Jacobian matrix φ k :

[0068]

[0069] The second step is to predict the state prior estimate x k+1|k :

[0070] x k+1|k =f(x k|k ) (19)

[0071] The third step is to predict the prior estimate of the covariance matrix P k+1|k :

[0072] P k+1|k =φ k P k|k φ k T +Q (20)

[0073] The fourth step is to calculate the state gain matrix K based on the obtained prior estimate k+1 :

[0074] K k+1 =P k+1|k H k+1 T (H k+1 P k+1|k H k+1 T +R) -1 (twenty one)

[0075] The fifth step is to obtain the state posterior estimate x according to the state gain matrix correction k+1|k+1 :

[0076] x k+1|k+1 =x k+1|k +K k+1 (z k+1 -Hx k+1|k ) (twenty two)

[0077] The sixth step is to obtain the covariance posterior estimate P according to the state gain matrix correction k+1|k+1 .

[0078] Furthermore, the covariance posterior estimate P k+1|k+1 for:

[0079] P k+1|k+1 =(IK k+1 H)P k+1|k (twenty three).

[0080] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the "two-step" filtering design method for estimating the angular rate of the missile-eye line of sight are implemented.

[0081] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of a "two-step" filter design method for estimating the angular rate of the missile-to-target line of sight.

[0082] Beneficial effects of the present invention:

[0083] The present invention proposes a "two-step" filter design method for estimating the line-of-sight angular rate between a missile and a target. The method comprehensively applies the missile-target information obtained by "ground tracking" and the target maneuvering acceleration information obtained by "passive tracking" to perform "two-step" filtering to achieve line-of-sight angular rate extraction. For large maneuvering targets, the method first estimates the target acceleration through the Jerk model, and then brings it into the established six-dimensional model to estimate the line-of-sight angular rate. Simulation results show that the method can achieve high-precision extraction of the line-of-sight angular rate, greatly improving the interception performance of the interception system against maneuvering targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0085] Figure 1 It is a schematic diagram of the acceleration estimation in the y-axis direction;

[0086] Figure 2 It is a schematic diagram of the acceleration estimation in the z-axis direction;

[0087] Figure 3 It is a schematic diagram of the estimation of angular velocity of high and low angle sight lines;

[0088] Figure 4 It is a schematic diagram of the azimuth line of sight angular rate estimation;

[0089] Figure 5 It is a schematic diagram of the angular rate estimation error of high and low angle sight lines;

[0090] Figure 6 It is a schematic diagram of the azimuth line of sight angular rate estimation error;

[0091] Figure 7 This is a schematic diagram of the off-target amount of 100 Monte Carlo simulation experiments. DETAILED DESCRIPTION

[0092] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] Combination Figure 1-Figure 7 The present invention proposes a "two-step" filter design method for estimating the missile-target line-of-sight angular velocity, the method comprising the following steps:

[0094] Step 1: The first step is filtering, combining the Jerk model and the extended Kalman filter to estimate the target acceleration through the measured target position and velocity information;

[0095] In step 1, the Jerk model is used to describe the changes of the target jerk components on the three axes of the inertial coordinate system of the launch point; the dynamic model of the interception system is

[0096]

[0097] in,

[0098]

[0099] x(t), y(t), z(t) represent the three components of the relative position between the target and the tracker in the rectangular coordinate system; v x (t),v y (t),v z (t) represents the three components of the relative velocity between the target and the tracker in the rectangular coordinate system; and a tx (t),a ty (t),a tz (t) represents the three components of target acceleration in the rectangular coordinate system; Represents the three components of target acceleration in the rectangular coordinate system;

[0100]

[0101] Each part in A and B represents a 3×3 matrix; λ t Given by the following formula

[0102]

[0103] λ is the inverse of the target acceleration time constant in the Jerk model;

[0104] After discretizing equation (1), we can get

[0105] x(t+1)=Φx(t)+u(t) (2)

[0106] in,

[0107]

[0108] Where Δt represents the measurement period;

[0109] The position and velocity information in three directions can be measured by the seeker; the measurement equation is:

[0110] η x =[xv x yv y z z ]

[0111] The measurement matrix is:

[0112]

[0113] According to the linear state prediction equation (2) and the linear measurement equation, the Kalman filter can be constructed.

[0114] Its prediction equation is

[0115]

[0116] Among them, Q is the model prediction error covariance matrix; the measurement correction equation of the filter is

[0117]

[0118] The acceleration of the target is estimated from this.

[0119] Step 2: Based on the positional relationship between the target and the tracker, derive the relative motion dynamics equation between the tracker and the target in the line of sight coordinate system;

[0120] In step 2, the target-tracker sight line vector is expressed as

[0121]

[0122] in, and Represent the position vectors of the target and the tracker in the inertial coordinate system respectively; Differentiating the above formula with respect to time, we get

[0123]

[0124] in, and Respectively represent the time derivative of the sight vector in the inertial coordinate system and the sight coordinate system, represents the angular velocity of the line of sight coordinate system relative to the inertial coordinate system, and Represent the target and tracker velocity vectors respectively; the above formula is written in the projection form in the line of sight coordinate system:

[0125]

[0126] in,

[0127]

[0128] but

[0129]

[0130] Put the formula

[0131]

[0132] Taking the derivative relative to time, we get

[0133]

[0134] Projected to the sight coordinate system, we get

[0135]

[0137] Right now

[0138]

[0139] Among them, a r 、a ε and a β is the component of the tracker acceleration on the three axes of the line of sight coordinate system, a tr 、a tε and a tβ It is the component of target acceleration on the three axes of the line of sight coordinate system.

[0140] Step 3: Combine the filtering results of the first step with the dynamic equations and select appropriate state variables to establish the 6th-order maneuver model of the target;

[0141] In step 3, substituting equation (10) into equation (13) yields

[0142]

[0143] Among them, v r 、v ε and v β are the components of the tracker's relative velocity vector on the three axes of the line of sight coordinate system; select the state variable:

[0144] X=[rv r q ε v ε q β v β ] T (15)

[0145] In summary, we can get the 6th-order maneuver model:

[0146]

[0147] Select relative distance r, relative speed High and low angle q ε and azimuth q β As a measurement quantity; a tr 、a tε and a tβ It is obtained by coordinate transformation from the acceleration estimated by the Jerk model.

[0148] Step 4: 'Second step' filtering, based on the 6th order maneuver model, establish a tracking filter model, and design an extended Kalman filter through the relative distance, relative speed and line of sight angle information measured by the seeker to estimate the line of sight angle rate;

[0149] In step 4, we can know from formula (5) According to the measurement information of the seeker, the relative distance r and relative speed are measured. High and low angle q ε and azimuth q β , thus, the measurement matrix is:

[0150]

[0151] Based on the above, the model of the tracking filter can be obtained as follows:

[0152]

[0153] Where X∈R 6and Y∈R 4 is the measured output of the filter, W and V are the system noise and measurement noise, and both are Gaussian white noise.

[0154] The steps for designing an extended Kalman filter based on the tracking filter model are as follows:

[0155] The first step is to linearize the tracking filter model and obtain the Jacobian matrix φ k :

[0156]

[0157] H itself is linear and does not need Taylor expansion.

[0158] The second step is to predict the state prior estimate x k+1|k :

[0159] x k+1|k =f(x k|k ) (19)

[0160] The third step is to predict the prior estimate of the covariance matrix P k+1|k :

[0161] P k+1|k =φ k P k|k φ k T +Q (20)

[0162] The fourth step is to calculate the state gain matrix K based on the obtained prior estimate k+1 :

[0163] K k+1 =P k+1|k H k+1 T (H k+1 P k+1|k H k+1 T +R) -1 (twenty one)

[0164] The fifth step is to obtain the state posterior estimate x according to the state gain matrix correction k+1|k+1 :

[0165] x k+1|k+1 =x k+1|k +K k+1 (z k+1 -Hx k+1|k ) (twenty two)

[0166] The sixth step is to obtain the covariance posterior estimate P according to the state gain matrix correction k+1|k+1 .

[0167] The posterior estimate of the covariance P k+1|k+1 for:

[0168] P k+1|k+1 =(IK k+1 H)P k+1|k (twenty three).

[0169] Step 5: Combine the “two-step” filtering and use the estimated line-of-sight angular rate to perform guidance simulation.

[0170] Simulation part

[0171] In order to verify the effectiveness of the sixth-order maneuvering model proposed in this invention for line-of-sight angular rate extraction, for the reentry strong maneuvering target, the maneuvering frequency is 0.5 Hz and the maximum maneuvering acceleration is a max = ±20g of the screw maneuver was simulated.

[0172] Assume that the initial missile-target distance is R = 13900m and the initial speed of the target is v xt =-1.28×10 3 m / s,v yt =-1.49×10 3 m / s,v zt =0m / s, the initial speed of the tracker is v x =1.026×10 3 m / s,v y =1.117×10 3 m / s,v z = 0m / s. The measurement error of the missile-target distance R is 3σ = 30m, the measurement error of the relative speed V is 3σ = 2m / s, and the sight angle q ε and q β The measurement error is σ = 0.01°, from which the measurement noise variance matrix R can be calculated n , the system noise variance matrix is

[0173] Q n =diag[Q1 Q2 ... Q6]

[0174] in,

[0175] Q1=10^(-6), Q2=10^(-6), Q3=10^(-14), Q4=10^(-2), Q5=10^(-14), Q6=10^(-2),

[0176] Through simulation, it can be concluded that when the maximum overload is 20g and the maneuvering frequency is 0.5Hz, the "two-step" filtering design method proposed in the present invention is adopted, and the accuracy of acceleration estimation is also higher, and the estimated line of sight angular rate error is less than 0.1° / s in both the elevation and azimuth directions; through 100 Monte Carlo simulation experiments, the miss distance is within 0.06m, and the average miss distance is 0.03m, which can effectively intercept strong maneuvering targets, has important engineering application value, and lays the foundation for the design of guidance law.

[0177] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the "two-step" filtering design method for estimating the angular rate of the missile-eye line of sight are implemented.

[0178] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of a "two-step" filter design method for estimating the angular rate of the missile-to-target line of sight.

[0179] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0180] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0181] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0182] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined and performed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0183] The above is a detailed introduction to the "two-step" filtering design method for estimating the angular rate of the line of sight between a missile and a target proposed in the present invention. In this article, specific examples are used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A two-step filter design method for estimating the angular velocity of the missile-to-target line of sight, characterized in that: The method comprises the following steps: Step 1: The first step is filtering, combining the Jerk model and the extended Kalman filter to estimate the target acceleration through the measured target position and velocity information; Step 2: Based on the positional relationship between the target and the tracker, derive the relative motion dynamics equation between the tracker and the target in the line of sight coordinate system; Step 3: Combine the filtering results of the first step with the dynamic equations and select appropriate state variables to establish the 6th-order maneuver model of the target; Step 4: 'Second step' filtering, based on the 6th order maneuver model, establish a tracking filter model, and design an extended Kalman filter through the relative distance, relative speed and line of sight angle information measured by the seeker to estimate the line of sight angle rate; Step 5: Combine the "two-step" filtering and use the estimated line-of-sight angular rate to perform guidance simulation.

2. The method according to claim 1, characterized in that: In step 1, the Jerk model is used to describe the changes of the target jerk components on the three axes of the inertial coordinate system of the launch point; the dynamic model of the interception system is in, x(t), y(t), z(t) represent the three components of the relative position between the target and the tracker in the rectangular coordinate system; v x (t),v y (t),v z (t) represents the three components of the relative velocity between the target and the tracker in the rectangular coordinate system; and a tx (t),a ty (t),a tz (t) represents the three components of target acceleration in the rectangular coordinate system; Represents the three components of target acceleration in the rectangular coordinate system; Each part in A and B represents a 3×3 matrix; λ t Given by the following formula λ is the inverse of the target acceleration time constant in the Jerk model; After discretizing equation (1), we can get x(t+1)=Φx(t)+u(t) (2) in, Where Δt represents the measurement period; The position and velocity information in three directions can be measured by the seeker; the measurement equation is: η x =[x v x y v y z v z ] The measurement matrix is: According to the linear state prediction equation (2) and the linear measurement equation, the Kalman filter can be constructed. Its prediction equation is Among them, Q is the model prediction error covariance matrix; the measurement correction equation of the filter is The acceleration of the target is estimated from this.

3. The method according to claim 2, characterized in that In step 2, the target-tracker sight line vector is expressed as in, and Represent the position vectors of the target and the tracker in the inertial coordinate system respectively; Differentiating the above formula with respect to time, we get in, and Respectively represent the time derivative of the sight vector in the inertial coordinate system and the sight coordinate system, represents the angular velocity of the line of sight coordinate system relative to the inertial coordinate system, and Represent the target and tracker velocity vectors respectively; the above formula is written in the projection form in the line of sight coordinate system: in, but Put the formula Taking the derivative relative to time, we get Projected to the sight coordinate system, we get Right now Among them, a r 、a ε and a β is the component of the tracker acceleration on the three axes of the line of sight coordinate system, a tr 、a tε and a tβ It is the component of target acceleration on the three axes of the line of sight coordinate system.

4. The method according to claim 3, characterized in that In step 3, substituting equation (10) into equation (13) yields Among them, v r 、v ε and v β are the components of the tracker's relative velocity vector on the three axes of the line of sight coordinate system; select the state variable: X=[r v r q ε v ε q β v β ] T (15) In summary, we can get the 6th-order maneuver model: Select relative distance r, relative speed High and low angle q ε and azimuth q β As a measurement quantity; a tr 、a tε and a tβ It is obtained by coordinate transformation from the acceleration estimated by the Jerk model.

5. The method according to claim 4, characterized in that In step 4, we can know from formula (5) According to the measurement information of the seeker, the relative distance r and relative speed are measured. High and low angle q ε and azimuth q β , thus, the measurement matrix is: Based on the above, the model of the tracking filter can be obtained as follows: Where X∈R 6 and Y∈R 4 is the measured output of the filter, W and V are the system noise and measurement noise, and both are Gaussian white noise.

6. The method according to claim 5, characterized in that The specific steps of designing an extended Kalman filter based on the tracking filter model are: The first step is to linearize the tracking filter model and obtain the Jacobian matrix φ k : The second step is to predict the state prior estimate x k+1|k : x k+1|k =f(x k|k ) (19) The third step is to predict the prior estimate of the covariance matrix P k+1|k : P k+1|k =φ k P k|k φ k T +Q (20) The fourth step is to calculate the state gain matrix K based on the obtained prior estimate k+1 : K k+1 =P k+1|k H k+1 T (H k+1 P k+1|k H k+1 T +R) -1 (21) The fifth step is to obtain the state posterior estimate x according to the state gain matrix correction k+1|k+1 : x k+1|k+1 =x k+1|k +K k+1 (z k+1 -Hx k+1|k ) (22) The sixth step is to obtain the covariance posterior estimate P according to the state gain matrix correction k+1|k+1 .

7. The method according to claim 6, characterized in that The posterior estimate of the covariance P k+1|k+1 for: P k+1|k+1 =(I-K k+1 H)P k+1|k (23)。 8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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