阅读说明：本技术 基于高阶滑膜跟踪微分器的加速度估计方法、系统、设备和介质 (Acceleration estimation method, system, device and medium based on high-order synovial membrane tracking differentiator ) 是由 胡红爽 吴永东 魏凯敏 罗伟其 张继连 于 2021-08-30 设计创作，主要内容包括：本发明公开了一种基于高阶滑膜跟踪微分器的加速度估计方法、系统、设备和介质,所述方法包括：利用增广最小二乘法对速度进行参数辨识,得到速度估计的最大测量噪声幅值；根据所述速度估计的最大测量噪声幅值,使用一阶滑膜跟踪微分器精确估计目标的速度,得到目标速度的估计值；根据所述目标速度的估计值,得到加速度估计的最大测量噪声幅值；根据所述加速度估计的最大测量噪声幅值,使用高阶滑膜跟踪微分器精确估计目标的加速度,得到目标加速度的估计值。本发明使用增广最小二乘法进行参数辨识,分别获得速度和加速度估计的最大测量噪声幅值；同时使用高阶滑膜跟踪微分器,保证了加速度估计值能够达到要求的精度。(The invention discloses an acceleration estimation method, a system, equipment and a medium based on a high-order synovial membrane tracking differentiator, wherein the method comprises the following steps: carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation; accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude of the speed estimation to obtain an estimation value of the target speed; obtaining the maximum measurement noise amplitude of the acceleration estimation according to the estimation value of the target speed; and accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration. The method adopts an augmented least square method to identify parameters and respectively obtains the maximum measurement noise amplitude of speed and acceleration estimation; and meanwhile, a high-order slip film tracking differentiator is used, so that the acceleration estimated value can reach the required precision.)

1. A method for acceleration estimation based on a higher order synovial membrane tracking differentiator, the method comprising:

carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation;

accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude of the speed estimation to obtain an estimation value of the target speed;

according to the estimated value of the target speed, performing parameter identification on the acceleration by using an augmented least square method to obtain the maximum measurement noise amplitude of the acceleration estimation;

and accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

2. The acceleration estimation method according to claim 1, wherein the parameter identification of the velocity using the augmented least squares method specifically includes:

let the motion trail of the target object be x (t), wherein the real motion trail is x₀(t), white noise is e (t), and its continuous time domain is represented as:

x(t)＝x₀(t)+e(t) (1)

wherein t is the time in the continuous time domain;

converting equation (1) to a discrete time domain:

x(k)＝x₀(k)+e(k) (2)

k is a time value on a discrete domain, and k is an integer greater than or equal to 0; e (k) gaussian distribution obeying N (0, 1);

the sampling time of the target tracking system is T_S，kT_sTrue velocity at time v₀(k) Then, the velocity is obtained by the definition:

x₀(k)-x₀(k-1)＝v₀(k)T_S (3)

the measured speed values thus satisfy the following relationship:

x(k)-x(k-1)＝v₀(k)T_s+e(k)-e(k-1) (4)

according to the relationship in equation (1), let:

y(k)＝x(k)-x(k-1)+e(k-1) (5)

from the equations (4) and (5), the pair velocity v is obtained₀(k) The relationship for parameter identification is:

y(k)＝v₀(k)T_s+e(k) (6)。

3. the acceleration estimation method according to claim 2, wherein the obtaining of the maximum measurement noise amplitude of the velocity estimation by performing parameter identification on the velocity by using the augmented least squares method specifically includes:

obtaining the velocity v of the augmented least square method with forgetting factor according to the formula (6)₀(k) Recursive estimation algorithm, as follows:

wherein the content of the first and second substances,is an estimate of the velocity at the current time,is the speed estimation value of the previous moment, K (k) is a gain matrix, P (k) is an estimation variance matrix of the current moment, mu is a forgetting factor, and the value of y (k) is calculated according to the formula (5);

calculated from recursionIs found as the maximum measured noise amplitude L of the velocity estimate₁Namely:

where m is calculated recursivelyTotal number of values.

4. The acceleration estimation method according to claim 1, wherein the accurately estimating the velocity of the target using a first-order synovial tracking differentiator according to the maximum measured noise amplitude of the velocity estimate to obtain the estimated value of the velocity of the target comprises:

the first-order sliding mode tracking differentiator is based on a second-order sliding mode control algorithm to control u (t), so that the system keeps sigma x-f (t) 0 and

the first order synovium tracks the specific form of the differentiator, as follows:

wherein f (t) is the input of a first-order synovial membrane tracking differentiator, namely the measured displacement of the target, sgn (·) is a sign function, and the parameters α and λ satisfy the following relationship:

wherein，L₁A maximum measured noise amplitude value estimated for the velocity;

an accurate estimated value v (t) of the target speed is calculated according to equations (9) and (10).

5. The acceleration estimation method according to claim 2, wherein the obtaining of the maximum measurement noise amplitude of the acceleration estimation by performing parameter identification on the acceleration by using an augmented least squares method according to the estimated value of the target speed specifically includes:

discretizing the estimated value v (t) of the target speed to obtain:

y(k)＝v(k)-v(k-1)+e(k-1) (11)

wherein v (k) is an estimated value of the target speed at the time k, and e (k-1) is white noise at the time k-1;

according to the formula (6), the pair acceleration a is obtained₀(k) The relationship for parameter identification is:

y(k)＝a₀(k)T_s+e(k) (12)

according to the formula (12), the velocity a of the augmented least square method with the forgetting factor is obtained₀(k) The recursive estimation algorithm is as follows:

wherein the content of the first and second substances,is an estimate of the acceleration at the present time,is the acceleration estimated value at the previous moment, K (k) is a gain matrix, P (k) is an estimated variance matrix at the current moment, mu is a forgetting factor, and the value of y (k) is calculated according to a formula (11);

calculated from recursionIs found as the maximum measured noise amplitude L of the acceleration estimation₂Namely:

where m is calculated recursivelyTotal number of values.

6. The acceleration estimation method according to claim 1, wherein the accurately estimating the acceleration of the target using a higher-order synovial tracking differentiator according to the maximum measurement noise amplitude of the acceleration estimation to obtain an estimated value of the target acceleration comprises:

the high-order synovium tracking differentiator is based on a high-order synovium control algorithm, so that the system can reach the target value after a period of timeThe state of (1);

the specific form of the high-order synovial membrane tracking differentiator is as follows:

wherein, σ is the input of the target tracking system, r is the order of the target tracking system, sign (·) is a switching function;

from equation (15), the equation for obtaining an estimate of acceleration is as follows:

wherein σ is the input of the target tracking system, i.e. the position of the tracking target at the current timeMarking; l is₂A maximum measurement noise amplitude estimated for the acceleration; v. of₀Is the speed estimated value of the current moment; z is a radical of₀、z₁、z₂Are respectively the predicted values of displacement, velocity and acceleration in the differentiator,are each z₀、z₁、z₂The first derivative of (a); a is the output value of the system and is used as the estimated value of the acceleration;

the premise that the high-order synovial membrane tracking differentiator accurately estimates the acceleration isIs the absolute value of the second derivative of σ.

7. The acceleration estimation method according to claim 1, characterized in that the method further comprises before:

in a video-based target tracking system, a motion trajectory of a target object is acquired.

8. An acceleration estimation system based on a second order synovial tracking differentiator, the system comprising:

the first parameter identification module is used for carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation;

the first estimation module is used for accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude value estimated by the speed to obtain an estimated value of the speed of the target;

the second parameter identification module is used for carrying out parameter identification on the acceleration by using an augmented least square method according to the estimated value of the target speed to obtain the maximum measurement noise amplitude value of the acceleration estimation;

and the second estimation module is used for accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

9. A computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the acceleration estimation method of any of claims 1-7.

10. A storage medium storing a program, wherein the program, when executed by a processor, implements the acceleration estimation method according to any one of claims 1 to 7.

Technical Field

The invention belongs to the technical field of automatic control, and particularly relates to an acceleration estimation method and system based on a high-order synovial membrane tracking differentiator, computer equipment and a storage medium.

Background

With the development of computer vision, the target tracking method can accurately obtain the motion track of a target object in a picture set or a video. According to the motion trail of the target object, more motion characteristics can be obtained, and therefore the motion trend of the target object at the next moment can be predicted. Among these, the acquisition of the acceleration is particularly important. However, the process of estimating the acceleration from the position information of the object is a third-order differential process, which is very sensitive to noise, and the accuracy of the differential rapidly decreases after a plurality of successive iterations, and many studies have been made by scholars and engineers in recent years.

Least squares and kalman filtering are classical state estimation methods. The acceleration estimation method based on the least square method needs to perform a two-layer differential calculation process, and each time one layer of differential calculation is performed, noise is amplified once, so that the obtained result is seriously distorted. In the acceleration estimation method based on kalman filtering, the model to be estimated is a linear model, and the noise of the observer is known. In practical practice, the noise of the observer is often difficult to obtain, and therefore acceleration estimation using this method is also difficult to achieve.

To address the above problems, atasi and Khalil et al propose a high gain differentiator that provides an accurate derivative as the gain approaches infinity, but also results in high sensitivity to small amplitude high frequency noise. Meanwhile, when the gain tends to infinity, the maximum output value of the system in the transient state will also become infinity. Therefore, the value converged by the differentiator is a value in the vicinity of the acceleration, and an accurate acceleration value cannot be obtained. Golembo et al propose a synovial differentiator, but this differentiator is a filter of the output and does not provide an accurate differentiation with a finite time convergence, and therefore, an accurate acceleration value cannot be obtained.

Disclosure of Invention

To solve the above-mentioned deficiencies of the prior art, the present invention provides a method, system, computer device and storage medium for estimating acceleration based on a high-order synovial tracking differentiator, which accomplishes accurate estimation of acceleration by designing a high-order precise differentiator with good robustness to sensor noise.

The first purpose of the invention is to provide an acceleration estimation method based on a high-order synovial membrane tracking differentiator.

It is a second object of the present invention to provide an acceleration estimation system based on a higher order synovial membrane tracking differentiator.

It is a third object of the invention to provide a computer apparatus.

It is a fourth object of the present invention to provide a storage medium.

The first purpose of the invention can be achieved by adopting the following technical scheme:

a method of acceleration estimation based on a higher order synovial tracking differentiator, the method comprising:

carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation;

accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude of the speed estimation to obtain an estimation value of the target speed;

according to the estimated value of the target speed, performing parameter identification on the acceleration by using an augmented least square method to obtain the maximum measurement noise amplitude of the acceleration estimation;

and accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

Further, the parameter identification of the speed by using the augmented least square method specifically comprises:

let the motion trail of the target object be x (t), wherein the real motion trail is x₀(t), white noise is e (t), and its continuous time domain is represented as:

x(t)＝x₀(t)+e(t) (1)

wherein t is the time in the continuous time domain;

converting equation (1) to a discrete time domain:

x(k)＝x₀(k)+e(k) (2)

k is a time value on a discrete domain, and k is an integer greater than or equal to 0; e (k) gaussian distribution obeying N (0, 1);

the sampling time of the target tracking system is T_S，kT_sTrue velocity at time v₀(k) Then, the velocity is obtained by the definition:

x₀(k)-x₀(k-1)＝v₀(k)T_S (3)

the measured speed values thus satisfy the following relationship:

x(k)-x(k-1)＝v₀(k)T_s+e(k)-e(k-1) (4)

according to the relationship in equation (1), let:

y(k)＝x(k)-x(k-1)+e(k-1) (5)

from the equations (4) and (5), the pair velocity v is obtained₀(k) The relationship for parameter identification is:

y(k)＝v₀(k)T_s+e(k) (6)。

further, the parameter identification is performed on the speed by using the augmented least square method to obtain the maximum measurement noise amplitude of the speed estimation, and the method specifically includes:

obtaining the velocity v of the augmented least square method with forgetting factor according to the formula (6)₀(k) Recursive estimation algorithm, as follows:

wherein the content of the first and second substances,is an estimate of the velocity at the current time,is the speed estimation value of the previous moment, K (k) is a gain matrix, P (k) is an estimation variance matrix of the current moment, mu is a forgetting factor, and the value of y (k) is calculated according to the formula (5);

calculated from recursionIs found as the maximum measured noise amplitude L of the velocity estimate₁Namely:

where m is calculated recursivelyTotal number of values.

Further, the step of accurately estimating the speed of the target by using a first-order slip film tracking differentiator according to the maximum measurement noise amplitude value estimated by the speed to obtain an estimated value of the speed of the target specifically includes:

the first-order sliding mode tracking differentiator is based on a second-order sliding mode control algorithm to control u (t), so that the system keeps sigma x-f (t) 0 and

the first order synovium tracks the specific form of the differentiator, as follows:

where f (t) is the input of the first-order synovial tracking differentiator, i.e. the measured displacement of the target, sgn (·) is a sign function, and the parameter α, λ satisfies the following relationship:

wherein L is₁A maximum measured noise amplitude value estimated for the velocity;

an accurate estimated value v (t) of the target speed is calculated according to equations (9) and (10).

Further, the obtaining of the maximum measurement noise amplitude of the acceleration estimation by performing parameter identification on the acceleration by using an augmented least square method according to the estimated value of the target speed specifically includes:

discretizing the estimated value v (t) of the target speed to obtain:

y(k)＝v(k)-v(k-1)+e(k-1) (11)

wherein v (k) is an estimated value of the target speed at the time k, and e (k-1) is white noise at the time k-1;

according to the formula (6), the pair acceleration a is obtained₀(k) The relationship for parameter identification is:

y(k)＝a₀(k)T_s+e(k) (12)

according to the formula (12), the velocity a of the augmented least square method with the forgetting factor is obtained₀(k) The recursive estimation algorithm is as follows:

wherein the content of the first and second substances,is an estimate of the acceleration at the present time,is the acceleration estimated value at the previous moment, K (k) is a gain matrix, P (k) is an estimated variance matrix at the current moment, mu is a forgetting factor, and the value of y (k) is calculated according to a formula (11);

calculated from recursionIs found in the value ofFinding the maximum value as the maximum measurement noise amplitude L of the acceleration estimation₂Namely:

where m is calculated recursivelyTotal number of values.

Further, the accurately estimating the acceleration of the target by using a high-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude estimated from the acceleration to obtain an estimated value of the target acceleration specifically includes:

the high-order synovium tracking differentiator is based on a high-order synovium control algorithm, so that the system can reach the target value after a period of timeThe state of (1);

the specific form of the high-order synovial membrane tracking differentiator is as follows:

wherein, σ is the input of the target tracking system, r is the order of the target tracking system, sign (·) is a switching function;

from equation (15), the equation for obtaining an estimate of acceleration is as follows:

wherein, σ is the input of the target tracking system, namely the position coordinate of the tracking target at the current moment; l is₂A maximum measurement noise amplitude estimated for the acceleration; v. of₀Is the speed estimated value of the current moment; z is a radical of₀、z₁、z₂Prediction of displacement, velocity and acceleration in differentiatorsThe value of the one or more of the one,are each z₀、z₁、z₂The first derivative of (a); a is the output value of the system and is used as the estimated value of the acceleration;

the premise that the high-order synovial membrane tracking differentiator accurately estimates the acceleration isIs the absolute value of the second derivative of σ.

Further, the method also comprises the following steps:

in a video-based target tracking system, a motion trajectory of a target object is acquired.

The second purpose of the invention can be achieved by adopting the following technical scheme:

a high order synovial tracking differentiator based acceleration estimation system, the system comprising:

the first parameter identification module is used for carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation;

the first estimation module is used for accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude value estimated by the speed to obtain an estimated value of the speed of the target;

the second parameter identification module is used for carrying out parameter identification on the acceleration by using an augmented least square method according to the estimated value of the target speed to obtain the maximum measurement noise amplitude value of the acceleration estimation;

and the second estimation module is used for accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

The third purpose of the invention can be achieved by adopting the following technical scheme:

a computer device comprising a processor and a memory for storing a program executable by the processor, the processor implementing the acceleration estimation method when executing the program stored by the memory.

The fourth purpose of the invention can be achieved by adopting the following technical scheme:

a storage medium stores a program that, when executed by a processor, implements the acceleration estimation method described above.

Compared with the prior art, the invention has the following beneficial effects:

1. the invention provides a method for identifying parameters by using an augmented least square method, and maximum measurement noise amplitudes of speed and acceleration estimation are respectively obtained.

2. According to the invention, the accuracy of the high-order synovial membrane tracking differentiator and the amplitude of the maximum measurement noise form a positive correlation relationship, so that the acceleration estimation value can reach the required accuracy by using two synovial membrane tracking differentiators connected in series.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.

Fig. 1 is a flowchart of a method for estimating acceleration based on a higher-order synovial membrane tracking differentiator according to embodiment 1 of the present invention.

Fig. 2 is a block diagram of an acceleration estimation system based on a higher-order synovial membrane tracking differentiator according to embodiment 2 of the present invention.

Fig. 3 is a block diagram of a computer device according to embodiment 3 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention. It should be understood that the description of the specific embodiments is intended to be illustrative only and is not intended to be limiting.

Example 1:

arie Levant proposes a high-order sliding mode tracking differentiator which can achieve the accurate robustness of any order, and the differentiator is based on theoretical proof that when a signal containing noise has an n-order derivative, and a Lipschitz constant of the n-order derivative is limited by a given constant L, the optimal differentiation accuracy of the ith (i < ═ n) order derivative and the amplitude of the maximum measurement noise form a positive correlation relationship. The differentiator based on the theory can reach an approximate true value with any order and any precision when obtaining the maximum amplitude of the maximum measurement noise, namely the maximum value of the true acceleration, but the maximum value of the true acceleration is very difficult to obtain in the situation of lacking the actual application of the acceleration sensor. Therefore, the invention adopts a compromise method, adopts least square identification to obtain the approximate value of the maximum measurement noise amplitude, and achieves high-precision estimation of the differentiator through parameter fine adjustment.

As shown in fig. 1, this embodiment provides an acceleration estimation method based on a high-order synovial membrane tracking differentiator, where in a video-based target tracking system, if a motion trajectory of a target object obtained by target tracking is known as x (t), an acceleration estimation process of a motion of the object includes the following steps:

s101, carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of the speed estimation.

If the measured motion track of the target object is known as x (t), wherein the real motion track is x₀(t), white noise is e (t), and its continuous time domain is represented as:

x(t)＝x₀(t)+e(t) (1)

wherein t is the time in the continuous time domain;

converting equation (1) to a discrete time domain:

x(k)＝x₀(k)+e(k) (2)

k is a time value on a discrete domain, and k is an integer greater than or equal to 0; e (k) gaussian distribution obeying N (0, 1);

the sampling time of the target tracking system is T_S,kT_sTrue velocity at time v₀(k) Then, the definition of speed can be given as:

x₀(k)-x₀(k-1)＝v₀(k)T_S (3)

wherein, Ts is 0.1s in this embodiment.

The measured speed values thus satisfy the following relationship:

x(k)-x(k-1)＝v₀(k)T_s+e(k)-e(k-1) (4)

according to the relationship in equation (1), let:

y(k)＝x(k)-x(k-1)+e(k-1) (5)

from the equations (4) and (5), the pair velocity v is obtained₀(k) The relationship for parameter identification is:

y(k)＝v₀(k)T_s+e(k) (6)

obtaining the velocity v of the augmented least square method with forgetting factor according to the formula (6)₀(k) The recursive estimation algorithm is as follows:

wherein the content of the first and second substances,is the speed estimated value of the current moment;is the velocity estimation value of the last moment; k (k) is a gain matrix; the value of y (k) is calculated by equation (5), p (k) is the estimated variance matrix at the current time, μ is the forgetting factor.

The initial default value K (0) of K (K) is 0, and the larger the initial value of p (K), the larger the value of μ ∈ (0, 1).

In this embodiment, the initial value P (0) of P (k) is 1000000, and the forgetting factor μ is 0.33.

Calculated from recursionIs found as the maximum measured noise amplitude L of the velocity estimate₁Namely:

where m is calculated recursivelyTotal number of values.

And S102, accurately estimating the speed of the target by using a first-order synovial tracking differentiator according to the maximum measurement noise amplitude value estimated by the speed to obtain an estimated value of the target speed.

The first-order slip form tracking differentiator is based on a second-order slip form control algorithm to control u (t), so that the system can keep sigma x-f (t) 0 andthe specific form of the differentiator is as follows:

where f (t) is the input to the differentiator, i.e. the measured displacement of the target, sgn (-) is a sign function, and the parameter α, λ satisfies the following relationship:

wherein L is₁Maximum measured noise amplitude for the velocity estimate obtained in step S101；

From equations (9) and (10), an accurate estimated value v (t) of the target velocity can be calculated.

S103, according to the estimated value of the target speed, performing parameter identification on the acceleration by using an augmented least square method to obtain the maximum measurement noise amplitude of the acceleration estimation.

Discretizing the estimated value v (t) of the target speed obtained in step S102 to obtain:

y(k)＝v(k)-v(k-1)+e(k-1) (11)

where v (k) represents an estimated value of the target velocity at time k, and e (k-1) represents white noise at time k-1.

According to the formula (6), the pair acceleration a is obtained₀(k) The relationship for parameter identification is:

y(k)＝a₀(k)T_s+e(k) (12)

according to the formula (12), the velocity a of the augmented least square method with the forgetting factor is obtained₀(k) The recursive estimation algorithm is as follows:

wherein the content of the first and second substances,the acceleration estimated value at the current moment is the result of parameter identification;the value of y (k) is calculated from equation (11) as the acceleration estimation value at the previous time.

Calculated from recursionIs found as the maximum measured noise amplitude L of the acceleration estimation₂Namely:

where m is calculated recursivelyTotal number of values.

And S104, accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

The high-order synovium tracking differentiator is based on a high-order synovium control algorithm, so that the system can reach the target value after a period of timeThe state of (1).

The specific form of the high-order synovial membrane tracking differentiator is as follows:

wherein, σ is the input of the target tracking system, r is the order of the target tracking system, sign (·) is a switching function;

from equation (15), the equation for the estimate of acceleration can be derived as follows:

wherein, σ is the input of the target tracking system, namely the position coordinate of the tracking target at the current moment; l is₂A maximum measurement noise amplitude estimated for the acceleration; v. of₀Is the speed estimated value of the current moment; a is the output value of the system and is used as the estimated value of the acceleration; z is a radical of₀、z₁、z₂Are respectively the predicted values of displacement, velocity and acceleration in the differentiator,are each z₀、z₁、z₂ToThe second derivative.

V above₀Is an intermediate quantity in the differentiator, and is a velocity estimation value calculated by the differentiator.

The premise that the differentiator can accurately estimate the acceleration isIs the absolute value of the second derivative of σ.

Since step S103 obtains accurate L₂And thus an accurate acceleration estimation value a can be obtained.

Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct associated hardware, and the corresponding program may be stored in a computer-readable storage medium.

It should be noted that although the method operations of the above-described embodiments are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

Example 2:

as shown in fig. 2, the present embodiment provides an acceleration estimation system based on a higher-order synovial tracking differentiator, which includes a first parameter identification module 201, a first estimation module 202, a second parameter identification module 203, and a second estimation module 204, wherein:

the first parameter identification module 201 is configured to perform parameter identification on the speed by using an augmented least square method to obtain a maximum measurement noise amplitude of speed estimation;

a first estimation module 202, configured to accurately estimate a speed of the target using a first-order synovial tracking differentiator according to the maximum measurement noise amplitude of the speed estimation, so as to obtain an estimated value of the target speed;

the second parameter identification module 203 is used for performing parameter identification on the acceleration by using an augmented least square method according to the estimated value of the target speed to obtain the maximum measurement noise amplitude value of the acceleration estimation;

and the second estimation module 204 is configured to accurately estimate the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude of the acceleration estimation, so as to obtain an estimated value of the target acceleration.

The specific implementation of each module in this embodiment may refer to embodiment 1, which is not described herein any more; it should be noted that the system provided in this embodiment is only illustrated by the division of the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.

Example 3:

the present embodiment provides a computer device, which may be a computer, as shown in fig. 3, and includes a processor 302, a memory, an input system 303, a display 304 and a network interface 305 connected by a system bus 301, where the processor is used to provide computing and control capabilities, the memory includes a nonvolatile storage medium 306 and an internal memory 307, the nonvolatile storage medium 306 stores an operating system, a computer program and a database, the internal memory 307 provides an environment for the operating system and the computer program in the nonvolatile storage medium to run, and when the processor 302 executes the computer program stored in the memory, the acceleration estimation method of the above embodiment 1 is implemented as follows:

carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation;

accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude of the speed estimation to obtain an estimation value of the target speed;

according to the estimated value of the target speed, performing parameter identification on the acceleration by using an augmented least square method to obtain the maximum measurement noise amplitude of the acceleration estimation;

and accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

Example 4:

the present embodiment provides a storage medium, which is a computer-readable storage medium, and stores a computer program, and when the computer program is executed by a processor, the acceleration estimation method of the above embodiment 1 is implemented as follows:

carrying out parameter identification on the speed by using an augmented least square method to obtain the maximum measurement noise amplitude of speed estimation;

accurately estimating the speed of the target by using a first-order synovial membrane tracking differentiator according to the maximum measurement noise amplitude of the speed estimation to obtain an estimation value of the target speed;

according to the estimated value of the target speed, performing parameter identification on the acceleration by using an augmented least square method to obtain the maximum measurement noise amplitude of the acceleration estimation;

and accurately estimating the acceleration of the target by using a high-order synovial tracking differentiator according to the maximum measurement noise amplitude estimated by the acceleration to obtain an estimated value of the target acceleration.

It should be noted that the computer readable storage medium of the present embodiment may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the scope of the present invention.