A landing result evaluation method guided by redundant guidance information fusion

Abstract

The present application belongs to the field of sensor information fusion technology and is a method for evaluating landing results guided by redundancy-guided information fusion. The method first constructs a landing coordinate system and then calculates the expected Ex, Ey, and entropy En of the landing coordinate set through digital environment simulation or experiment. x ,En y 、Super entropy He x , He y , generate samples, and then generate cloud droplets according to the cloud model algorithm. Each cloud droplet is regarded as a landing point, so as to roughly restore more simulation results and expand the sample data volume; at the same time, according to the reverse cloud generator model, the super entropy of the landing point can be calculated, and the uncertainty of the membership can be effectively evaluated on the basis of realizing the landing point grasp and risk assessment functions, effectively solving the problem of too few samples and not considering the uncertainty of the membership. At the same time, the wild value detection module can be used to eliminate abnormal results caused by the system environment or human operation, and effectively realize the landing result evaluation.

Description

Technical Field

[0001] The present application belongs to the field of sensor information fusion technology, and in particular, relates to a landing result evaluation method guided by redundancy guidance information fusion. Background Art

[0002] Landing result evaluation is the most effective and direct evaluation method for redundant guidance information fusion guidance. Considering that a single digital simulation system closed-loop test takes a long time, the number of samples that can obtain valid data conclusions in flight test verification is even more scarce. Since each digital simulation or flight test verification process is independent, the random phenomenon of the landing result can be regarded as obeying the normal distribution. In the modeling of solving these phenomenon problems, the uncertainty and uncertainty processing methods of the phenomenon can adopt or draw on the working principle of the normal cloud generator.

[0003] At present, the commonly used landing result evaluation technology, each algorithm has its advantages and disadvantages: a. It cannot solve the problem of too small sample size. Due to the long time of digital simulation and the difficulty in obtaining flight test verification results, too small a sample size is a problem that has not been effectively solved in many evaluation technologies. The cloud method obtains the digital characteristics of the landing point diagram on the deck through the reverse cloud generator calculation simulation, which reflects the performance of each landing guidance method, and then uses the forward cloud generator to simulate and generate different numbers of cloud droplets, which can roughly restore more simulation results to evaluate and analyze the performance of the guidance system; b. Traditional means only consider the digital characteristics of expected value and entropy, which can evaluate the membership of landing assurance and landing risk, but the super entropy, that is, the discrete degree of entropy, has not been effectively evaluated, and the uncertainty evaluation of membership has not been considered.

[0004] Therefore, how to evaluate the landing results more effectively is a problem that needs to be solved. Summary of the invention

[0005] The purpose of this application is to provide a landing result evaluation method guided by redundancy-guided information fusion, so as to solve the problems of too small sample size and failure to consider the uncertainty of membership in the prior art.

[0006] The technical solution of the present application is: a method for evaluating landing results guided by redundant guidance information fusion, comprising: taking the ideal landing point as the origin of the coordinate system, taking the geoid as the coordinate reference plane, and constructing a landing coordinate system with the X-axis pointing to the bow along the bow-to-stern line of the landing site as positive, and the Y-axis pointing to the port side as positive;

[0007] Perform multiple digital environment simulations or tests and record the drop coordinates of each drop (x i ,y i )(i=1,2,…,n);

[0008] Construct a cloud generator. Using the principle of the reverse cloud generator, calculate the expectations Ex, Ey and entropy En of the landing coordinate set x , En y and hyper-entropy He x , He y ;

[0009] Using the principle of the forward cloud generator, generate a normal random number En' with En x , En y as the expectation and He x , He y as the standard deviation according to the three digital characteristics of the cloud generator x , En' y ;

[0010] Take Ex, Ey as the expectations. At the same time, take the absolute value of En x ', En' y as the standard deviation to generate a normal random coordinate point (x, y), and call (x, y) a cloud droplet of the landing result coordinate set;

[0011] Set the cloud droplet threshold, and judge whether the number of cloud droplets generated by the cloud generator reaches the cloud droplet threshold. If not, perform digital environment simulation or experiment again; if so, collect the parameters of the generated cloud droplets;

[0012] Take the landing points of the n recorded cloud droplets and the m points restored by the cloud model as samples, and calculate the expectations Ex, Ey and entropy En x , En y and hyper-entropy He x , He y , and evaluate the landing result according to the above digital characteristics.

[0013] Preferably, if the constructed landing coordinate system adopts a geodetic coordinate system or other coordinate systems with the origin not being a fixed point on a ship or an airplane in the extended application, collect the heave motion parameters of the landing location and introduce them into the Z-axis to form a three-dimensional coordinate system model.

[0014] Preferably, after the landing coordinate system is constructed, set a wild value detection function module. The wild value detection function module collects the parameters of the digital environment simulation or experiment and sets corresponding judgment thresholds respectively. When the parameter change caused by the system environment or human operation reaches the corresponding judgment threshold, the parameter is eliminated.

[0015] A landing result evaluation method guided by redundant guidance information fusion in this application. First, construct a landing coordinate system, and then calculate the expectations Ex, Ey and entropy En of the landing coordinate set through digital environment simulation or experiment x , En y and hyper-entropy Hex , He y , generate samples, and then generate cloud droplets according to the cloud model algorithm. Each cloud droplet is regarded as a landing point, so as to roughly restore more simulation results and expand the sample data volume; at the same time, according to the reverse cloud generator model, the super entropy of the landing point can be calculated, and the uncertainty of the membership can be effectively evaluated on the basis of realizing the landing point grasp and risk assessment functions, effectively solving the problem of too few samples and not considering the uncertainty of the membership. At the same time, the wild value detection module can be used to eliminate abnormal results caused by the system environment or human operation, and effectively realize the landing result evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution provided by the present application, the following is a brief introduction to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present application.

[0017] Figure 1 This is a schematic diagram of the overall process of this application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the implementation of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below in conjunction with the drawings in the embodiments of this application.

[0019] A landing result evaluation method based on redundant guidance information fusion guidance, such as Figure 1 As shown, including:

[0020] Step S100, constructing a landing coordinate system with the ideal landing point as the origin of the coordinate system, the earth's horizontal plane as the coordinate reference plane, the X-axis pointing to the bow of the ship along the bow-stern line of the landing site as positive, and the Y-axis pointing to the port side as positive;

[0021] Preferably, if the landing coordinate system is constructed in an expanded application using a geodetic coordinate system or other coordinate system whose origin is not a fixed point on a ship or an airplane, the heave motion parameters of the landing site are collected and introduced into the Z axis to form a three-dimensional coordinate system model to ensure that the coordinate system is compatible with the actual environment.

[0022] Preferably, after the landing coordinate system is constructed, an outlier detection function module is set up. The outlier detection function module collects parameters of digital environment simulation or test, and sets corresponding judgment thresholds respectively. When the parameter change caused by the system environment or human operation reaches the corresponding judgment threshold, the parameter is eliminated to avoid outlier data entering the sample and affecting the accuracy of landing result evaluation.

[0023] Step S200, perform multiple digital environment simulations or tests, and record the drop coordinates drop (x i ,y i)(i = 1, 2, …, n);

[0024] Step S300: Construct a cloud generator. Using the principle of the reverse cloud generator, calculate the expectation Ex, Ey, entropy En x , En y , hyperentropy He x , He y ; Through this step, a certain amount of

[0025] If the digital environment simulation or test data volume is sufficient, the following steps are not adopted. In practice, however, the digital environment simulation or test data volume generally fails to meet the usage requirements. Therefore, a cloud generator is needed to generate the required data.

[0026] Step S400: Using the principle of the forward cloud generator, generate a normal random number En x , En y with En x , He y as the standard deviation, taking En x ′, En′ y ;

[0027] Adopting the cloud model structure can effectively and roughly restore more simulation results.

[0028] Step S500: Take Ex, Ey as the expectation. At the same time, take the absolute value of En x ′, En′ y as the standard deviation to generate a normal random coordinate point (x, y), and call (x, y) a cloud droplet of the landing result coordinate set;

[0029] Step S600: Set the cloud droplet threshold. Judge whether the number of cloud droplets generated by the cloud generator reaches the cloud droplet threshold. If not, perform digital environment simulation or test again; if so, collect the parameters of the generated cloud droplets;

[0030] Step S700: Take the landing points of the n recorded cloud droplets and the m points restored by the cloud model as samples, and calculate the expectation Ex, Ey, entropy En x , En y , hyperentropy He x , He y , and evaluate the landing result according to the above digital features.

[0031] In this application, by first constructing a landing coordinate system, and then calculating the expectation Ex, Ey, entropy En x , En y , hyperentropy He x, He y , generate samples, and then generate cloud droplets according to the cloud model algorithm. Each cloud droplet is regarded as a landing point, so as to roughly restore the simulation results of more times and expand the sample data volume. At the same time, according to the inverse cloud generator model, the hyper entropy of the landing point can be calculated. On the basis of realizing the function of grasping the landing point and risk assessment, the uncertainty of the membership degree can be effectively evaluated, and the problems of too few sample sizes and the uncertainty of the membership degree not being considered can be effectively solved. At the same time, an outlier detection module is adopted to eliminate abnormal results caused by the system environment or human operation, and the evaluation of the landing result is effectively realized.

[0032] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating landing results guided by redundant guidance information fusion, characterized in that, it includes: Taking the ideal landing point as the origin of the coordinate system, the earth's horizontal plane as the coordinate reference plane, the X-axis pointing towards the bow along the fore-and-aft line of the landing site is positive, and the Y-axis pointing towards the port side is positive, to construct a landing coordinate system; Perform multiple digital environment simulations or tests, and record the landing coordinates drop(x i ,y i )(i = 1, 2, …, n); Construct a cloud generator, and calculate the expectations Ex, Ey, entropy En of the landing coordinate set using the principle of the reverse cloud generator x , En y , hyper-entropy He x , He y ; Using the principle of the forward cloud generator, generate a normal random number En' with an expectation of En and a standard deviation of He according to the three digital characteristics of the cloud generator x , En y as the expectation, and He x , He y as the standard deviation x , En' y ; Taking Ex and Ey as expectations, and at the same time, taking the absolute value of En′ x , En′ y as the standard deviation, generating a normal random coordinate point (x, y), and calling (x, y) a cloud droplet of the landing result coordinate set; Setting a cloud droplet threshold, and judging whether the number of cloud droplets generated by the cloud generator reaches the cloud droplet threshold. If not, perform digital environment simulation or experiment again; if so, collect the parameters of the generated cloud droplets; Take the landing points of the recorded n cloud droplets and the m points restored by the cloud model as samples, and calculate the expectations Ex and Ey, entropy En x , En y , hyperentropy He x , He y , and evaluate the landing results based on the above numerical characteristics.

2. The method for evaluating landing results guided by redundant guidance information fusion according to claim 1, characterized in that: If the constructed landing coordinate system adopts the geodetic coordinate system or other coordinate systems with the origin not being a fixed point on the ship or the aircraft in the extended application, collect the heave motion parameters of the landing site and introduce them into the Z-axis to form a three-dimensional coordinate system model.

3. The method for evaluating landing results guided by redundant guidance information fusion according to claim 1, characterized in that: After the landing coordinate system is constructed, set an outlier detection function module. The outlier detection function module collects the parameters of the digital environment simulation or experiment and sets corresponding judgment thresholds respectively. When the parameter changes caused by the system environment or human operation reach the corresponding judgment thresholds, the parameter is removed.

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