CN108227750A - A kind of ground target real-time tracking performance estimating method and system - Google Patents
A kind of ground target real-time tracking performance estimating method and system Download PDFInfo
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Abstract
The present invention provides a kind of ground target real-time tracking performance estimating method and system, this method include：The evaluated error probability distribution of target estimator is obtained, the target estimator is ground target real-time tracking state estimator to be assessed；Analyze the similarity between the evaluated error probability distribution and preset anticipation error probability distribution；Tracking performance assessment is carried out to the target estimator according to the similarity.The present invention is distributed by using measurement errorRelative to the similarity for it is expected reference quantity, that is, it is expected levelness amount, realize the effective evaluation to different conditions estimator quality, and then realize the evaluation of the objective and fair of tracking mode estimation technique progress on a surface target.
Description
Technical Field
The invention relates to the technical field of performance evaluation of a ground target tracking state estimation technology, in particular to a ground target real-time tracking performance evaluation method and system based on estimation error distribution.
Background
With the rapid development of the modern high-precision sensor technology, the requirements for verifying and evaluating the performance of a tracking algorithm are more and more urgent in the real-time tracking process of a ground target. The accurate tracking algorithm performance verification and evaluation method can help engineers select the filter meeting the performance requirement, and the tracking performance is improved.
At present, the existing verification and evaluation method for the performance of the tracking algorithm is realized by calculating the size of the root mean square of the estimation error between the target real state and the estimation state. However, the error measurement using the root mean square of the estimated error has a serious drawback, and is easily dominated by a large error value, and cannot meet the requirement of performance evaluation.
Therefore, how to objectively and fairly evaluate the ground target tracking state estimation technology has important significance.
Disclosure of Invention
In view of the above problems, the present invention is proposed to provide a method and a system for evaluating real-time tracking performance of a ground target, which overcome or at least partially solve the above problems, and can effectively evaluate the quality of a target tracking algorithm.
In one aspect of the present invention, a method for evaluating real-time tracking performance of a ground target is provided, which includes:
acquiring estimation error probability distribution of a target estimator, wherein the target estimator is a ground target real-time tracking state estimator to be estimated;
analyzing the similarity between the estimation error probability distribution and a preset expected error probability distribution;
and evaluating the tracking performance of the target estimator according to the similarity.
Wherein, prior to said analyzing a similarity between said estimated error probability distribution and a preset expected error probability distribution, said method further comprises:
and judging the distribution type of the expected error probability distribution, and selecting a corresponding similarity analysis model according to the distribution type.
If the expected error probability distribution is gaussian distribution or laplace distribution, analyzing the similarity between the estimated error probability distribution and the expected error probability distribution by using a first similarity analysis model, where the first similarity analysis model is as follows:
wherein ρ (0) is a similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
If the expected error probability distribution is a non-gaussian distribution and a laplace distribution, analyzing the similarity between the estimated error probability distribution and the expected error probability distribution by using a second similarity analysis model, wherein the second similarity analysis model is as follows:
where ρ' (0) is the degree of similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
Wherein, if the estimation error probability distribution is discrete distribution, the discrete estimation error set is
The analyzing the similarity between the estimation error probability distribution and a preset expected error probability distribution comprises:
randomly extracting the expected error set with the same number of sampling points as the estimated error set from the expected distribution
Respectively to theAndcarrying out standardization to obtainAnd
respectively calculateAndcorresponding autocorrelation matrix R1And R2And calculating R1Feature vector ofR2Feature vector of
Respectively calculateThe correlation between two is shown as follows:
and determining the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
In another aspect of the present invention, a system for evaluating real-time tracking performance of a ground target is provided, which includes:
the estimation error distribution acquisition module is suitable for acquiring the estimation error probability distribution of a target estimator, and the target estimator is a real-time tracking state estimator of a ground target to be estimated;
a similarity analysis module adapted to analyze a similarity between the estimated error probability distribution and a preset expected error probability distribution;
and the performance evaluation module is suitable for evaluating the tracking performance of the target estimator according to the similarity.
Wherein the system further comprises:
and the judging module is suitable for judging the distribution type of the expected error probability distribution before the similarity analyzing module analyzes the similarity between the estimation error probability distribution and the preset expected error probability distribution, and selecting a corresponding similarity analyzing model according to the distribution type.
The similarity analysis module is specifically adapted to, when the expected error probability distribution is a gaussian distribution or a laplace distribution, analyze the similarity between the estimated error probability distribution and the expected error probability distribution by using a first similarity analysis model, where the first similarity analysis model is as follows:
wherein ρ (0) is a similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
The similarity analysis module is specifically adapted to, when the expected error probability distribution is a non-gaussian distribution and a laplace distribution, analyze the similarity between the estimated error probability distribution and the expected error probability distribution by using a second similarity analysis model, where the second similarity analysis model is as follows:
where ρ' (0) is the degree of similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
The similarity analysis module specifically comprises:
a sampling sub-module adapted to sample a set of discrete estimation errors when the estimation error probability distribution is a discrete distributionRandomly extracting the expected error set with the same number of sampling points as the estimated error set from the expected distribution
Standardized submodules adapted to separately align theAndcarrying out standardization to obtainAnd
a computation submodule adapted to separately computeAndcorresponding autocorrelation matrix R1And R2And calculating R1Feature vector ofR2Feature vector of
The computation submodule is also adapted to compute separatelyThe correlation between two is shown as follows:
and the determining submodule is suitable for determining the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
The ground target real-time tracking performance evaluation method and system provided by the embodiment of the invention adopt measurement error distributionAnd the similarity, namely the expected level measurement, relative to a certain reference quantity is used for realizing effective evaluation on the advantages and disadvantages of different state estimators, and further realizing objective and fair evaluation on the ground target tracking state estimation technology.
In the process of realizing the method, the distribution information of estimation errors is fully considered, the technical performance of the ground target state estimation is evaluated fairly and fairly, and the tracking performance is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flowchart of a method for evaluating real-time tracking performance of a ground target according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system for evaluating real-time tracking performance of a ground target according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In order to overcome the defects of the existing evaluation indexes and realize objective and fair evaluation on the ground target tracking state estimation technology, the embodiment of the invention provides a method for evaluating the ground target tracking state by measuring error distributionSimilarity with respect to some reference, i.e. a desired level measure, to implement a performance evaluation method for different state estimators.
Fig. 1 schematically shows a flowchart of a ground target real-time tracking performance evaluation method according to an embodiment of the present invention. Referring to fig. 1, the ground target real-time tracking performance evaluation method of the embodiment of the invention specifically includes the following steps:
step S11, obtaining the estimation error probability distribution of a target estimator, wherein the target estimator is a ground target real-time tracking state estimator to be evaluated;
step S12, analyzing the similarity between the estimation error probability distribution and the preset expected error probability distribution; wherein the expected error probability distribution is a standard reference value of a target estimator.
And step S13, performing tracking performance evaluation on the target estimator according to the similarity.
In the embodiment of the present invention, the similarity between the estimation error probability distribution and a preset expected or ideal error probability distribution is used as an expected Level (DL) of the estimation error distribution, that is, distribution information based on the estimation error. By introducing the expected level of the estimation error distribution and describing the correlation or similarity between the distribution of the estimation error and the expected or ideal error distribution, the defect of the existing estimation index estimation is effectively overcome.
Practice of the inventionThe ground target real-time tracking performance evaluation method provided by the embodiment takes the preset expected error probability distribution as a reference quantity, and measures the error distributionAnd the similarity of the probability distribution relative to the expected error, namely the expected level measurement, is used for realizing effective evaluation on the advantages and disadvantages of different state estimators, and further realizing objective and fair evaluation on the ground target tracking state estimation technology.
A specific explanation is given below of the desired level of the estimation error distribution proposed in the embodiment of the present invention.
Defining two estimators in the form of correlation coefficients between two analog variablesEstimating error probability distributionRelative expected error probability distributionThe desired level of (d) is defined as:
this metric characterizes the correlation or similarity between two probability density functions.
Consider that in the discrete case, two probability mass functions are assumedSatisfies the following conditions:
the expression for ρ (0) is then:
as can be seen, ρ (0) can be viewed as a vector of N dimensionsThe cosine of the angle between them. In the continuous case, since the two probability density functions can be regarded as vectors of an infinite dimension, ρ (0) can be understood as a measure of the angle between the two distribution functions.
In the calculation, if the expected distribution is known as a gaussian distribution and a laplace distribution, an analytic result can be given, i.e., if the expected distribution is a gaussian distributionThen there are:
if the desired distribution is a Laplace distributionThen there are:
further, the extended form of the desired level also includes the following:
integral part of the equationAndit is difficult to accurately countIn calculation, the embodiment of the invention also provides an extended form thereof, and the definition rho' (0) isAndcorrelation coefficient of (d):
this is due to the application of integrating the probability density function over the full domain of definition to 1, i.e.Thus, the calculation difficulty is greatly simplified, and the original definition is completely avoidedAndtwo integral equations.
Furthermore, consider that in practical engineering applications, there may be no relevant information to estimate the true distribution of errors. The main features extracted after dimensionality reduction have better properties: firstly, principal component analysis does not lose the main information of original data, and the features belonging to the original data have unique feature vectors corresponding to the feature vectors; the extracted main features have stability, and when the estimation error vector has small change, the corresponding main feature change is insensitive, so the embodiment of the invention also provides an estimation error expectation level based on the principal component analysis.
In summary, the analysis model is analyzed for similarity for expected error probability distributions of different distribution types or estimated error probability distributions of different distribution types. Therefore, in this embodiment of the present invention, before the analyzing the similarity between the estimation error probability distribution and the preset expected error probability distribution, the method further includes: and judging the distribution type of the expected error probability distribution, and selecting a corresponding similarity analysis model according to the distribution type so as to select a proper similarity analysis model according to the expected error probability distribution and/or the distribution type of the estimated error probability distribution.
In an optional embodiment of the present invention, if the expected error probability distribution is a gaussian distribution or a laplace distribution, a first similarity analysis model is used to analyze a similarity between the estimated error probability distribution and the expected error probability distribution, where the first similarity analysis model is as follows:
wherein ρ (0) is a similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
In an optional embodiment of the present invention, if the expected error probability distribution is a non-gaussian distribution and a laplace distribution, a second similarity analysis model is used to analyze a similarity between the estimated error probability distribution and the expected error probability distribution, where the second similarity analysis model is as follows:
where ρ' (0) is the degree of similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
In another embodiment of the invention, state estimation technique performance evaluation is achieved based on principal component analysis when estimation error true distribution information is unknown, e.g., estimation error probability distribution is discrete.
Further, the analyzing the similarity between the estimated error probability distribution and a preset expected error probability distribution includes the following specific steps:
given a desired distribution fd～(0,Cd) And a discrete set of estimation errors
Randomly extracting the expected error set with the same number of sampling points as the estimated error set from the expected distribution
Respectively to theAndcarrying out standardization to obtainAndwherein,andsatisfies the following conditions:
respectively calculateAndcorresponding autocorrelation matrix R1And R2Wherein:
solving an autocorrelation matrix R1,R2Characteristic value ofAnd calculating R1Feature vector ofR2Feature vector ofThe eigenvalues are sorted in descending order to obtainAnd correspondingly adjusting the characteristic vector
Respectively calculateThe correlation between two is shown as follows:
and determining the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
The embodiment of the invention provides a method for calculating the correlation between two distributions based on principal component analysis, which can extract the characteristics in data by using the principal component analysis and has the characteristic that the characteristics are independent from each other. If the two distributions have strong correlation, if points are randomly acquired from each distribution, some characteristics should be used between the two data sets to reflect the correlation, and if the two data sets are from the distribution with strong similarity, the included angle between the principal component directions of the two data sets should be capable of representing the correlation. Therefore, the included angles of the main component directions after respective sequencing can be calculated one by one, and if each included angle is very small, the strong correlation between the two distributions is considered.
It can be understood that when the number of the N points is smaller, the number of the sampling points can be increased; of course, this is only a necessary condition for estimating the correlation between the error distribution and the expected distribution, so when the included angle between the two eigenvectors is calculated, if the included angle is small, it indicates that the principal components of the two distributions are very similar. According to the embodiment of the invention, the problem of solving the relevance of the high-dimensional error distribution is decomposed into a plurality of one-dimensional sub-problems, so that the similarity analysis is simply and quickly realized.
For simplicity of explanation, the method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the embodiments of the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Fig. 2 schematically shows a structural diagram of a ground target real-time tracking performance evaluation system according to an embodiment of the invention. Referring to fig. 2, the system for evaluating the real-time tracking performance of a ground target in the embodiment of the present invention specifically includes an estimation error distribution obtaining module 201, a similarity analyzing module 202, and a performance evaluating module 203, where:
an estimation error distribution obtaining module 201, adapted to obtain an estimation error probability distribution of a target estimator, where the target estimator is a ground target real-time tracking state estimator to be evaluated;
a similarity analysis module 202 adapted to analyze a similarity between the estimated error probability distribution and a preset expected error probability distribution;
and the performance evaluation module 203 is suitable for carrying out tracking performance evaluation on the target estimator according to the similarity.
The ground target real-time tracking performance evaluation system provided by the embodiment of the invention takes the preset expected error probability distribution as a reference quantity, and measures the error distributionAnd the similarity of the probability distribution relative to the expected error, namely the expected level measurement, is used for realizing effective evaluation on the advantages and disadvantages of different state estimators, and further realizing objective and fair evaluation on the ground target tracking state estimation technology.
In this embodiment of the method, the system further includes a determining module, not shown in the drawings, adapted to determine a distribution type of the expected error probability distribution before the similarity analyzing module 202 analyzes the similarity between the estimated error probability distribution and a preset expected error probability distribution, and select a corresponding similarity analyzing model according to the distribution type.
In an optional embodiment of the present invention, the similarity analysis module 202 is specifically adapted to, when the expected error probability distribution is a gaussian distribution or a laplace distribution, analyze a similarity between the estimated error probability distribution and the expected error probability distribution by using a first similarity analysis model, where the first similarity analysis model is as follows:
wherein ρ (0) is a similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
In an optional embodiment of the present invention, the similarity analysis module 202 is specifically adapted to, when the expected error probability distribution is a non-gaussian distribution and a laplacian distribution, analyze the similarity between the estimated error probability distribution and the expected error probability distribution by using a second similarity analysis model, where the second similarity analysis model is as follows:
where ρ' (0) is the degree of similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
In another embodiment of the present invention, the similarity analysis module 202 specifically includes a sampling sub-module, a normalization sub-module, a calculation sub-module, and a determination sub-module, where:
a sampling sub-module adapted to aggregate discrete estimation errors into a set when the estimation error probability distribution is a discrete distributionRandomly extracting the expected error set with the same number of sampling points as the estimated error set from the expected distribution
Standardized submodules adapted to separately align theAndcarrying out standardization to obtainAnd
a computation submodule adapted to separately computeAndcorresponding autocorrelation matrix R1And R2And calculating R1Feature vector ofR2Feature vector of
The computation submodule is also adapted to compute separatelyThe correlation between two is shown as follows:
and the determining submodule is suitable for determining the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
For the system embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiment of the invention provides a ground target real-time tracking performance evaluation method and system, provides a state estimation technology performance evaluation measurement method based on principal component analysis, provides a measurement criterion for measuring an estimation error distribution expectation level, and measures the error distribution by adoptingAnd the similarity, namely the expected level measurement, relative to a certain reference quantity is used for realizing effective evaluation on the advantages and disadvantages of different state estimators, and further realizing objective and fair evaluation on the ground target tracking state estimation technology.
In the process of realizing the method, the distribution information of estimation errors is fully considered, the technical performance of the ground target state estimation is evaluated fairly and fairly, and the tracking performance is improved.
Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method as described in fig. 1.
In this embodiment, the integrated module/unit of the ground target real-time tracking performance evaluation system may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The computer device provided by the embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps in the above-mentioned embodiments of the method for evaluating real-time tracking performance of a ground target, for example, the method steps shown in fig. 1, are implemented.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the ground target real-time tracking performance evaluation system.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for evaluating the real-time tracking performance of a ground target is characterized by comprising the following steps:
acquiring estimation error probability distribution of a target estimator, wherein the target estimator is a ground target real-time tracking state estimator to be estimated;
analyzing the similarity between the estimation error probability distribution and a preset expected error probability distribution;
and evaluating the tracking performance of the target estimator according to the similarity.
2. The method of claim 1, wherein prior to said analyzing a similarity between said estimated error probability distribution and a preset expected error probability distribution, said method further comprises:
and judging the distribution type of the expected error probability distribution, and selecting a corresponding similarity analysis model according to the distribution type.
3. The method of claim 2, wherein if the expected error probability distribution is a gaussian distribution or a laplacian distribution, a first similarity analysis model is used to analyze the similarity between the estimated error probability distribution and the expected error probability distribution, and the first similarity analysis model is as follows:
wherein ρ (0) is a similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
4. The method of claim 2, wherein if the expected error probability distribution is a non-gaussian distribution and a laplacian distribution, a second similarity analysis model is used to analyze the similarity between the estimated error probability distribution and the expected error probability distribution, wherein the second similarity analysis model is as follows:
where ρ' (0) is the degree of similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
5. The method of claim 1, wherein if the estimation error probability distribution is a discrete distribution, the discrete estimation error set is
The analyzing the similarity between the estimation error probability distribution and a preset expected error probability distribution comprises:
randomly extracting the expected error set with the same number of sampling points as the estimated error set from the expected distribution
Respectively to theAndcarrying out standardization to obtainAnd
respectively calculateAndcorresponding autocorrelation matrix R1And R2And calculating R1Feature vector ofR2Feature vector of
Respectively calculateThe correlation between two is shown as follows:
and determining the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
6. A system for real-time tracking performance assessment of a ground target, comprising:
the estimation error distribution acquisition module is suitable for acquiring the estimation error probability distribution of a target estimator, and the target estimator is a real-time tracking state estimator of a ground target to be estimated;
a similarity analysis module adapted to analyze a similarity between the estimated error probability distribution and a preset expected error probability distribution;
and the performance evaluation module is suitable for evaluating the tracking performance of the target estimator according to the similarity.
7. The system of claim 6, further comprising:
and the judging module is suitable for judging the distribution type of the expected error probability distribution before the similarity analyzing module analyzes the similarity between the estimation error probability distribution and the preset expected error probability distribution, and selecting a corresponding similarity analyzing model according to the distribution type.
8. The system according to claim 7, wherein the similarity analysis module is specifically adapted to analyze the similarity between the estimated error probability distribution and the expected error probability distribution using a first similarity analysis model when the expected error probability distribution is a gaussian distribution or a laplace distribution, the first similarity analysis model being as follows:
wherein ρ (0) is a similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
9. The system according to claim 7, wherein the similarity analysis module is specifically adapted to analyze the similarity between the estimated error probability distribution and the expected error probability distribution using a second similarity analysis model when the expected error probability distribution is a non-gaussian distribution and a laplace distribution, the second similarity analysis model being as follows:
where ρ' (0) is the degree of similarity,in order for the target estimator to be,in order to estimate the error probability distribution function,is a desired error probability distribution function.
10. The system of claim 6, wherein the similarity analysis module specifically comprises:
a sampling sub-module adapted to sample a set of discrete estimation errors when the estimation error probability distribution is a discrete distributionRandomly extracting the expected error set with the same number of sampling points as the estimated error set from the expected distribution
Standardized submodules adapted to separately align theAndcarrying out standardization to obtainAnd
a computation submodule adapted to separately computeAndcorresponding autocorrelation matrix R1And R2And calculating R1Feature vector ofR2Feature vector of
The computation submodule is also adapted to compute separatelyThe correlation between two is shown as follows:
and the determining submodule is suitable for determining the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
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