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
Performance Evaluation technical field more particularly to one kind the present invention relates to Ground Target Tracking State Estimation are based on
The ground target real-time tracking performance estimating method and system of evaluated error distribution.
Background technology
With the development of modern high-precision sensor rapid technological improvement, during ground target real-time tracking, to
The verification of track algorithm performance and evaluation requirement are more and more urgent.Accurate Image Tracking Algorithms Performance verification and appraisal procedure, Neng Goubang
Engineering staff is helped to select the wave filter that choosing meets performance requirement, improves tracking performance.
At present, the verification of existing Image Tracking Algorithms Performance quality and appraisal procedure, be by calculate target time of day and
The size of estimated error mean squares root between estimated state is realized.But error is carried out using estimated error mean squares root
Measurement has the defects of serious, and the error amount of Yi Shou great is dominated, it is impossible to meet the requirement of Performance Evaluation.
Therefore, how to realize that the evaluation that tracking mode estimation technique carries out objective and fair on a surface target has important meaning
Justice.
Invention content
In view of the above problems, it is proposed that the present invention overcomes the above problem in order to provide one kind or solves at least partly
State the ground target real-time tracking performance estimating method and system of problem, can effective evaluation target tracking algorism quality.
One aspect of the present invention provides a kind of ground target real-time tracking performance estimating method, including:
The evaluated error probability distribution of target estimator is obtained, the target estimator is real-time for ground target to be assessed
Tracking mode estimator；
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.
Wherein, it is similar between the analysis evaluated error probability distribution and preset anticipation error probability distribution
Before degree, the method further includes：
Judge the distribution pattern of the anticipation error probability distribution, corresponding similarity point is chosen according to the distribution pattern
Analyse model.
Wherein, if the anticipation error probability distribution is Gaussian Profile or laplacian distribution, using the first similarity
Analysis model analyzes the similarity between the evaluated error probability distribution and the anticipation error probability distribution, first phase
It is as follows like degree analysis model：
Wherein, ρ (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
Wherein, it is similar using second if the anticipation error probability distribution is non-gaussian distribution and laplacian distribution
Similarity between the degree analysis model analysis evaluated error probability distribution and the anticipation error probability distribution, described second
Similarity analysis model is as follows：
Wherein, ρ ' (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
Wherein, if the evaluated error probability distribution is discrete distribution, discrete evaluated error collection is combined into
The similarity analyzed between the evaluated error probability distribution and preset anticipation error probability distribution includes：
The anticipation error set of sampled point quantity identical with evaluated error set is randomly selected from desired distribution
Respectively to describedWithIt is standardized, obtainsWith
It calculates respectivelyWithCorresponding autocorrelation matrix R1And R2, and calculate R1Feature vectorR2
Feature vector
It calculates respectivelyCorrelation between two-by-two, formula are as follows：
According to evaluated error set and the correlation for it is expected each sampled point in sampled point set, determine that the evaluated error is general
Rate is distributed the similarity between preset anticipation error probability distribution.
Another aspect of the present invention provides a kind of ground target real-time tracking performance evaluation system, including：
Evaluated error distributed acquisition module, suitable for obtaining the evaluated error probability distribution of target estimator, the target is estimated
Gauge is ground target real-time tracking state estimator to be assessed；
Similarity analysis module, suitable for analyze the evaluated error probability distribution and preset anticipation error probability distribution it
Between similarity；
Performance estimation module, suitable for carrying out tracking performance assessment to the target estimator according to the similarity.
Wherein, the system also includes：
Determination module, suitable in evaluated error probability distribution described in the similarity analysis module analysis and preset expectation
Before similarity between probability of error distribution, the distribution pattern of the anticipation error probability distribution is judged, according to the distribution
Type chooses corresponding similarity analysis model.
Wherein, the similarity analysis module, it is Gaussian Profile or drawing to be particularly adapted to when the anticipation error probability distribution
During this distribution of pula, using evaluated error probability distribution described in the first similarity analysis model analysis and the anticipation error probability
Similarity between distribution, the first similarity analysis model are as follows：
Wherein, ρ (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
Wherein, the similarity analysis module, be particularly adapted to when the anticipation error probability distribution for non-gaussian distribution and
It is general using evaluated error probability distribution and the anticipation error described in the second similarity analysis model analysis during laplacian distribution
Similarity between rate distribution, the second similarity analysis model are as follows：
Wherein, ρ ' (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
Wherein, the similarity analysis module, specifically includes：
Submodule is sampled, suitable for working as the evaluated error probability distribution for discrete distribution, discrete evaluated error collection is combined intoWhen, the anticipation error set of sampled point quantity identical with evaluated error set is randomly selected from desired distribution
Normalizer module, suitable for respectively to describedWithIt is standardized, obtainsWith
Computational submodule, suitable for calculating respectivelyWithCorresponding autocorrelation matrix R1And R2, and calculate R1Spy
Sign vectorR2Feature vector
The computational submodule is further adapted for calculating respectivelyCorrelation between two-by-two, formula are as follows：
Determination sub-module, suitable for according to evaluated error set and it is expected sampled point set in each sampled point correlation, really
Fixed similarity between the evaluated error probability distribution and preset anticipation error probability distribution.
Ground target real-time tracking performance estimating method provided in an embodiment of the present invention and system, by using measurement error
DistributionRelative to the similarity of a certain reference quantity, that is, levelness amount it is expected, to realize to different conditions estimator quality
Effective evaluation, and then realize the evaluation of the objective and fair of tracking mode estimation technique progress on a surface target.
In the implementation of the present invention, the distributed intelligence using evaluated error, fair and just ground-to-ground face are fully considered
Target state estimator technical performance is assessed, and improves tracking performance.
Above description is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can
It is clearer and more comprehensible, below the special specific embodiment for lifting the present invention.
Description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this field
Technical staff will become clear.Attached drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings：
Fig. 1 is a kind of flow chart of ground target real-time tracking performance estimating method of the embodiment of the present invention；
Fig. 2 is a kind of structure diagram of ground target real-time tracking performance evaluation system of the embodiment of the present invention.
Specific embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Completely it is communicated to those skilled in the art.
Those skilled in the art of the present technique are appreciated that unless otherwise defined all terms used herein are (including technology art
Language and scientific terminology), there is the meaning identical with the general understanding of the those of ordinary skill in fields of the present invention.Should also
Understand, those terms such as defined in the general dictionary, it should be understood that have in the context of the prior art
The consistent meaning of meaning, and unless by specific definitions, otherwise will not be explained with the meaning of idealization or too formal.
The shortcomings that in order to overcome existing evaluation index, realizes that tracking mode estimation technique carries out objective and fair on a surface target
Evaluation, the embodiment of the present invention propose one kind is distributed by measurement errorRelative to the similarity of a certain reference quantity, promptly
Horizontal measurement is hoped, to realize the performance estimating method of different conditions estimator.
Fig. 1 diagrammatically illustrates the flow of the ground target real-time tracking performance estimating method of one embodiment of the invention
Figure.With reference to Fig. 1, the ground target real-time tracking performance estimating method of the embodiment of the present invention specifically includes following steps：
Step S11, the evaluated error probability distribution of target estimator is obtained, the target estimator is ground to be assessed
Object real-time tracking state estimator；
Step S12, the similarity between the evaluated error probability distribution and preset anticipation error probability distribution is analyzed；
Wherein, the anticipation error probability distribution is the standard reference value of target estimator.
Step S13, tracking performance assessment is carried out to the target estimator according to the similarity.
It, will be between evaluated error probability distribution and preset expectation or ideal probability of error distribution in the embodiment of the present invention
The aspiration level (DL, Desirability Level) that is distributed as evaluated error of similarity, i.e., point based on evaluated error
Cloth information.The aspiration level being distributed by introducing evaluated error portrays the distribution of evaluated error and expectation or ideal error point
Correlation or similarity between cloth effectively overcome the defects of existing evaluation index is assessed.
Ground target real-time tracking performance estimating method provided in an embodiment of the present invention, by preset anticipation error probability point
Cloth is used as with reference to measuring, and is distributed by using measurement errorRelative to the similarity of anticipation error probability distribution, that is, it is expected water
Pingdu amount to realize the effective evaluation to different conditions estimator quality, and then realizes the skill of tracking mode estimation on a surface target
Art carries out the evaluation of objective and fair.
The aspiration level being distributed below to the evaluated error proposed in the embodiment of the present invention, which provides, to be illustrated.
Related coefficient form between two variable of analogy defines two estimatorsEvaluated error probability distributionIt is opposite it is expected
The probability of error is distributedAspiration level be defined as：
This measurement features the correlation or similarity between two probability density functions.
Consider in the discrete case, it is assumed that two probability mass functionsMeet：
Then the expression formula of ρ (0) is：
As it can be seen that ρ (0) is considered as the vector of N-dimensionalBetween angle
Cosine value.In the case of continuous, since two probability density functions can regard an infinite dimensional vector as, it is possible to ρ
(0) it is interpreted as the measurement of angle between two distribution functions.
In the calculation, if known desired distribution is Gaussian Profile and laplacian distribution, analysis result can be provided, even
Desired distribution is Gaussian ProfileThen have：
If desired distribution is laplacian distributionThen have：
It is further desired to horizontal extension form further includes following content：
The integral part in formulaWithWhen being difficult accurate calculate, the embodiment of the present invention
Its extension form is given, defining ρ ' (0) isWithRelated coefficient：
This is because apply to probability density function full domain upper integral be 1, i.e.,This
Sample one, enormously simplifies difficulty in computation, avoids completely in original justiceWithTwo integration formulas.
Further, it is contemplated that in practical engineering application, may there is no the relevant information that evaluated error is really distributed.And after dimensionality reduction
The main feature extracted has preferable property：First, the main information of former data is not lost in principal component analysis, belongs to former data
Feature there is unique characteristic vector to be corresponding to it；Next main feature extracted has stability, when evaluated error vector
When having minor change, corresponding main changing features are insensitive, and therefore, the embodiment of the present invention is additionally provided based on principal component analysis
Evaluated error aspiration level.
It to sum up analyzes, the estimation of anticipation error probability distribution or different distributions type for different distributions type misses
Poor probability distribution, for there is different similarity analysis models.Therefore, in the embodiment of the present invention, in the analysis estimation
The probability of error is distributed before the similarity between preset anticipation error probability distribution, and the method further includes：Described in judgement
The distribution pattern of anticipation error probability distribution chooses corresponding similarity analysis model, to realize root according to the distribution pattern
Suitable similarity analysis model is chosen according to the distribution pattern of anticipation error probability distribution and/or evaluated error probability distribution.
In an alternate embodiment of the present invention where, if the anticipation error probability distribution is Gaussian Profile or Laplce
During distribution, using evaluated error probability distribution described in the first similarity analysis model analysis and the anticipation error probability distribution it
Between similarity, the first similarity analysis model is as follows：
Wherein, ρ (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
In an alternate embodiment of the present invention where, if the anticipation error probability distribution is non-gaussian distribution and La Pula
During this distribution, using evaluated error probability distribution described in the second similarity analysis model analysis and the anticipation error probability distribution
Between similarity, the second similarity analysis model is as follows：
Wherein, ρ ' (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
In another embodiment of the present invention, when the true distributed intelligence of evaluated error is unknown, for example, evaluated error is general
When rate is distributed as discrete difference, State Estimation Performance Evaluation is realized based on Principal Component Analysis.
Further, the phase analyzed between the evaluated error probability distribution and preset anticipation error probability distribution
Like degree, specific implementation step is as follows：
Given desired distribution fd~(0, Cd) and discrete evaluated error set
The anticipation error set of sampled point quantity identical with evaluated error set is randomly selected from desired distribution
Respectively to describedWithIt is standardized, obtainsWithWherein,WithIt is full
Foot：
It calculates respectivelyWithCorresponding autocorrelation matrix R1And R2, wherein：
Autocorrelation matrix R is obtained1,R2Characteristic valueAnd calculate R1Feature vectorR2Feature vectorCharacteristic value sorts to obtain in descending orderAnd to feature
Vector adjusts accordingly
It calculates respectivelyCorrelation between two-by-two, formula are as follows：
According to evaluated error set and the correlation for it is expected each sampled point in sampled point set, determine that the evaluated error is general
Rate is distributed the similarity between preset anticipation error probability distribution.
The embodiment of the present invention can be extracted mutually independent between feature and each feature in data using principal component analysis
Property, the method for proposing to calculate the correlation between two distributions based on principal component analysis.If two are distributed with stronger correlation,
If the random sampling site from each distribution, should also there are some features to react this correlation between two datasets, if two
Data set carrys out the strong distribution of self-similarity, and the angle between each principal component direction should can characterize this correlation.Therefore
The angle in principal component direction after respectively sorting can be calculated one by one, if the equal very little of each angle, consider there is very strong phase between two distributions
Guan Xing.
It will be appreciated that when N number of number is smaller, sampling site number can increase；Certainly this is only evaluated error distribution and it is expected
Relevant necessary condition is distributed, so during the angle of two feature vectors of calculating, if angle very little, illustrates that two distributions are respective
This principal component is much like.The embodiment of the present invention by the relativity problem for solving the distribution of higher-dimension error by having resolved into several one
The subproblem of dimension simply, quickly realizes similarity analysis.
For embodiment of the method, in order to be briefly described, therefore it is all expressed as to a series of combination of actions, but this field
Technical staff should know that the embodiment of the present invention is not limited by described sequence of movement, because implementing according to the present invention
Example, certain steps may be used other sequences or are carried out at the same time.Secondly, those skilled in the art should also know, specification
Described in embodiment belong to preferred embodiment, necessary to the involved action not necessarily embodiment of the present invention.
The structure that Fig. 2 diagrammatically illustrates the ground target real-time tracking performance evaluation system of one embodiment of the invention is shown
It is intended to.With reference to Fig. 2, the ground target real-time tracking performance evaluation system of the embodiment of the present invention specifically includes evaluated error distribution and obtains
Modulus block 201, similarity analysis module 202 and performance estimation module 203, wherein：
Evaluated error distributed acquisition module 201, suitable for obtaining the evaluated error probability distribution of target estimator, the target
Estimator is ground target real-time tracking state estimator to be assessed；
Similarity analysis module 202, suitable for analyzing the evaluated error probability distribution and preset anticipation error probability point
Similarity between cloth；
Performance estimation module 203, suitable for carrying out tracking performance assessment to the target estimator according to the similarity.
Ground target real-time tracking performance evaluation system provided in an embodiment of the present invention, by preset anticipation error probability point
Cloth is used as with reference to measuring, and is distributed by using measurement errorRelative to the similarity of anticipation error probability distribution, that is, it is expected water
Pingdu amount to realize the effective evaluation to different conditions estimator quality, and then realizes the skill of tracking mode estimation on a surface target
Art carries out the evaluation of objective and fair.
In this law embodiment, the system also includes determination module unshowned in attached drawing, the determination module is fitted
It is analyzed between the evaluated error probability distribution and preset anticipation error probability distribution in the similarity analysis module 202
Similarity before, judge the distribution pattern of the anticipation error probability distribution, corresponding phase chosen according to the distribution pattern
Like degree analysis model.
In an alternate embodiment of the present invention where, the similarity analysis module 202 is particularly adapted to it is expected to miss when described
It is general using evaluated error described in the first similarity analysis model analysis when poor probability distribution is Gaussian Profile or laplacian distribution
Rate is distributed the similarity between the anticipation error probability distribution, and the first similarity analysis model is as follows：
Wherein, ρ (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
In an alternate embodiment of the present invention where, the similarity analysis module 202 is particularly adapted to it is expected to miss when described
When poor probability distribution is non-gaussian distribution and laplacian distribution, using evaluated error described in the second similarity analysis model analysis
Similarity between probability distribution and the anticipation error probability distribution, the second similarity analysis model are as follows：
Wherein, ρ ' (0) is similarity,For target estimator,For evaluated error probability-distribution function,By a definite date
Hope probability of error distribution function.
In another embodiment of the present invention, the similarity analysis module 202 specifically includes sampling submodule, mark
Quasi- beggar's module, computational submodule and determination sub-module, wherein：
Sample submodule, suitable for when the evaluated error probability distribution be discrete distribution when, discrete evaluated error set
ForWhen, the anticipation error set of sampled point quantity identical with evaluated error set is randomly selected from desired distribution
Normalizer module, suitable for respectively to describedWithIt is standardized, obtainsWith
Computational submodule, suitable for calculating respectivelyWithCorresponding autocorrelation matrix R1And R2, and calculate R1Spy
Sign vectorR2Feature vector
The computational submodule is further adapted for calculating respectivelyCorrelation between two-by-two, formula are as follows：
Determination sub-module, suitable for according to evaluated error set and it is expected sampled point set in each sampled point correlation, really
Fixed similarity between the evaluated error probability distribution and preset anticipation error probability distribution.
For system embodiment, since it is basicly similar to embodiment of the method, so description is fairly simple, it is related
Part illustrates referring to the part of embodiment of the method.
Ground target real-time tracking performance estimating method provided in an embodiment of the present invention and system provide a kind of based on master
The measure of the State Estimation Performance Evaluation of constituent analysis, it is proposed that weigh the measurement of evaluated error distribution aspiration level
Criterion is distributed by using measurement errorRelative to the similarity of a certain reference quantity, that is, levelness amount it is expected, with realization pair
The effective evaluation of different conditions estimator quality, and then realize that tracking mode estimation technique carries out objective and fair on a surface target
Evaluation.
In the implementation of the present invention, the distributed intelligence using evaluated error, fair and just ground-to-ground face are fully considered
Target state estimator technical performance is assessed, and improves tracking performance.
In addition, the embodiment of the present invention additionally provides a kind of computer readable storage medium, computer program is stored thereon with,
The step of method as described in Figure 1 is realized when the program is executed by processor.
In the present embodiment, if module/unit that the ground target real-time tracking performance evaluation system integrates is with software
The form of functional unit is realized and is independent product sale or in use, can be stored in a computer-readable storage
In medium.Based on such understanding, the present invention realizes all or part of flow in above-described embodiment method, can also pass through meter
Calculation machine program is completed to instruct relevant hardware, and the computer program can be stored in a computer readable storage medium
In, the computer program is when being executed by processor, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the calculating
Machine program includes computer program code, and the computer program code can be source code form, object identification code form, can hold
Style of writing part or certain intermediate forms etc..The computer-readable medium can include：The computer program code can be carried
Any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disc, CD, computer storage, read-only memory (ROM,
Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunications letter
Number and software distribution medium etc..It should be noted that the content that the computer-readable medium includes can be managed according to the administration of justice
Local legislation and the requirement of patent practice carry out appropriate increase and decrease, such as in certain jurisdictions, according to legislation and patent
Practice, computer-readable medium do not include electric carrier signal and telecommunication signal.
Computer equipment provided in an embodiment of the present invention, including memory, processor and storage on a memory and can be
The computer program run on processor, the processor realize that above-mentioned each ground target is real when performing the computer program
When tracking performance appraisal procedure embodiment in step, such as method and step shown in FIG. 1.
Illustratively, the computer program can be divided into one or more module/units, one or more
A module/unit is stored in the memory, and is performed by the processor, to complete the present invention.It is one or more
A module/unit can be the series of computation machine program instruction section that can complete specific function, which is used to describe institute
State implementation procedure of the computer program in the ground target real-time tracking performance evaluation system.
The computer equipment can be that the calculating such as desktop PC, notebook, palm PC and cloud server are set
It is standby.
The processor can be central processing unit (Central Processing Unit, CPU), can also be it
His general processor, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable GateArray, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor can also be any conventional processor
Deng the processor is the control centre of the computer equipment, utilizes various interfaces and the entire computer equipment of connection
Various pieces.
The memory can be used for storing the computer program and/or module, and the processor is by running or performing
The computer program and/or module that are stored in the memory and the data being stored in memory are called, described in realization
The various functions of computer equipment.The memory can mainly include storing program area and storage data field, wherein, store program
It area can storage program area, the application program (such as sound-playing function, image player function etc.) needed at least one function
Deng；Storage data field can be stored uses created data (such as audio data, phone directory etc.) etc. according to mobile phone.In addition,
Memory can include high-speed random access memory, can also include nonvolatile memory, such as hard disk, memory, grafting
Formula hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card
(Flash Card), at least one disk memory, flush memory device or other volatile solid-state parts.
It will be appreciated by those of skill in the art that although some embodiments in this are included included by other embodiments
Certain features rather than other feature, but the combination of the feature of different embodiments means to be within the scope of the present invention simultaneously
And form different embodiments.For example, in the following claims, the one of arbitrary of embodiment claimed all may be used
It is used in a manner of in any combination.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that：It still may be used
To modify to the technical solution recorded in foregoing embodiments or carry out equivalent replacement to which part technical characteristic；
And these modification or replace, various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of ground target real-time tracking performance estimating method, which is characterized in that including:
The evaluated error probability distribution of target estimator is obtained, the target estimator is ground target real-time tracking to be assessed
State estimator；
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.
2. according to the method described in claim 1, it is characterized in that, in the analysis evaluated error probability distribution with presetting
Anticipation error probability distribution between similarity before, the method further includes：
Judge the distribution pattern of the anticipation error probability distribution, corresponding similarity analysis mould is chosen according to the distribution pattern
Type.
3. if according to the method described in claim 2, it is characterized in that, the anticipation error probability distribution is Gaussian Profile or drawing
During this distribution of pula, using evaluated error probability distribution described in the first similarity analysis model analysis and the anticipation error probability
Similarity between distribution, the first similarity analysis model are as follows：
Wherein, ρ (0) is similarity,For target estimator,For evaluated error probability-distribution function,It is expected to miss
Poor probability-distribution function.
If 4. according to the method described in claim 2, it is characterized in that, the anticipation error probability distribution for non-gaussian distribution and
It is general using evaluated error probability distribution and the anticipation error described in the second similarity analysis model analysis during laplacian distribution
Similarity between rate distribution, the second similarity analysis model are as follows：
Wherein, ρ ' (0) is similarity,For target estimator,For evaluated error probability-distribution function,It is expected to miss
Poor probability-distribution function.
If 5. according to the method described in claim 1, it is characterized in that, the evaluated error probability distribution be discrete distribution when,
Discrete evaluated error collection is combined into
The similarity analyzed between the evaluated error probability distribution and preset anticipation error probability distribution includes：
The anticipation error set of sampled point quantity identical with evaluated error set is randomly selected from desired distribution
Respectively to describedWithIt is standardized, obtainsWith
It calculates respectivelyWithCorresponding autocorrelation matrix R1And R2, and calculate R1Feature vectorR2Spy
Sign vector
It calculates respectivelyCorrelation between two-by-two, formula are as follows：
According to evaluated error set and the correlation for it is expected each sampled point in sampled point set, the evaluated error probability point is determined
Similarity between cloth and preset anticipation error probability distribution.
6. a kind of ground target real-time tracking performance evaluation system, which is characterized in that including:
Evaluated error distributed acquisition module, suitable for obtaining the evaluated error probability distribution of target estimator, the target estimator
For ground target real-time tracking state estimator to be assessed；
Similarity analysis module, suitable for analyzing between the evaluated error probability distribution and preset anticipation error probability distribution
Similarity；
Performance estimation module, suitable for carrying out tracking performance assessment to the target estimator according to the similarity.
7. system according to claim 6, which is characterized in that the system also includes：
Determination module, suitable in evaluated error probability distribution described in the similarity analysis module analysis and preset anticipation error
Before similarity between probability distribution, the distribution pattern of the anticipation error probability distribution is judged, according to the distribution pattern
Choose corresponding similarity analysis model.
8. system according to claim 7, which is characterized in that the similarity analysis module was particularly adapted to when the phase
When hoping that the probability of error is distributed as Gaussian Profile or laplacian distribution, missed using estimation described in the first similarity analysis model analysis
Similarity between poor probability distribution and the anticipation error probability distribution, the first similarity analysis model are as follows：
Wherein, ρ (0) is similarity,For target estimator,For evaluated error probability-distribution function,It is expected to miss
Poor probability-distribution function.
9. system according to claim 7, which is characterized in that the similarity analysis module was particularly adapted to when the phase
When hoping that the probability of error is distributed as non-gaussian distribution and laplacian distribution, using estimation described in the second similarity analysis model analysis
The probability of error is distributed the similarity between the anticipation error probability distribution, and the second similarity analysis model is as follows：
Wherein, ρ ' (0) is similarity,For target estimator,For evaluated error probability-distribution function,It is expected to miss
Poor probability-distribution function.
10. system according to claim 6, which is characterized in that the similarity analysis module specifically includes：
Submodule is sampled, suitable for working as the evaluated error probability distribution for discrete distribution, discrete evaluated error collection is combined into
When, the anticipation error set of sampled point quantity identical with evaluated error set is randomly selected from desired distribution
Normalizer module, suitable for respectively to describedWithIt is standardized, obtainsWith
Computational submodule, suitable for calculating respectivelyWithCorresponding autocorrelation matrix R1And R2, and calculate R1Feature to
AmountR2Feature vector
The computational submodule is further adapted for calculating respectivelyCorrelation between two-by-two, formula are as follows：
Determination sub-module, suitable for according to evaluated error set and the correlation for it is expected each sampled point in sampled point set, determining institute
State the similarity between evaluated error probability distribution and preset anticipation error probability distribution.
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