CN112417377B - Efficiency evaluation method for military reconnaissance system - Google Patents

Efficiency evaluation method for military reconnaissance system Download PDF

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CN112417377B
CN112417377B CN202011298467.0A CN202011298467A CN112417377B CN 112417377 B CN112417377 B CN 112417377B CN 202011298467 A CN202011298467 A CN 202011298467A CN 112417377 B CN112417377 B CN 112417377B
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李俊
范斌
胡磊
戴文瑞
孙吉红
武昕伟
王锐
吴坤
陆珊珊
秦鹏程
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PLA Army Academy of Artillery and Air Defense
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Abstract

The invention provides a performance evaluation method of a military reconnaissance system, which comprises the steps of establishing a performance evaluation model based on an information theory, wherein the establishment of the performance evaluation model comprises the establishment of an information integrity model, an information accuracy model and an information timeliness model, and the performance evaluation model comprises an accuracy Q Sacc Integrity Q Scomp And the ageing degree Q Scurr Composition, overall efficacy E, can be expressed as: e=w Sacc Q Sacc +W Scomp Q Scomp +W Scurr Q Scurr Wherein W is Sacc 、W Scomp And W is Scurr Accuracy Q respectively Sacc Integrity Q Scomp And the ageing degree Q Scurr Is a weight of (a). The invention establishes the information integrity model, the information accuracy model and the information timeliness model of the military reconnaissance system, achieves the aim of evaluating the comprehensive combat effectiveness of the military reconnaissance system, has complete theory and advanced technology, has strong operability, is suitable for evaluating the combat effectiveness of various military reconnaissance systems, and can provide technical support for equipment development, combat planning and other works.

Description

Efficiency evaluation method for military reconnaissance system
Technical Field
The invention relates to the field of military reconnaissance, in particular to a performance evaluation method of a military reconnaissance system.
Background
The military reconnaissance system mainly carries out reconnaissance on key areas on a battlefield, then processes and synthesizes the reconnaissance data to generate corresponding information, provides information support for a command decision system or a combat unit, measures the size of the information support function, mainly monitors whether the reconnaissance system can timely, accurately and reliably provide support information, and does not have a method for evaluating the effectiveness of the military reconnaissance system in the prior art.
Disclosure of Invention
The invention aims to provide a performance evaluation method of a military reconnaissance system, so as to solve the technical problems.
The invention aims to solve the technical problems, and is realized by adopting the following technical scheme: a performance evaluation method of a military reconnaissance system comprises the steps of establishing a performance evaluation model based on an information theory, wherein the establishment of the performance evaluation model comprises the establishment of an information integrity model, an information accuracy model and an information timeliness model, and the performance evaluation model consists of an accuracy Q Sacc Integrity Q Scomp And the ageing degree Q Scurr Composition, overall efficacy E, can be expressed as: e=w Sacc Q Sacc +W Scomp Q Scomp +W Scurr Q Scurr Wherein W is Sacc 、W Scomp And W is Scurr Accuracy Q respectively Sacc Integrity Q Scomp And the ageing degree Q Scurr Is a weight of (a).
Preferably, the information is a state description of the system or the event or a message about the state, let m= { x 1 ,x 2 ,…,x n The system X is a set of all possible states, p= { P 1 ,p 2 ,…,p n The probability of occurrence of each state is set, and according to the concept of shannon entropy, if the probability of occurrence of a state of a system or event can be represented mathematically, the entropy of information describing the system is:
H(x)＝-∑xlog[p(x)]
if the system state set is a continuous interval [ a, b ], and there is a probability distribution density function f (x), then the information entropy is:
preferably, the information integrity model establishment includes a target detection process, provided with n signals to be detected, and letting H 0 Indicating "no target signal present", H 1 Indicating "target signal present", D 0 Indicating "decision as target", D 1 Indicating "decision as no target" for H 0 :z(t)、H 1 Z (t) is used for carrying out statistical test to judge which hypothesis is established, z (t) is an observation signal, and H is compared 0 And H 1 The magnitude of the probability of occurrence, i.e. the comparison of the posterior probability P (H 0 Z) and P (H) 1 I z), which probability is large decides which is true, expressed by a decision formula as P (H) 1 |z)＞P(H 0 Z) is established, it is determined as H 1 ，P(H 1 |z)＜P(H 0 Z) is established, it is determined as H 0 The posterior probability can be expressed as follows, according to the bayesian formula:wherein, P (z) is the probability density of z, and P (zH) 0 )、P(zH 1 ) Is a conditional probability density, when->When established, judge as H 1 The method comprises the steps of carrying out a first treatment on the surface of the When->When established, judge as H 0 ；P(H 0 )、P(H 1 ) Respectively H 0 Hypothesis sum H 1 The a priori probabilities of the hypotheses, generally known as a priori knowledge,/, are assumed>Is a decision threshold;
the binary detection problem decision makes four cases that describe the performance of the binary detection device, which can be expressed in terms of conditional probabilities as follows:
(1) Set H 0 Assuming true, the decision is D 0 Represents selection H 0 For true correct decisions, the conditional probability P (D 0 |H 0 ) The representation is made of a combination of a first and a second color,
(2) Set H 1 Assuming true, the decision is D 1 Represents selection H 1 For true correct decisions, the conditional probability P (D 1 |H 1 ) The representation is made of a combination of a first and a second color,in signal detection, the signal is the target signal and the target is judged, also called detection probability, P is used d The representation is made of a combination of a first and a second color,
(3) Set H 0 Assuming true, the decision is D 1 Represents selection H 0 For false first type erroneous decisions, the conditional probability P (D 1 |H 1 ) The representation is made of a combination of a first and a second color,in the signal detection, no target signal is judged to be a target, also called false alarm probability, P is used f The representation is made of a combination of a first and a second color,
(4) Set H 1 Assuming true, the decision is D 0 Represents selection H 1 For false decisions of the second type, the conditional probability P (D 0 |H 1 ) The representation is made of a combination of a first and a second color,in signal detection, the signal is the target signal and the signal is judged to be the target-free signal, which is also called as the probability of alarm leakage, P is used m A representation;
according to the Bayes formula and the full probability formula, the corresponding posterior probability can be obtained:
from the basic principle of the information theory, it is known that H (X) represents the degree of loss of information amount caused by false alarm and missed alarm, the greater the false alarm and missed alarm probability, the greater the loss of information,
the information acquisition integrity model is as follows
In the middle of
H max (X) -entropy at maximum uncertainty, reached when both false alarm probability and false alarm probability are 0.5;
H min (X) -entropy with minimum uncertainty, reached when both false alarm probability and false alarm probability are 0;
the result of the enemy disturbance reduces my correct decision P (D 0 |H 0 ) And P (D) 1 |H 1 ) (probability of detection) thereby increasing H (X), Q Scomp (X) decrease.
Preferably, the information accuracy model building step is as follows:
is provided with aThe dimensional random variable is [ -delta, delta]The interval obeys the uniform distribution of equal probability, delta is the maximum range of uncertainty of random variables, is generally known as priori knowledge, and the probability density function isIts information entropy is->If the one-dimensional continuous random variable is subjected to normal distribution, the probability density function is +.>Its entropy is
Then the difference of the information entropy of the two is the reduction degree of the uncertainty rangeGeneralizing this conclusion to the N-dimensional case, N-dimensional continuous random vector x= (X) 1 ,x 2 ,…,x n ) T Is defined as the joint entropy of (2)If the N-dimensional continuous random vector X obeys a normal distribution, its probability density function is
Wherein μ= [ μ ] 1 ,μ 2 ,…,μ N ]Is the mean and has covariance matrix
The non-diagonal elements being random variablesx i And x j Is formed by sigma i,j ＝(x i -μ i )(x j -μ j ) Obtained, random variable x i And x j The correlation of (2) is expressed asSigma when i=j i,j The variance of the covariance matrix;
the joint entropy of the N-dimensional continuous random vector X is
Where Sigma is the modulus of the determinant of the covariance matrix Sigma, and since n is a constant, H (X) is simplified to obtain the relative entropy H r (X) =log|Σ|, i.e. only with respect to covariance,
for a normal distribution of multiple variables, it is first assumed that the maximum joint entropy exists, i.e. that a uniform distribution is achieved
H max (X)＝log|∑| max
Definition Q Sacc (X) is interval [0,1 ]]A value in between, and has
The information entropy can be used to obtain a representation Q of the accuracy of information acquisition Sacc (X)，0≤Q Sacc (X). Ltoreq.1 reflects the accuracy of information acquisition, i.e. for the information element { a } 1 ,a 2 ,…,a C The values and the degree of mastery of the relationship between them are determined as Q Sacc (X) →1, represents the highest accuracy, and Q Sacc (X) →0 means that the accuracy is the lowest.
Preferably, the information aging model building step is as follows: the degree of freshness of the obtained information described by the time of the information can be expressed as
t i Indicating the current time, i.e. the time when the combat unit puts forward the information demand, t l Refers to the latest update time of the information, t 0 The time when the information actually starts to exist is defined as η, which is a coefficient related to the importance of the information. .
The beneficial effects of the invention are as follows:
the information theory method is adopted in the invention, the information integrity model, the information accuracy model and the information timeliness model of the military reconnaissance system are established, the aim of evaluating the comprehensive combat effectiveness of the military reconnaissance system is achieved, the evaluation method is complete in theory and advanced in technology, has strong operability, is suitable for evaluating the combat effectiveness of various military reconnaissance systems, and can provide technical support for equipment development, combat planning and other works.
Drawings
FIG. 1 is a schematic diagram of the object detection process of the present invention;
FIG. 2 is a graph of the probability of transition of target detection according to the present invention;
FIG. 3 is a schematic diagram of the time efficiency of information acquisition according to the present invention.
Detailed Description
In order that the manner in which the above recited features, objects and advantages of the present invention are obtained, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Based on the examples in the embodiments, those skilled in the art can obtain other examples without making any inventive effort, which fall within the scope of the invention.
Specific embodiments of the present invention are described below with reference to the accompanying drawings.
Example 1
As shown in FIG. 1, a performance evaluation method of a military reconnaissance system comprises the steps of establishing a performance evaluation model based on an information theory, wherein the establishment of the performance evaluation model comprises the establishment of an information integrity model, an information accuracy model and an information timeliness model, and the performance evaluation model consists of an accuracy Q Sacc Integrity Q Scomp And the ageing degree Q Scurr Composition, overall efficacy E, can be expressed as: e=w Sacc Q Sacc +W Scomp Q Scomp +W Scurr Q Scurr Wherein W is Sacc 、W Scomp And W is Scurr Accuracy Q respectively Sacc Integrity Q Scomp And the ageing degree Q Scurr Is a weight of (a).
The information theory is a state description of the system or the event or a message about the state, let M= { x 1 ,x 2 ,…,x n The system X is a set of all possible states, p= { P 1 ,p 2 ,…,p n The probability of occurrence of each state is set, and according to the concept of shannon entropy, if the probability of occurrence of a state of a system or event can be represented mathematically, the entropy of information describing the system is:
H(x)＝-∑xlog[p(x)]
if the system state set is a continuous interval [ a, b ], and there is a probability distribution density function f (x), then the information entropy is:
the information integrity model establishment comprises a target detection process, wherein n signals to be detected are arranged, so that H is 0 Indicating "no target signal present", H 1 Indicating "target signal present", D 0 Indicating "decision as target", D 1 Indicating "decision as no target" for H 0 :z(t)、H 1 Z (t) is used for carrying out statistical test to judge which hypothesis is established, z (t) is an observation signal, and H is compared 0 And H 1 The magnitude of the probability of occurrence, i.e. the comparison of the posterior probability P (H 0 Z) and P (H) 1 I z), which probability is large decides which is true, expressed by a decision formula as P (H) 1 |z)＞P(H 0 Z) is established, it is determined as H 1 ，P(H 1 |z)＜P(H 0 Z) is established, it is determined as H 0 The posterior probability can be expressed as follows, according to the bayesian formula:wherein, P (z) is the probability density of z, and P (zH) 0 )、P(zH 1 ) Is a conditional probability density, when->When established, judge as H 1 The method comprises the steps of carrying out a first treatment on the surface of the When (when)When established, judge as H 0 ；P(H 0 )、P(H 1 ) Respectively H 0 Hypothesis sum H 1 The a priori probabilities of the hypotheses, generally known as a priori knowledge,/, are assumed>Is a decision threshold;
the binary detection problem decision makes four cases that describe the performance of the binary detection device, which can be expressed in terms of conditional probabilities as follows:
(1) Set H 0 Assuming true, the decision is D 0 Represents selection H 0 For true correct decisions, the conditional probability P (D 0 |H 0 ) The representation is made of a combination of a first and a second color,
(2) Set H 1 Assuming true, the decision is D 1 Represents selection H 1 For true correct decisions, the conditional probability P (D 1 |H 1 ) The representation is made of a combination of a first and a second color,in signal detection, the signal is the target signal and the target is judged, also called detection probability, P is used d The representation is made of a combination of a first and a second color,
(3) Set H 0 Assuming true, the decision is D 1 Represents selection H 0 For false first type erroneous decisions, the conditional probability P (D 1 |H 1 ) The representation is made of a combination of a first and a second color,in the signal detection, no target signal is judged to be a target, also called false alarm probability, P is used f The representation is made of a combination of a first and a second color,
(4) Set H 1 Assuming true, the decision is D 0 Represents selection H 1 For false decisions of the second type, the conditional probability P (D 0 |H 1 ) The representation is made of a combination of a first and a second color,in signal detection, the signal is the target signal and the signal is judged to be the target-free signal, which is also called as the probability of alarm leakage, P is used m A representation;
according to the Bayes formula and the full probability formula, the corresponding posterior probability can be obtained:
from the basic principle of the information theory, it is known that H (X) represents the degree of loss of information amount caused by false alarm and missed alarm, the greater the false alarm and missed alarm probability, the greater the loss of information,
the information acquisition integrity model is as follows
In the middle of
H max (X) -entropy at maximum uncertainty, reached when both false alarm probability and false alarm probability are 0.5;
H min (X) -entropy with minimum uncertainty, reached when both false alarm probability and false alarm probability are 0;
the result of the enemy disturbance reduces my correct decision P (D 0 |H 0 ) And P (D) 1 |H 1 ) (probability of detection) thereby increasing H (X), Q Scomp (X) decrease.
The information accuracy model building steps are as follows:
setting one-dimensional random variable at [ -delta, delta]The interval obeys the uniform distribution of equal probability, delta is the maximum range of uncertainty of random variables, is generally known as priori knowledge, and the probability density function isIts information entropy is->If the one-dimensional continuous random variable is subjected to normal distribution, the probability density function is +.>Its entropy is
Then the difference of the information entropy of the two is the reduction degree of the uncertainty rangeGeneralizing this conclusion to the N-dimensional case, N-dimensional continuous random vector x= (X) 1 ,x 2 ,…,x n ) T Is defined as the joint entropy of (2)If the N-dimensional continuous random vector X obeys a normal distribution, its probability density function is
Wherein μ= [ μ ] 1 ,μ 2 ,…,μ N ]Is the mean and has covariance matrix
The non-diagonal elements being random variables x i And x j Is formed by sigma i,j ＝(x i -μ i )(x j -μ j ) Obtained, random variable x i And x j The correlation of (2) is expressed asSigma when i=j i,j The variance of the covariance matrix;
the joint entropy of the N-dimensional continuous random vector X is
Where Sigma is the modulus of the determinant of the covariance matrix Sigma, and since n is a constant, H (X) is simplified to obtain the relative entropy H r (X) =log|Σ|, i.e. only with respect to covariance,
for a normal distribution of multiple variables, it is first assumed that the maximum joint entropy exists, i.e. that a uniform distribution is achieved
H max (X)＝log|∑| max
Definition Q Sacc (X) is interval [0,1 ]]A value in between, and has
The information entropy can be used to obtain a representation Q of the accuracy of information acquisition Sacc (X)，0≤Q Sacc (X). Ltoreq.1 reflects the accuracy of information acquisition, i.e. for the information element { a } 1 ,a 2 ,…,a C The values and the degree of mastery of the relationship between them are determined as Q Sacc (X) →1, represents the highest accuracy, and Q Sacc (X) →0 means that the accuracy is the lowest.
The information aging model is established as follows: the degree of freshness of the obtained information described by the time of the information can be expressed as
t i Indicating the current time, i.e. the time when the combat unit puts forward the information demand, t l Refers to the latest update time of the information, t 0 The time when the information actually starts to exist is defined as η, which is a coefficient related to the importance of the information.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (2)

1. The method for evaluating the efficiency of the military reconnaissance system is characterized by comprising the steps of establishing an efficiency evaluation model based on an information theory, wherein the establishment of the efficiency evaluation model comprises the establishment of an information integrity model, an information accuracy model and an information timeliness model, and the efficiency evaluation model consists of an accuracy Q Sacc Integrity Q Scomp And the ageing degree Q Scurr Composition, overall efficacy E, can be expressed as: e=w Sacc Q Sacc +W Scomp Q Scomp +W Scurr Q Scurr Wherein W is Sacc 、W Scomp And W is Scurr Accuracy Q respectively Sacc Integrity Q Scomp And the ageing degree Q Scurr Weight of (2);
the information integrity model establishment comprises a target detection process, wherein n signals to be detected are arranged, so that H is 0 Indicating "no target signal present", H 1 Indicating "target signal present", D 0 Indicating "decision as target", D 1 Indicating "decision as no target" for H 0 :z(t)、H 1 Z (t) is used for carrying out statistical test to judge which hypothesis is established, z (t) is an observation signal, and H is compared 0 And H 1 The magnitude of the probability of occurrence, i.e. the comparison of the posterior probability P (H 0 Z) and P (H) 1 I z), which probability is large decides which is true, expressed by a decision formula as P (H) 1 |z)＞P(H 0 Z) is established, it is determined as H 1 ，P(H 1 |z)＜P(H 0 Z) is established, it is determined as H 0 According to the She Beisi formula, the posterior probability can be expressed as:wherein, P (z) is the probability density of z, and P (z|H 0 )、P(z|H 1 ) Is a conditional probability density, when->When established, judge as H 1 The method comprises the steps of carrying out a first treatment on the surface of the When (when)When established, judge as H 0 ；P(H 0 )、P(H 1 ) Respectively H 0 Hypothesis sum H 1 The a priori probabilities of the hypotheses, generally known as a priori knowledge,/, are assumed>Is a decision threshold;
the binary detection problem decision makes four cases that describe the performance of the binary detection device, which can be expressed in terms of conditional probabilities as follows:
(1) Set H 0 Assuming true, the decision is D 0 Represents selection H 0 For true correct decisions, the conditional probability P (D 0 |H 0 ) The representation is made of a combination of a first and a second color,
(2) Set H 1 Assuming true, the decision is D 1 Represents selection H 1 For true correct decisions, the conditional probability P (D 1 |H 1 ) The representation is made of a combination of a first and a second color,in signal detection, the signal is the target signal and the target is judged, also called detection probability, P is used d The representation is made of a combination of a first and a second color,
(3) Set H 0 Assuming true, the decision is D 1 Represents selection H 0 For false first type erroneous decisions, the conditional probability P (D 1 |H 1 ) The representation is made of a combination of a first and a second color,in the signal detection, no target signal is judged to be a target, also called false alarm probability, P is used f The representation is made of a combination of a first and a second color,
(4) Set H 1 Assuming true, the decision is D 0 Represents selection H 1 For false decisions of the second type, the conditional probability P (D 0 |H 1 ) The representation is made of a combination of a first and a second color,in signal detection, the signal is the target signal and the signal is judged to be the target-free signal, which is also called as the probability of alarm leakage, P is used m A representation;
according to the Bayes formula and the full probability formula, the corresponding posterior probability can be obtained:
from the basic principle of the information theory, it is known that H (X) represents the degree of loss of information amount caused by false alarm and missed alarm, the greater the false alarm and missed alarm probability, the greater the loss of information,
the information acquisition integrity model is as follows
In the middle of
H max (X) -entropy at maximum uncertainty, reached when both false alarm probability and false alarm probability are 0.5;
H min (X) -entropy with minimum uncertainty, reached when both false alarm probability and false alarm probability are 0;
the result of the enemy disturbance reduces my correct decision P (D 0 |H 0 ) And P (D) 1 |H 1 ) (probability of detection) thereby increasing H (X), Q Scomp (X) reduction;
the information accuracy model building steps are as follows:
setting one-dimensional random variable at [ -delta, delta]The interval obeys the uniform distribution of equal probability, delta is the maximum range of uncertainty of random variables, is generally known as priori knowledge, and the probability density function isIts entropy isIf the one-dimensional continuous random variable is subjected to normal distribution, the probability density function is thatIts entropy is
Then the difference of the information entropy of the two is the reduction degree of the uncertainty rangeGeneralizing this conclusion to the N-dimensional case, N-dimensional continuous random vector x= (X) 1 ,x 2 ,…,x n ) T Is defined as the joint entropy of (2)If the N-dimensional continuous random vector X obeys a normal distribution, its probability density function is
Wherein μ= [ μ ] 1 ,μ 2 ,…,μ N ]Is the mean and has covariance matrix
The non-diagonal elements being random variables x i And x j Is formed by sigma i,j ＝(x i -μ i )(x j -μ j ) Obtained, random variable x i And x j The correlation of (2) is expressed asSigma when i=j i,j The variance of the covariance matrix;
the joint entropy of the N-dimensional continuous random vector X is
Where Sigma is the modulus of the determinant of the covariance matrix Sigma, and since n is a constant, H (X) is simplified to obtain the relative entropy H r (X) =log|Σ|, i.e. only with respect to covariance,
for a normal distribution of multiple variables, it is first assumed that the maximum joint entropy exists, i.e. that a uniform distribution is achieved
H max (X)＝log|∑| max
Definition Q Sacc (X) is interval [0,1 ]]A value in between, and has
The information entropy can be used to obtain a representation Q of the accuracy of information acquisition Sacc (X)，0≤Q Sacc (X). Ltoreq.1 reflects the accuracy of information acquisition, i.e. for the information element { a } 1 ,a 2 ,…,a C The values and the degree of mastery of the relationship between them are determined as Q Sacc (X) →1, represents the highest accuracy, and Q Sacc (X) →0 represents the lowest accuracy;
the information aging model is established as follows: the degree of freshness of the obtained information described by the time of the information can be expressed as
t i Indicating the current time, i.e. the time when the combat unit puts forward the information demand, t l Refers to the latest update time of the information, t 0 The time when the information actually starts to exist is defined as η, which is a coefficient related to the importance of the information.
2. The method for evaluating the performance of a military reconnaissance system according to claim 1, wherein: the information theory is a state description of a system or an event or a message related to the state, and M= { x 1 ,x 2 ,…,x n The system X is a set of all possible states, p= { P 1 ,p 2 ,…,p n The probability of occurrence of each state is set, and according to the concept of shannon entropy, if the probability of occurrence of a state of a system or event can be represented mathematically, the entropy of information describing the system is:
H(x)＝-∑xlog[p(x)]
if the system state set is a continuous interval [ a, b ], and there is a probability distribution density function f (x), then the information entropy is:
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