CN114494819A - Anti-interference infrared target identification method based on dynamic Bayesian network - Google Patents
Anti-interference infrared target identification method based on dynamic Bayesian network Download PDFInfo
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Abstract
The invention relates to an anti-interference infrared target identification method based on a dynamic Bayesian network, which is used for constructing and updating Bayesian feature vectors. The Bayesian feature vector is mainly formed by combining the radiation intensity of the target and the interference, the appearance contour and other feature abstract representations, so that the characteristic definition of the target and the interference is realized, and a foundation is laid for accurate identification. Feature vector timing association and matching. And on the basis of the completion of the construction of the feature vector, constructing a static Bayesian network by taking the features as a core, and describing the association among the features. Meanwhile, a dynamic Bayesian network framework is constructed by taking the static Bayesian network as an initial network and aiming at the same node characteristics at different moments. And reasoning and identifying under the Bayesian network. In the interference projection process, the target and the interference characteristics are continuously changed, and the characteristic difference between the front moment and the rear moment is small. On the basis of the construction of the current dynamic Bayesian network, target information is identified through characteristic time sequence variation reasoning, so that the identification precision is improved, and the architecture design of the target identification method is completed.
Description
Technical Field
The invention belongs to the field of information processing, and relates to an anti-interference infrared target identification method based on a dynamic Bayesian network.
Background
With the development of modern air combat weaponry, the air combat environment becomes increasingly complex. Only for the infrared air-to-air missile, the target interferes the missile guidance system by releasing artificial baits such as point sources and surface sources which are similar to the target characteristic information, so that the guidance system cannot make accurate and effective judgment. Therefore, the resistance to the artificial interference in the complex scene is the core problem faced by the current infrared air-to-air missile, and the anti-interference capability is of great significance to the infrared guidance system.
Most of the traditional aerial infrared target identification methods firstly extract features, use the extracted features as templates and then verify the extracted features through template matching. Typical features include shape features, local texture features, edge features, and the like. Firstly, people express target information through a single feature, later people fuse and process a plurality of features, and the method can comprehensively analyze and process image information. However, with the increasingly complex environmental background and the emergence of infrared interference, the traditional method is difficult to deal with the contemporary air battle environment.
The application requirements of actual air battlefield countermeasures cannot be met only by considering the traditional feature fusion method, and the time sequence correlation among multiple feature frames needs to be comprehensively considered. In the process of throwing the bait into the target, the bait gradually covers the target in a half or full way from the target position and then is separated. In the process, the real-time change of the features is accelerated, and the complex change relation between the features cannot be expressed by simple feature fusion. Therefore, a multi-feature inter-frame time-series related dynamic bayesian network was introduced as part of the classification system for identifying targets. The dynamic Bayesian network introduces a time dimension on the basis of static Bayes, the characteristics of the target on each time node are represented by a group of variables, the relation among the variables is used for expressing the law that the characteristics of the target change constantly along with the time, and the target is modeled in such a way, so that the stability of target identification is greatly improved.
Therefore, on the basis of deep research on the anti-interference target identification of the traditional infrared air-to-air missile, the anti-interference infrared target identification method based on the dynamic Bayesian network is provided, and the target identification and anti-interference tracking capabilities of the seeker are improved.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides an anti-interference infrared target identification method based on a dynamic Bayesian network, which is used for realizing stable target tracking in an infrared point source interference strong shielding environment.
Technical scheme
An anti-interference infrared target identification method based on a dynamic Bayesian network is characterized by comprising the following steps:
step 1: constructing a Bayesian feature vector of target interference:
step 2: constructing a static Bayesian network, which consists of a directed acyclic graph and a conditional probability table, wherein the directed acyclic graph qualitatively represents the dependency relationship among the variables, and the conditional probability table quantitatively describes the relationship strength among the variables; each node of the network corresponds to a characteristic variable, and a time slice comprises 17 characteristic nodes; according to the relationship between the characteristic nodes and the characteristics and the conditional probability table, the Bayesian network obtains the joint probability distribution of all the characteristic nodes;
establishing an anti-interference simulation data set of the infrared air-to-air missile, and respectively extracting positive and negative sample characteristics to form a characteristic vector Ai＝{X1,X2,X3,…,X17Forming a positive and negative sample library S+＝{A1,A2,A3,…,APAnd S-＝{A1,A2,A3,…,AQThe static bayesian network formed by 17 features is:
constructing a dynamic Bayesian network according to the time sequence correlation of the constructed static Bayesian network: the method comprises the steps that a static Bayesian network is used as an initial network, connection relations of various random variables among time slices are determined through directed arcs, and the initial network is expanded to a dynamic Bayesian network;
training an anti-interference recognition classifier by adopting a dynamic Bayesian network;
and step 3: and inputting the received infrared image into a classifier, and carrying out target identification on the infrared image by the classifier.
The static bayesian network structure of said step 2 is shown in fig. 3.
The dynamic bayesian network structure of step 2 is shown in fig. 4.
Advantageous effects
The invention provides an anti-interference infrared target identification method based on a dynamic Bayesian network, which is used for constructing and updating Bayesian feature vectors. The Bayesian feature vector is mainly formed by combining the radiation intensity of the target and the interference, the appearance outline and other feature abstract representations, so that the target and the interference are defined in a characteristic manner, and a foundation is laid for accurate identification. And (5) associating and matching the feature vector timing. And on the basis of the completion of the construction of the feature vector, constructing a static Bayesian network by taking the features as a core, and describing the association among the features. Meanwhile, a dynamic Bayesian network framework is constructed by taking the static Bayesian network as an initial network and aiming at the same node characteristics at different moments. And reasoning and identification under the Bayesian network. In the interference projection process, the target and the interference characteristics are continuously changed, and the characteristic difference between the front moment and the rear moment is small. On the basis of the construction of the current dynamic Bayesian network, target information is identified through characteristic time sequence variation reasoning, so that the identification precision is improved, and the architecture design of the target identification method is completed.
The invention has the advantages and beneficial effects that: therefore, the problem of identification of a strong shielding target is solved by constructing a dynamic Bayesian network based on the characteristic difference of target interference, the real-time requirement of a guidance system is met, the anti-interference capability of the infrared air-air missile is improved, and the accurate guidance and attack capability is effectively improved.
Drawings
FIG. 1: a flow chart of an anti-interference infrared target identification method based on a dynamic Bayesian network;
FIG. 2: an anti-interference infrared target identification method test chart based on a dynamic Bayesian network;
a: 160 ° left turn (# 52);
b: 160 ° left turn (# 225);
c: 160 ° left turn (# 446);
d: 160 ° left turn (# 545);
e: 100 degree jump (#114)
f: 100 degree jump (#377)
g: 100 degree jump (#609)
h: 100 degree jump (#720)
FIG. 3: static bayesian network structure diagram;
FIG. 4: dynamic bayesian network architecture.
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
according to the method, the target and the interference characteristic vector are constructed mainly through the dynamic Bayesian network, the position of the target under the strong shielding condition is predicted in real time by utilizing the characteristic difference, and the identification method based on the dynamic Bayesian network is obtained, so that the problem of accurate identification of the air combat target is solved. The invention is further described by the following specific implementation method with the attached drawings:
the method comprises the following specific steps:
the method comprises the following steps: through analysis of target interference characteristics, variables capable of distinguishing the target and the interference characteristics are selected to be combined to form a characteristic vector. Seventeen features of length-width ratio, perimeter, energy, area, circularity, gray average value, entropy, Fourier descriptor and the like are selected as feature vectors to represent the characteristics of distinguishing targets from interference, and specific description of the features is shown in table 1.
TABLE 1 characteristics of the process used
Step two: and after the characteristics representing the target and the interference are determined, constructing a static Bayesian network according to the selected characteristics. And then constructing a dynamic Bayesian network according to the time sequence correlation of the constructed static Bayesian network.
Construction of static Bayesian networks
The Bayesian network is composed of a directed acyclic graph and a conditional probability table, wherein the directed acyclic graph qualitatively shows the dependency relationship among variables, and the conditional probability table quantitatively describes the relationship strength among the variables.
In this context, each node of the network corresponds to a feature variable, and a time slice contains 17 feature nodes. According to the feature nodes, the relationship among the features and the conditional probability table, the Bayesian network can obtain the joint probability distribution of all the feature nodes.
Establishing a simulation data set, and respectively extracting positive and negative sample characteristics to form a characteristic vector Ai＝{X1,X2,X3,…,X17Forming a positive and negative sample library S+＝{A1,A2,A3,…,APAnd S-＝{A1,A2,A3,…,AQ}. The static Bayesian network formed by the 17 characteristics of the method is shown in FIG. 3.
Dynamic Bayesian network structure and parameter learning
The dynamic Bayesian network is generally composed of an initial network and a transfer network, the static Bayesian network is used as the initial network in the method, the directed arcs are used for determining how various random variables are related among time slices, the initial network is extended to the dynamic Bayesian network to model the classifier based on the anti-interference recognition, and the structure of the dynamic Bayesian network is shown in FIG. 4.
On the basis, a parameter learning process needs to be completed, and the method adopts a maximum likelihood estimation method.
Let us assume an arbitrary characteristic variable Xi(0. ltoreq. i. ltoreq.N) has riValue case is set, its parent node pa (X)i) The variable has qiIf the value is taken, the parameter vector of the characteristic variable is theta＝{θijk|i＝1,...,N；j＝1,...,ri；k＝1,...,qiTherein of
The probability sum of the values of any characteristic variable is 1. Will satisfy p (X)i|pa(Xi) K) is represented as θi,k＝{θijk|j＝1,...,ri}. The parameters to be estimated are:
θijk＝p(Xi＝j|pa(Xi)＝k)(i＝1,...,N；j＝1,...,ri；k＝1,...,qi)
from the log-likelihood estimation method, the following formula can be obtained, where NijkRepresents satisfying thetaijkThe number of samples.
According to the parameter learning process, each feature node X can be calculated respectively1,X2,X3,…,X17The conditional probability table of (2) is shown in table 3, and the conditional probability table is an area node as an example.
TABLE 2 Intra-chip conditional probability table of area feature nodes
TABLE 3 transition probability table for area-characterized nodes
Note: the parent node of the on-chip probabilistic surface area is the perimeter; the parent node of the inter-slice transition probability table area is the perimeter of the current time and the area of the previous time. The transition probability table of the area feature node is a three-dimensional probability table, and table 3 selects the transition probability table of the parent node corresponding to the fifth feature interval of the area at the current time.
Step three: after the dynamic Bayesian network is constructed, the parameters in the transfer network are not changed, so that the network is expanded to the Tth time slice according to the initial distribution and the conditional distribution between the adjacent time slices, and the identification of the target is realized.
Since the discrete dynamic bayesian network conforms to the conditional independence assumption, the probability distribution of the initial time of the dynamic bayesian network can be obtained as follows:
wherein, note
Is composed of
To represent
Is selected.
The conditional probability table between two adjacent time slice variables is distributed as follows, wherein,
for the ith node on the t-th time slice,
is composed of
The parent node of (2).
t > 0 represents the conditional probability distribution of each node in the next time slice. Node point
Of parent node
Within either of two time slices.
From the homogeneous assumption, a joint probability distribution across multiple time slices can be derived:
wherein T is the number of time slices, and m is the number of nodes in a single time slice. From this, the bayesian formula of the dynamic bayesian network is:
wherein the content of the first and second substances,
represents the degree to which the test data matches class C, and p (C) is the prior probability of class C.
For a set of eigenvalues
According to Table 2, Table 3Calculating the intra-chip conditional probability table and the inter-chip transition probability table of each attribute node
Prior probability P (C) of each classi) It can be calculated from training samples, i.e., P (C ═ 1) ═ 0.5334, and P (C ═ 0) ═ 0.4666. And then according to dynamic Bayesian structure calculation (Q is the category of the first time slice):
the same can be calculated:
by comparison
And
obtaining a sample
The dynamic bayesian network detection result. If it is
The sample belongs to the target and otherwise to the interference.
Therefore, the design flow of the target anti-interference identification method based on the dynamic bayesian network is shown in fig. 1:
1)Tipreprocessing and connected region marking are carried out on the frame image, the number of connected regions of the current frame is counted, and the characteristics of the connected regions are extracted to obtain a characteristic value set.
2) And respectively calculating the probability of each class of the characteristic value of each connected region according to the conditional probability table in the time slice and the transition probability table among the time slices obtained by training.
3) And comparing to obtain the probability of each type of the single connected region, wherein the type with the highest probability is the type of the connected region.
Examples effects of implementation
The invention can well construct an anti-interference infrared target identification method based on the dynamic Bayesian network, and can identify the target and the interference with high probability, thereby determining the position of the target, improving the anti-interference performance of the infrared air-to-air missile and realizing accurate attack.
The invention is tested based on the image sequence after background suppression, and the test result is shown in fig. 2.
Claims (3)
1. An anti-interference infrared target identification method based on a dynamic Bayesian network is characterized by comprising the following steps:
step 1: constructing a Bayesian feature vector of target interference:
step 2: constructing a static Bayesian network, which consists of a directed acyclic graph and a conditional probability table, wherein the directed acyclic graph qualitatively represents the dependency relationship among the variables, and the conditional probability table quantitatively describes the relationship strength among the variables; each node of the network corresponds to a characteristic variable, and a time slice comprises 17 characteristic nodes; according to the relationship between the characteristic nodes and the characteristics and the conditional probability table, the Bayesian network obtains the joint probability distribution of all the characteristic nodes;
establishing an anti-interference simulation data set of the infrared air-to-air missile, and respectively extracting positive and negative sample characteristics to form a characteristic vector Ai＝{X1,X2,X3,…,X17Forming a positive and negative sample library S+＝{A1,A2,A3,…,APAnd S-＝{A1,A2,A3,…,AQThe static bayesian network formed by 17 features is:
constructing a dynamic Bayesian network according to the time sequence correlation of the constructed static Bayesian network: the method comprises the steps that a static Bayesian network is used as an initial network, connection relations of various random variables among time slices are determined through directed arcs, and the initial network is expanded to a dynamic Bayesian network;
training an anti-interference recognition classifier by adopting a dynamic Bayesian network;
and step 3: and inputting the received infrared image into a classifier, and carrying out target identification on the infrared image by the classifier.
2. The method for recognizing the anti-interference infrared target based on the dynamic Bayesian network as recited in claim 1, wherein: the static Bayesian network structure of the step 2 is as follows:
a. the initial node is the length-width ratio, and the child nodes are circularity;
b. the circularity child nodes are perimeter, descriptor 1 and descriptor 2;
c. the perimeter sub-node is the area, the area sub-node is the energy, the energy sub-node is the average gray level, and the average gray level sub-node is the entropy;
d. the child node of the descriptor 1 is a descriptor 3, and the child nodes of the descriptor 3 are a descriptor 4 and a descriptor 5;
e. the descriptor 5 child nodes are a descriptor 6 and a descriptor 7;
f. the descriptor 6 child node is a descriptor 8, and the descriptor 8 child node is a descriptor 10;
g. the descriptor 7 child node is the descriptor 9.
3. The method for recognizing the anti-interference infrared target based on the dynamic Bayesian network as recited in claim 1, wherein: the dynamic Bayesian network structure of the step 2 is as follows:
the network structure in the first time slice and the second time slice is the same as the static Bayesian network structure, the network connection mode between the first time slice and the second time slice is corresponding feature connection, such as the aspect ratio feature in the first time slice is connected with the aspect ratio feature in the second time slice, the circularity feature in the first time slice is connected with the circularity feature in the second time slice, the descriptor feature in the first time slice is connected with the descriptor feature in the second time slice, the perimeter feature in the first time slice is connected with the perimeter feature in the second time slice, the area feature in the first time slice is connected with the area feature in the second time slice, the energy feature in the first time slice is connected with the energy feature in the second time slice, the average gray feature in the first time slice is connected with the average gray feature in the second time slice, and the entropy feature in the first time slice is connected with the entropy feature in the second time slice.
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