CN110991386A - Robust nearest neighbor radar target one-dimensional range profile identification method and system - Google Patents

Robust nearest neighbor radar target one-dimensional range profile identification method and system Download PDF

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CN110991386A
CN110991386A CN201911286732.0A CN201911286732A CN110991386A CN 110991386 A CN110991386 A CN 110991386A CN 201911286732 A CN201911286732 A CN 201911286732A CN 110991386 A CN110991386 A CN 110991386A
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郝志刚
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Changsha Core Lianxin Intelligent System Co Ltd
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Abstract

The application relates to a robust nearest neighbor radar target one-dimensional range profile identification method and system. The method comprises the following steps: the method comprises the steps of obtaining mixed Gaussian distribution of training samples of various labels in a one-dimensional range profile training sample set, setting soft attribute labels of the training samples in the one-dimensional range profile training sample set according to the mixed Gaussian distribution, obtaining a feature vector of a one-dimensional range profile sample to be tested, obtaining a plurality of training samples with the nearest distance according to the feature vector as neighbor samples, obtaining distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples, conducting discount processing on the soft attribute labels corresponding to the neighbor samples according to an evidence discount rule and the distance measurement, fusing the soft attribute labels corresponding to the neighbor samples after discount processing according to an evidence combination rule to obtain quantitative trust measurement of the one-dimensional range profile sample to be tested, and identifying the one-dimensional range profile to be tested according to the quantitative trust measurement. By adopting the method, the problem of robustness of robust nearest neighbor radar target one-dimensional range profile identification can be solved.

Description

Robust nearest neighbor radar target one-dimensional range profile identification method and system
Technical Field
The application relates to the technical field of radar target identification, in particular to a robust nearest neighbor radar target one-dimensional range profile identification method and system.
Background
With the continuous expansion of the broadband radar technology to the civil field, the identification requirement based on the radar target one-dimensional range profile is more and more strong. The nearest neighbor method is the most classical and least primitive method in the target recognition direction, and is widely applied to engineering. Like other target identification methods, the nearest neighbor method also needs support of a large number of training sample data sets to classify and identify the test samples, and in the specific implementation process, a plurality of nearest neighbor training samples of the test samples are searched in a feature space, and decision judgment is made according to attribute information of the nearest neighbor samples.
However, because the one-dimensional range profile of the broadband radar target has the characteristics of amplitude, translation, attitude sensitivity and the like, and the presence of intentional or unintentional interference is added, the quality of a large amount of collected training sample data is different, and if the quality difference of the training sample is ignored, the decision result of the nearest neighbor method is possibly induced to influence the robustness of the one-dimensional range profile identification of the robust nearest neighbor radar target.
Disclosure of Invention
In view of the above, it is necessary to provide a robust nearest neighbor radar target one-dimensional range profile identification method and system capable of solving the robustness problem affecting the robust nearest neighbor radar target one-dimensional range profile identification.
A robust nearest neighbor radar target one-dimensional range profile identification method comprises the following steps:
acquiring mixed Gaussian distribution of training samples of various class labels in a one-dimensional range profile training sample set;
setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
acquiring a feature vector of a one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the nearest distance as neighbor samples according to the feature vector, and acquiring distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples;
according to an evidence discount rule and the distance measurement, carrying out discount processing on the soft attribute label corresponding to the adjacent sample;
fusing the soft attribute labels corresponding to the discounted neighbor samples according to an evidence combination rule to obtain a quantitative trust measurement of the one-dimensional distance image sample to be tested;
and identifying the one-dimensional distance image to be tested according to the quantitative trust measurement.
In one embodiment, the method further comprises the following steps: by adopting an expectation maximization method, clustering analysis is carried out on training samples of different types of labels, and the mixed Gaussian distribution of the training samples of all types of labels in the one-dimensional range profile training sample set is obtained as follows:
Figure BDA0002318194640000021
wherein the content of the first and second substances,
Figure BDA0002318194640000022
Figure BDA0002318194640000023
represents the s-th cluster center corresponding to the l-th class training sample,
Figure BDA0002318194640000024
a covariance matrix, N, representing the correspondence of the cluster centerlRepresents the number of cluster centers within a cluster, N (. | m, P) represents a Gaussian distribution with mean m and covariance P.
In one embodiment, the method further comprises the following steps: according to generalized Bayes' theorem, setting the soft attribute label of each training sample in the one-dimensional range profile training sample set as
Figure RE-GDA0002386902430000025
Wherein the content of the first and second substances,
Figure RE-GDA0002386902430000026
Dxrepresents the dimension of vector x, { c1，c2，…，cLIndicates a category label.
In one embodiment, the method further comprises the following steps: normalizing the soft attribute label to obtain the normalized expression of the soft attribute label as follows:
Figure BDA0002318194640000026
in one embodiment, the method further comprises the following steps: acquiring a characteristic vector x of a one-dimensional range profile sample to be tested, and acquiring K training samples with the nearest Euclidean distance according to the characteristic vector x as neighbor samples x(k)；
Obtaining the distance measurement x between the one-dimensional range profile sample x to be tested and the neighboring sample(k)The distance metric of (a) is:
Figure BDA0002318194640000027
wherein, the one-dimensional range profile sample x is tested to be away from the neighbor sample x(k)Is d (x, x)(k))≥0。
In one embodiment, the method further comprises the following steps: according to the evidence discount rule and the distance measurement, the discount processing process of the soft attribute label corresponding to the adjacent sample is as follows:
Figure BDA0002318194640000031
in one embodiment, the method further comprises the following steps: according to an evidence combination rule, fusing soft attribute labels corresponding to the discounted neighbor samples to obtain a one-dimensional distance image sample quantitative trust metric mx (·) to be tested;
wherein the content of the first and second substances,
Figure BDA0002318194640000032
indicating the size of the collision between different neighboring samples.
In one embodiment, the method further comprises the following steps: and carrying out hard decision on the quantitative trust metric according to a betting probability decision rule in a trust function theory as follows:
Figure BDA0002318194640000033
where | represents the number of collection elements.
A robust nearest-neighbor radar target one-dimensional range profile recognition system, the system comprising:
the label setting module is used for acquiring the mixed Gaussian distribution of the training samples of various types of labels in the one-dimensional range profile training sample set; setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
the distance acquisition module is used for acquiring a feature vector of a one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the closest distance according to the feature vector to serve as neighbor samples, and acquiring the distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples;
the trust measurement module is used for carrying out discount processing on the soft attribute label corresponding to the neighbor sample according to an evidence discount rule and the distance measurement; fusing the soft attribute labels corresponding to the near samples after discount processing according to an evidence combination rule to obtain a quantitative trust measurement of the one-dimensional distance image sample to be tested;
and the identification module is used for identifying the one-dimensional distance image to be tested according to the quantitative trust measurement.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring mixed Gaussian distribution of training samples of various class labels in a one-dimensional range profile training sample set;
setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
acquiring a feature vector of a one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the nearest distance as neighbor samples according to the feature vector, and acquiring distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples;
according to an evidence discount rule and the distance measurement, carrying out discount processing on the soft attribute label corresponding to the adjacent sample;
fusing the soft attribute labels corresponding to the discounted neighbor samples according to an evidence combination rule to obtain a quantitative trust measurement of the one-dimensional distance image sample to be tested;
and identifying the one-dimensional distance image to be tested according to the quantitative trust measurement.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring mixed Gaussian distribution of training samples of various class labels in a one-dimensional range profile training sample set;
setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
acquiring a feature vector of a one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the nearest distance as neighbor samples according to the feature vector, and acquiring distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples;
according to an evidence discount rule and the distance measurement, carrying out discount processing on the soft attribute label corresponding to the adjacent sample;
fusing the soft attribute labels corresponding to the discounted neighbor samples according to an evidence combination rule to obtain a quantitative trust measurement of the one-dimensional distance image sample to be tested;
and identifying the one-dimensional distance image to be tested according to the quantitative trust measurement.
According to the robust nearest neighbor radar target one-dimensional range profile identification method, the robust nearest neighbor radar target one-dimensional range profile identification system, the computer equipment and the storage medium, the advantages of the trust function theory in the aspects of uncertain information representation and reasoning are fully utilized, the uncertain attributes of training samples with different qualities are effectively represented, and the robust reasoning of the uncertain information is completed in the subsequent nearest neighbor classification identification. In addition, different methods can be adopted for Gaussian mixture clustering, distance measurement, evidence discount processing, evidence combination rules, decision rules and the like related to each step, and the method does not depend on a specific implementation approach and has wide applicability.
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FIG. 1 is a schematic flow chart of a robust nearest neighbor radar target one-dimensional range profile identification method in one embodiment;
FIG. 2 is a block diagram of a robust nearest-neighbor radar target one-dimensional range profile identification system in an embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a robust one-dimensional range profile identification method for a nearest neighbor radar target is provided, which can be applied in a terminal, and includes the following steps:
and 102, acquiring mixed Gaussian distribution of the training samples of all class labels in the one-dimensional distance image training sample set.
The one-dimensional range image is the vector sum of the projection of a target scattering point sub-echo acquired by a broadband radar signal on a radar ray, and not only provides the geometric shape and the structural characteristics of a target, but also contains more relevant information required by target identification. The one-dimensional range profile is actually a scatter intensity profile for each range bin on the target.
The training sample set comprises a plurality of training samples, each training sample is classified under a class label, and the class labels are the belonged classes of different radar targets.
The Gaussian mixture clustering algorithm of the methods such as expected maximization, probability distribution and the like can be adopted to obtain the Gaussian mixture distribution.
And 104, setting soft attribute labels of all training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution.
The soft attribute labels are used for representing the uncertainty of the class attributes of the training samples, so that the training samples with different qualities are represented, and the soft attribute labels of the training samples can be determined by specifically adopting the generalized Bayes' theorem in the belief function theory to analyze.
And 106, acquiring a feature vector of the one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the nearest distance according to the feature vector to serve as neighbor samples, and acquiring the distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples.
The distance measurement may be implemented by euclidean distance and mahalanobis distance, which is not limited herein.
And step 108, performing discount processing on the soft attribute labels corresponding to the neighboring samples according to the evidence discount rule and the distance measurement.
The evidence discount rule is based on evidence theory and is a correction means before evidence combination. In this step, the soft attribute labels of the neighboring samples are corrected.
In this step, the evidence discount rule may also be evidence discount processing based on classical, context, etc.
And step 110, fusing the soft attribute labels corresponding to the discounted neighbor samples according to an evidence combination rule to obtain the quantitative trust measurement of the one-dimensional distance image sample to be tested.
And fusing the soft attribute labels to obtain the quantitative trust metric of the one-dimensional distance image sample to be tested. It is worth mentioning that the Evidence combination rules and Evidence discount rules both come from the document Shafer G.A mathematical theory of Evidence [ M ], Princeton: Princeton University Press, 1976.
The evidence combination rule can also be based on local and global conflict proportion distribution and other evidence combination rules.
And 112, identifying the one-dimensional range profile to be tested according to the quantitative trust measurement.
In making the decision, a decision rule based on trust, betting probability and the like can also be adopted.
In the robust nearest neighbor radar target one-dimensional range profile identification method, the advantages of the trust function theory in the aspects of uncertainty information characterization and reasoning are fully utilized, the effective characterization of uncertainty attributes of training samples with different qualities is realized, and the robust reasoning of uncertainty information is completed in the subsequent nearest neighbor classification identification. In addition, different methods can be adopted for Gaussian mixture clustering, distance measurement, evidence discount processing, evidence combination rules, decision rules and the like related to each step, and the method is independent of a specific implementation approach and has wide applicability.
In one embodiment, the specific step of calculating the mixed gaussian distribution includes: by adopting an expected maximization method, clustering analysis is carried out on training samples of different types of labels, and the mixed Gaussian distribution of the training samples of all types of labels in the one-dimensional distance image training sample set is obtained as follows:
Figure BDA0002318194640000071
wherein the content of the first and second substances,
Figure BDA0002318194640000072
Figure BDA0002318194640000073
represents the s-th cluster center corresponding to the l-th class training sample,
Figure BDA0002318194640000074
a covariance matrix, N, representing the correspondence of the cluster centerlRepresents the number of cluster centers within a cluster, N (. | m, P) represents a Gaussian distribution with mean m and covariance P.
In another embodiment, the step of setting the soft attribute tag comprises: according to generalized Bayes' theorem, setting the soft attribute label of each training sample in the one-dimensional range profile training sample set as
Figure BDA0002318194640000075
Wherein the content of the first and second substances,
Figure BDA0002318194640000076
Dxrepresents the dimension of vector x, { c1,c2,…,cLIndicates a category label.
In addition, soft attribute labels are required to be normalized to obtain
Figure BDA0002318194640000077
In one embodiment, the acquiring of the neighbor samples and the distance measurement are required, which specifically includes: acquiring a characteristic vector x of a one-dimensional range profile sample to be tested, and acquiring K training samples with the nearest Euclidean distance according to the characteristic vector x as neighbor samples x(k)(ii) a Obtaining a distance measurement x between the one-dimensional range profile sample x to be tested and the adjacent sample(k)The distance metric of (d) is:
Figure BDA0002318194640000078
wherein, the one-dimensional range profile sample x is tested to be away from the neighbor sample x(k)Is d (x, x)(k)) Is more than or equal to 0. Specifically, x and x(k)Are all vector representations.
In one embodiment, the step of performing a discount process includes: according to the evidence discount rule and the distance measurement, the discount processing process of the soft attribute label corresponding to the adjacent sample is as follows:
Figure BDA0002318194640000081
in one embodiment, the step of performing label fusion comprises: according to an evidence combination rule, fusing soft attribute labels corresponding to the discounted neighbor samples to obtain a one-dimensional distance image sample quantitative trust metric mx (·) to be tested;
wherein the content of the first and second substances,
Figure BDA0002318194640000082
indicating the size of the collision between different neighboring samples.
In one embodiment, the hard decision of the quantized trust metric according to the betting probability decision rule in the trust function theory is as follows:
Figure BDA0002318194640000083
where | represents the number of collection elements.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in a strict order unless explicitly stated herein, and may be performed in other orders. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, a robust nearest neighbor radar target one-dimensional range profile recognition system is provided, comprising: a tag setting module 202, a distance acquisition module 204, a trust metric module 206, and an identification module 208, wherein:
the label setting module 202 is configured to obtain mixed gaussian distribution of training samples of each class label in the one-dimensional range profile training sample set; setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
a distance obtaining module 204, configured to obtain a feature vector of a one-dimensional range profile sample to be tested, obtain, according to the feature vector, a plurality of training samples closest to each other as neighbor samples, and obtain a distance metric between the one-dimensional range profile sample to be tested and the neighbor samples;
a trust measurement module 206, configured to perform discount processing on the soft attribute tag corresponding to the neighboring sample according to an evidence discount rule and the distance measurement; according to an evidence combination rule, fusing soft attribute labels corresponding to the discounted neighbor samples to obtain a quantitative trust metric of the one-dimensional distance image sample to be tested;
and the identification module 208 is configured to identify the to-be-tested one-dimensional distance image according to the quantitative trust metric.
In one embodiment, the label setting module 202 is further configured to perform cluster analysis on training samples of different types of labels by using an expectation maximization method, and obtain a gaussian mixture distribution of the training samples of each type of label in the one-dimensional range profile training sample set as follows:
Figure BDA0002318194640000091
wherein the content of the first and second substances,
Figure BDA0002318194640000092
Figure BDA0002318194640000093
represents the s-th cluster center corresponding to the l-th class training sample,
Figure BDA0002318194640000094
a covariance matrix, N, representing the correspondence of the cluster centerlRepresents the number of cluster centers within a cluster, N (. | m, P) represents a Gaussian distribution with mean m and covariance P.
In one embodiment, the label setting module 202 is further configured to set the soft attribute label of each training sample in the one-dimensional distance image training sample set to be soft attribute label according to the generalized bayesian theorem by using the gaussian mixture distribution
Figure BDA0002318194640000095
Wherein the content of the first and second substances,
Figure BDA0002318194640000096
Dxrepresents the dimension of vector x, { c1,c2,…,cLIndicates a category label.
In one embodiment, the label setting module 202 is further configured to normalize the soft attribute label, and obtain a normalized representation of the soft attribute label as:
Figure BDA0002318194640000101
in one embodiment, the distance obtaining module 204 is further configured to obtain a feature vector x of the one-dimensional distance image sample to be tested, and obtain K training samples with the closest euclidean distance according to the feature vector x as neighbor samples x(k)(ii) a Obtaining the distance measurement x between the one-dimensional range profile sample x to be tested and the neighboring sample(k)The distance metric of (d) is:
Figure BDA0002318194640000102
wherein, the one-dimensional range profile sample x is tested to be away from the neighbor sample x(k)Is d (x, x)(k))≥0。
In one embodiment, the trust metric module 206 is further configured to perform a discount process on the soft attribute labels corresponding to the neighbor samples according to the evidence discount rule and the distance metric as follows:
Figure BDA0002318194640000103
in one embodiment, the trust metric module 206 is further configured to fuse the soft attribute labels corresponding to the discount-processed neighbor samples according to an evidence combination rule to obtain a one-dimensional distance image sample quantization trust metric mx (·) to be tested;
wherein the content of the first and second substances,
Figure BDA0002318194640000104
indicating the size of the collision between different neighboring samples.
In one embodiment, the identification module 208 is further configured to make a hard decision on the quantized trust metric according to a betting probability decision rule in trust function theory as follows:
Figure BDA0002318194640000105
where | represents the number of collection elements.
For specific limitations of the robust nearest-neighbor radar target one-dimensional range profile identification system, reference may be made to the above limitations of the robust nearest-neighbor radar target one-dimensional range profile identification method, and details are not repeated here. The modules in the system for identifying one-dimensional range profile of the target of the robust nearest neighbor radar can be fully or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a robust nearest neighbor radar target one-dimensional range profile identification method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps in the above-described method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.

Claims (10)

1. A robust nearest neighbor radar target one-dimensional range profile identification method comprises the following steps:
acquiring mixed Gaussian distribution of training samples of various class labels in a one-dimensional range profile training sample set;
setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
acquiring a feature vector of a one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the nearest distance as neighbor samples according to the feature vector, and acquiring distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples;
according to an evidence discount rule and the distance measurement, carrying out discount processing on the soft attribute label corresponding to the adjacent sample;
fusing the soft attribute labels corresponding to the discounted neighbor samples according to an evidence combination rule to obtain a quantitative trust measurement of the one-dimensional distance image sample to be tested;
and identifying the one-dimensional distance image to be tested according to the quantitative trust measurement.
2. The method of claim 1, wherein obtaining a mixture gaussian distribution of training samples for each class label in a one-dimensional range profile training sample set comprises:
by adopting an expectation maximization method, clustering analysis is carried out on training samples of different types of labels, and the mixed Gaussian distribution of the training samples of all types of labels in the one-dimensional range profile training sample set is obtained as follows:
Figure FDA0002318194630000011
wherein the content of the first and second substances,
Figure FDA0002318194630000012
Figure FDA0002318194630000013
represents the s-th cluster center corresponding to the l-th class training sample,
Figure FDA0002318194630000014
a covariance matrix, N, representing the correspondence of the cluster centerlRepresents the number of cluster centers within a cluster, N (. | m, P) represents a Gaussian distribution with mean m and covariance P.
3. The method of claim 2, wherein setting a soft attribute label for each of the training samples in the one-dimensional range profile training sample set according to the Gaussian mixture distribution comprises:
according to generalized Bayes' theorem, setting the soft attribute label of each training sample in the one-dimensional range profile training sample set as
Figure FDA0002318194630000015
Wherein the content of the first and second substances,
Figure FDA0002318194630000021
Dxrepresents the dimension of vector x, { c1,c2,…,cLIndicates a category label.
4. The method of claim 3, further comprising:
normalizing the soft attribute label to obtain the normalized expression of the soft attribute label as follows:
Figure FDA0002318194630000022
5. the method according to claim 2, wherein the obtaining a feature vector of the one-dimensional range profile sample to be tested, obtaining a plurality of the training samples closest to each other as neighbor samples according to the feature vector, and obtaining a distance measure between the one-dimensional range profile sample to be tested and the neighbor samples comprises:
acquiring a characteristic vector x of a one-dimensional range profile sample to be tested, and acquiring K training samples with the nearest Euclidean distance according to the characteristic vector x as neighbor samples x(k)；
Obtaining the distance measurement x between the one-dimensional range profile sample x to be tested and the neighboring sample(k)The distance metric of (d) is:
Figure FDA0002318194630000023
wherein, the one-dimensional range profile sample x is tested to be away from the neighbor sample x(k)Is d (x, x)(k))≥0。
6. The method of claim 5, wherein discounting the soft attribute labels corresponding to the neighboring samples according to an evidence discount rule and the distance metric comprises:
according to the evidence discount rule and the distance measurement, the discount processing process of the soft attribute label corresponding to the neighboring sample is as follows:
Figure FDA0002318194630000024
7. the method according to claim 6, wherein fusing the soft attribute labels corresponding to the discounted neighboring samples according to an evidence combination rule to obtain a quantitative confidence measure of the one-dimensional distance image sample to be tested, comprises:
according to an evidence combination rule, fusing soft attribute labels corresponding to the discounted neighbor samples to obtain a one-dimensional distance image sample quantitative trust metric mx (·) to be tested;
wherein the content of the first and second substances,
Figure FDA0002318194630000031
indicating the size of the collision between different neighboring samples.
8. The method of claim 7, wherein identifying the one-dimensional range profile to be tested according to the quantitative confidence metric comprises:
and carrying out hard decision on the quantitative trust metric according to a betting probability decision rule in a trust function theory as follows:
Figure FDA0002318194630000032
where | represents the number of collection elements.
9. A robust nearest-neighbor radar target one-dimensional range profile recognition system, the system comprising:
the label setting module is used for acquiring the mixed Gaussian distribution of the training samples of each class label in the one-dimensional range profile training sample set; setting soft attribute labels of the training samples in the one-dimensional distance image training sample set according to the mixed Gaussian distribution;
the distance acquisition module is used for acquiring a feature vector of a one-dimensional range profile sample to be tested, acquiring a plurality of training samples with the nearest distance as neighbor samples according to the feature vector, and acquiring the distance measurement between the one-dimensional range profile sample to be tested and the neighbor samples;
the trust measurement module is used for carrying out discount processing on the soft attribute label corresponding to the neighbor sample according to an evidence discount rule and the distance measurement; fusing the soft attribute labels corresponding to the discounted neighbor samples according to an evidence combination rule to obtain a quantitative trust measurement of the one-dimensional distance image sample to be tested;
and the identification module is used for identifying the one-dimensional distance image to be tested according to the quantitative trust measurement.
10. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 8 when executing the computer program.
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