CN115932899A - Interference type identification method, device, terminal equipment and medium - Google Patents
Interference type identification method, device, terminal equipment and medium Download PDFInfo
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Abstract
The application is applicable to the technical field of wireless communication, and provides an interference type identification method, an interference type identification device, terminal equipment and a medium. The method comprises the steps of obtaining baseband digital signal data of navigation signal data to be identified; reducing the dimension of the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data; randomly extracting a low-dimensional baseband digital signal from low-dimensional baseband digital signal data to obtain a training sample set; constructing a decision tree based on each training sample, and constructing a random forest classification recognition model according to all the decision trees; taking the interference type with the largest ratio on each node of each decision tree as the interference type of the node, and determining the interference type classification result corresponding to the decision tree according to the interference types of all the nodes on the decision tree; and determining the interference type of the interference signal in the navigation signal data to be identified according to a relative majority voting rule. The method and the device can identify different interference types.
Description
Technical Field
The present application belongs to the field of wireless communication technologies, and in particular, to an interference type identification method, apparatus, terminal device, and medium.
Background
The satellite navigation receiver receives a navigation signal broadcast by a satellite to realize positioning, and the navigation signal is interfered by various electromagnetic waves in the process of being transmitted from the satellite to the ground receiver, wherein the electromagnetic waves can be unintentional interference generated by other electronic equipment and can also be man-made malicious interference specially aiming at the navigation signal. These interferences can cause the receiver to fail to locate properly when severe. Common electromagnetic wave interference can be classified into single frequency interference, frequency sweep interference, narrow band interference and the like according to different signal spectrum characteristics.
At present, the detection and identification of interference are mainly based on post-processing, navigation signals are acquired and then converted into a frequency domain, the type of interference is artificially judged according to the spectral characteristics, and a method for identifying the type of interference in a receiver in real time is not available.
Along with the fact that high-precision positioning is more and more widely applied to the field of intelligent automobiles, the requirement on positioning reliability in various complex electromagnetic environments is higher and higher, the method is vital to real-time detection and identification of interference types, and only when the interference types are correctly detected and identified, an effective anti-interference algorithm can be correspondingly adopted, so that the optimal anti-interference effect is achieved. However, the current interference type identification method cannot identify different interference types.
Disclosure of Invention
The embodiment of the application provides an interference type identification method, an interference type identification device, terminal equipment and a medium, and can solve the problem that the interference type identification method cannot identify different interference types.
In a first aspect, an interference type identification method in an embodiment of the present application includes:
acquiring baseband digital signal data of navigation signal data to be identified;
performing dimensionality reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data includes a plurality of low-dimensional baseband digital signals;
randomly extracting low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, and taking the energy ratio characteristics of the low-dimensional baseband digital signals extracted each time in preset number as a training sample to obtain a training sample set; the training sample set comprises a plurality of training samples;
constructing a random forest classification recognition model; the random forest classification recognition model comprises a decision tree constructed based on each training sample;
respectively aiming at the decision tree of each training sample, taking the interference type with the largest proportion on each node of the decision tree as the interference type of the node, and determining the interference type classification result corresponding to the decision tree according to the interference types of all the nodes on the decision tree;
and determining the interference type of the interference signal in the navigation signal data to be identified from a plurality of interference type classification results according to a relative majority voting rule.
Optionally, the obtaining baseband digital signal data of the navigation signal data to be identified includes:
performing down-conversion processing on navigation signal data to be identified, and performing AD (analog-to-digital) conversion on a down-conversion processing result to obtain baseband digital signal data A; wherein, a = { a = 1 ,a 2 ,...,a i ,...,a n }，a i Represents the ith baseband digital signal of all n baseband digital signals, i =1, 2.
Optionally, the performing the dimension reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain the low-dimensional baseband digital signal data includes:
constructing a transformation matrix T according to the baseband digital signal data A;
by calculating the formula B = T T * A obtains low-dimensional baseband digital signal data B.
Optionally, constructing a transformation matrix T according to the baseband digital signal data a includes:
calculating the mean value mu of the baseband digital signal data A, and performing mean value removing processing on the baseband digital signal data A according to the mean value to obtain new baseband digital signal data A';
by calculating the formula Cx = A '. A' T Obtaining an autocorrelation function Cx of the new baseband digital signal data A';
obtaining a plurality of characteristic values lambda of the new baseband digital signal data A' by calculating a formula Cx t = lambda t; wherein t represents an optimal transformation vector;
arranging the plurality of eigenvalues according to the sequence of the eigenvalues from large to small to obtain the transformation momentArray T, T = { λ 1 ,λ 2 ,...,λ n }。
Optionally, regarding the decision tree of each training sample, taking the interference type with the largest ratio on each node of the decision tree as the interference type of the node, including:
respectively randomly selecting m energy ratio features for each node on a decision tree of each training sample to construct an energy ratio feature space; wherein m = log 2 d, d represents the total number of energy ratio features in the training sample;
and classifying the plurality of energy ratio characteristics of the energy ratio characteristic space according to the energy ratios of different interference types, and taking the interference type corresponding to the energy ratio characteristic with the largest quantity as the interference type of the node.
Optionally, determining the interference type of the interference signal in the navigation signal data to be identified according to a relative majority voting rule from a plurality of interference type classification results, including:
voting the interference type classification results of each decision tree by using a relative majority voting method, and determining the interference type classification result with the most votes in the multiple interference type classification results as the interference type of the interference signal in the navigation signal data to be identified.
In a second aspect, an embodiment of the present application provides an interference type identification apparatus, including:
the acquisition module is used for acquiring baseband digital signal data of the navigation signal data to be identified;
the dimension reduction module is used for carrying out dimension reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data includes a plurality of low-dimensional baseband digital signals;
the training module is used for randomly extracting low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, and taking the energy ratio characteristics of the low-dimensional baseband digital signals extracted each time in preset number as a training sample to obtain a training sample set; the training sample set comprises a plurality of training samples;
the construction module is used for constructing a random forest classification recognition model; the random forest classification recognition model comprises a decision tree constructed based on each training sample;
the first identification module is used for respectively aiming at the decision tree of each training sample, taking the interference type with the maximum ratio on each node of the decision tree as the interference type of the node, and determining the interference type classification result corresponding to the decision tree according to the interference types of all the nodes on the decision tree;
and the second identification module is used for determining the interference type of the interference signal in the navigation signal data to be identified from the plurality of interference type classification results according to a relative majority voting rule.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the interference type identification method when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for identifying an interference type is implemented.
The above scheme of this application has following beneficial effect:
in some embodiments of the application, the baseband digital signal data of the navigation signal data to be identified is obtained, then the dimension reduction processing is performed on the energy ratio characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data, then the low-dimensional baseband digital signals are extracted from the low-dimensional baseband digital signal data for multiple times at random, the energy ratio characteristics of the low-dimensional baseband digital signals extracted each time in a preset number are used as a training sample, then a random forest classification identification model is constructed, the interference type with the largest ratio on each node of each decision tree is used as the interference type of the node, then the corresponding interference type classification result of the decision tree is determined according to the interference types of all the nodes of the decision tree, and finally the interference type of the interference signal in the navigation signal data to be identified is determined from a plurality of interference type classification results according to a relative majority voting rule. Each decision tree in the random forest classification recognition model can recognize and classify the type of the interference signal in the navigation signal data to be recognized according to the energy proportion characteristic, and then determine the interference type of the interference signal according to a relative majority voting rule, so that different interference types can be recognized.
Other advantages of the present application will be described in detail in the detailed description that follows.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an interference type identification method according to an embodiment of the present application;
FIG. 2a is a time domain waveform of a non-interfering baseband digital signal according to an embodiment of the present application;
fig. 2b is a time domain waveform diagram of a baseband digital signal in the presence of single frequency interference according to an embodiment of the present application;
fig. 2c is a time domain waveform diagram of a baseband digital signal in the presence of swept-frequency interference according to an embodiment of the present application;
fig. 2d is a time domain waveform diagram of a baseband digital signal in the presence of narrowband interference according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an interference type identification apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The method comprises the steps of obtaining baseband digital signal data of navigation signal data to be identified, carrying out dimension reduction processing on energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data, randomly extracting low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, taking the energy proportion characteristics of a preset number of the low-dimensional baseband digital signals extracted each time as a training sample, constructing a random forest classification identification model, taking the interference type with the largest proportion on each node of each decision tree as the interference type of the node, determining the corresponding interference type classification result of the decision tree according to the interference types of all the nodes of the decision tree, and finally determining the interference type of an interference signal in the navigation signal data to be identified according to a relative majority rule voting classification result. Each decision tree in the random forest classification recognition model can recognize and classify the type of the interference signal in the navigation signal data to be recognized according to the energy proportion characteristic, and then determine the interference type of the interference signal according to a relative majority voting rule, so that different interference types can be recognized.
As shown in fig. 1, the method for identifying interference types provided by the present application includes the following steps:
and step 11, obtaining baseband digital signal data of the navigation signal data to be identified.
The navigation signal (navigation signal) can be received by an antenna in the receiver, but due to various electromagnetic wave interferences, various interference signals including single-frequency interference, frequency sweep interference, narrow-band interference and the like are mixed in the received navigation signal.
In order to detect and identify various interference signals in the navigation signal, the navigation signal needs to be converted into baseband digital signal data, so as to distinguish interference types according to energy ratio characteristics in the baseband digital signal data, and the conversion process is completed in the radio frequency front end.
And step 12, performing dimensionality reduction on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data.
Different interference types have different energy ratio characteristics, as shown in fig. 2a, when there is no interference, the signal energy of the baseband digital signal in the baseband digital signal data is concentrated in the middle area; as shown in fig. 2b, when there is single-frequency interference, the signal energy of the baseband digital signal in the baseband digital signal data is distributed in a high energy interval in a concentrated manner; as shown in fig. 2c, when there is frequency sweeping interference, signal energy of the baseband digital signal in the baseband digital signal data is distributed in high energy and low energy regions in a concentrated manner, and the ratio of the high energy and low energy regions is substantially the same; as shown in fig. 2d, when the narrowband interference exists, the signal energy distribution of the baseband digital signal in the baseband digital signal data is disordered and is not concentrated in each energy interval. In the above diagrams (fig. 2a, 2b, 2c, 2 d), the abscissa indicates the number of the baseband digital signal, and the ordinate indicates the energy corresponding to the baseband digital signal.
Based on the above features, the navigation signal with the interference signal can be detected and identified according to the energy ratio.
Each baseband digital signal contains a lot of energy ratio features, which makes the subsequent calculation very complicated, and the dimension reduction processing is performed on the energy ratio features, so that the number of the energy ratio features can be reduced, and the calculation amount is reduced.
The low-dimensional baseband digital signal data includes a plurality of low-dimensional baseband digital signals, each of which includes a plurality of energy-ratio features.
It should be noted that, in some embodiments of the present application, the dimension of the energy-to-energy ratio feature is uniformly reduced to 10, so that the recognition effect can be ensured and a smaller amount of calculation can be ensured.
And step 13, randomly extracting the low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, and taking the energy ratio characteristics of the low-dimensional baseband digital signals extracted each time in a preset number as a training sample to obtain a training sample set.
This step can improve the accuracy of the interference type identification.
And 14, constructing a random forest classification recognition model.
The random forest classification recognition model comprises a decision tree constructed based on each training sample.
And step 15, regarding the decision tree of each training sample, respectively, taking the interference type with the largest proportion on each node of the decision tree as the interference type of the node, and determining the interference type classification result corresponding to the decision tree according to the interference types of all the nodes on the decision tree.
And determining the interference type classification result of the decision tree according to the interference types of all the nodes, so that the interference of unexpected factors can be eliminated, and the accuracy of interference type identification is improved.
And step 16, determining the interference type of the interference signal in the navigation signal data to be identified from the plurality of interference type classification results according to a relative majority voting rule.
The relative majority Voting (Plurality Voting) is a simple Voting method that uses the rationale of minority-obeying. In the classification problem, the prediction result of the sample is usually the final classification category with the largest number of votes; if more than one category gets the highest ticket, one is randomly selected as the final category.
Specifically, voting is carried out on the interference type classification result of each decision tree by using a relative majority voting method, and the interference type classification result with the most votes in the multiple interference type classification results is determined as the interference type of the interference signal in the navigation signal data to be identified.
The specific process of step 11 (obtaining the baseband digital signal data of the navigation signal data to be identified) is explained as an example.
Specifically, down-conversion processing is carried out on the navigation signal data to be identified, andperforming AD conversion on the down-conversion processing result to obtain baseband digital signal data A; wherein a = { a = 1 ,a 2 ,...,a i ,...,a n }，a i Represents the ith baseband digital signal of all n baseband digital signals, i =1, 2.
The baseband digital signal data comprises a plurality of baseband digital signals, and each baseband digital signal comprises a plurality of energy ratio characteristics.
The following is an exemplary description of a specific process of step 12 (performing dimension reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data).
And step 12.1, constructing a transformation matrix T according to the baseband digital signal data A.
And step 12.1.1, calculating the mean value mu of the baseband digital signal data A, and performing mean value removing processing on the baseband digital signal data A according to the mean value to obtain new baseband digital signal data A'.
Specifically, a' = { a = 1 -μ,a 2 -μ,...,a i -μ,...,a n -μ}。
Step 12.1.2, calculating formula
Cx＝A′*A′ T
The autocorrelation function Cx of the new baseband digital signal data a' is obtained.
Step 12.1.3, calculating formula
Cx*t＝λ*t
Obtaining a plurality of characteristic values lambda of new baseband digital signal data A'; where t represents the optimal transform vector.
Step 12.1.4, arranging the plurality of eigenvalues in the order of the eigenvalues from large to small to obtain a transformation matrix T, wherein T = lambda 1 ,λ 2 ,...,λ n 。
Step 12.2, by calculating the formula B = T T * A obtains low-dimensional baseband digital signal data B.
The following is an exemplary description of a specific process of determining the interference type classification result corresponding to the decision tree according to the interference types of all nodes on the decision tree in step 15 (for each decision tree of the training sample, the interference type with the largest ratio on each node of the decision tree is taken as the interference type of the node, and the interference type classification result corresponding to the decision tree is determined according to the interference types of all nodes on the decision tree).
Step 15.1, randomly selecting m energy ratio features to construct an energy ratio feature space for each node on a decision tree of each training sample; wherein m = log 2 d, d represents the total number of energy-specific features in the training sample.
And step 15.2, classifying the multiple energy ratio characteristics of the energy ratio characteristic space according to the energy ratios of different interference types, and taking the interference type corresponding to the energy ratio characteristic with the largest quantity as the interference type of the node.
The interference type identification method obtains baseband digital signal data of navigation signal data to be identified, performs dimension reduction processing on energy ratio characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data, randomly extracts low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, uses the energy ratio characteristics of a preset number of low-dimensional baseband digital signals extracted each time as a training sample, then constructs a random forest classification identification model, uses an interference type with the largest ratio on each node of each decision tree as an interference type of the node, determines corresponding interference type classification results of the decision tree according to the interference types of all nodes of the decision tree, and finally determines the interference type of the interference signal in the navigation signal data to be identified according to a relative majority voting rule from a plurality of interference type classification results. Each decision tree in the random forest classification recognition model can recognize and classify the type of the interference signal in the navigation signal data to be recognized according to the energy proportion characteristic, and then determine the interference type of the interference signal according to a relative majority voting rule, so that different interference types can be recognized.
The following describes an exemplary interference type identification apparatus provided in the present application with reference to specific embodiments.
As shown in fig. 3, an embodiment of the present application provides an interference type identification apparatus 300, including:
the obtaining module 301 is configured to obtain baseband digital signal data of the navigation signal data to be identified.
A dimension reduction module 302, configured to perform dimension reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data includes a plurality of low-dimensional baseband digital signals.
The training module 303 is configured to randomly extract the low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, and use energy proportion characteristics of a preset number of low-dimensional baseband digital signals extracted each time as a training sample to obtain a training sample set; the training sample set includes a plurality of training samples.
A building module 304, configured to build a random forest classification recognition model; the random forest classification recognition model comprises a decision tree constructed based on each training sample.
The first identifying module 305 is configured to, for each decision tree of each training sample, use the interference type with the largest ratio on each node of the decision tree as the interference type of the node, and determine an interference type classification result corresponding to the decision tree according to the interference types of all nodes on the decision tree.
A second identifying module 306, configured to determine an interference type of the interference signal in the navigation signal data to be identified according to a relative majority voting rule from the multiple interference type classification results.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
As shown in fig. 4, an embodiment of the present application provides a terminal device, and a terminal device D10 of the embodiment includes: at least one processor D100 (only one processor is shown in fig. 4), a memory D101, and a computer program D102 stored in the memory D101 and operable on the at least one processor D100, wherein the processor D100 implements the steps of any of the method embodiments described above when executing the computer program D102.
Specifically, when the processor D100 executes the computer program D102, the baseband digital signal data of the navigation signal data to be identified is obtained, then the dimension reduction processing is performed on the energy ratio feature of the baseband digital signal data to obtain low-dimensional baseband digital signal data, then the low-dimensional baseband digital signal is randomly extracted from the low-dimensional baseband digital signal data for multiple times, the energy ratio features of the preset number of low-dimensional baseband digital signals extracted each time are used as a training sample, then a random forest classification identification model is constructed, the interference type with the largest ratio on each node of each decision tree is used as the interference type of the node, the corresponding interference type classification result of the decision tree is determined according to the interference types of all nodes of the decision tree, and finally the interference type of the interference signal in the navigation signal data to be identified is determined from the multiple interference type classification results according to a relative majority voting rule. Each decision tree in the random forest classification recognition model can recognize and classify the type of the interference signal in the navigation signal data to be recognized according to the energy proportion characteristic, and then determine the interference type of the interference signal according to a relative majority voting rule, so that different interference types can be recognized.
The Processor D100 may be a Central Processing Unit (CPU), and the Processor D100 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage D101 may be an internal storage unit of the terminal device D10 in some embodiments, for example, a hard disk or a memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the terminal device D10. Further, the memory D101 may include both an internal storage unit and an external storage device of the terminal device D10. The memory D101 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer programs. The memory D101 may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above may be implemented by instructing relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the methods described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include at least: any entity or means capable of carrying computer program code to the interference type identification means/terminal device, to a recording medium, to a computer Memory, to a Read-Only Memory (ROM), to a Random Access Memory (RAM), to an electrical carrier signal, to a telecommunications signal, and to a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In some jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and proprietary practices.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The interference type identification method has the characteristics of high real-time performance, high detection success rate and small calculated amount, effectively solves the problem that a high-precision satellite navigation receiver does not have the real-time interference detection identification method, and can be applied to the design and manufacture of high-precision satellite navigation positioning chips.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
While the foregoing is directed to the preferred embodiment of the present application, it will be appreciated by those skilled in the art that various changes and modifications may be made therein without departing from the principles of the application, and it is intended that such changes and modifications be covered by the scope of the application.
Claims (9)
1. An interference type identification method, comprising:
acquiring baseband digital signal data of navigation signal data to be identified;
performing dimensionality reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data comprises a plurality of low-dimensional baseband digital signals;
randomly extracting low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, and taking the energy ratio characteristics of the low-dimensional baseband digital signals extracted each time in preset number as a training sample to obtain a training sample set; the training sample set comprises a plurality of training samples;
constructing a random forest classification recognition model; the random forest classification recognition model comprises a decision tree constructed based on each training sample;
respectively aiming at the decision tree of each training sample, taking the interference type with the largest proportion on each node of the decision tree as the interference type of the node, and determining the interference type classification result corresponding to the decision tree according to the interference types of all the nodes on the decision tree;
and determining the interference type of the interference signal in the navigation signal data to be identified from a plurality of interference type classification results according to a relative majority voting rule.
2. The interference type identification method according to claim 1, wherein the obtaining baseband digital signal data of the navigation signal data to be identified comprises:
performing down-conversion processing on the navigation signal data to be identified, and performing AD conversion on a down-conversion processing result to obtain baseband digital signal data A; wherein, a = { a = 1 ，a 2 ，...，a i ，...，a n }，a i Represents the ith baseband digital signal of all n baseband digital signals, i =1, 2.
3. The method according to claim 2, wherein the performing a dimension reduction process on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data includes:
constructing a transformation matrix T according to the baseband digital signal data A;
by calculating the formula B = T T * And A, obtaining the low-dimensional baseband digital signal data B.
4. The interference type identification method according to claim 3, wherein said constructing a transformation matrix T from said baseband digital signal data A comprises:
calculating the mean value mu of the baseband digital signal data A, and performing mean value removing processing on the baseband digital signal data A according to the mean value to obtain new baseband digital signal data A';
by calculating the formula Cx = A '. A' T Obtaining an autocorrelation function Cx of the new baseband digital signal data a';
obtaining a plurality of characteristic values λ of the new baseband digital signal data a' by calculating a formula Cx × t = λ × t; wherein t represents an optimal transformation vector;
arranging the plurality of eigenvalues in the order of eigenvalue descending to obtain the transformation matrix T, T = { λ = { (λ) } 1 ，λ 2 ，...，λ n }。
5. The interference type identification method according to claim 1, wherein the step of taking the interference type with the largest ratio at each node of the decision tree as the interference type of the node, which is performed by the decision tree for each training sample, comprises:
respectively randomly selecting m energy ratio features for each node on a decision tree of each training sample to construct an energy ratio feature space; wherein m = log 2 d, d represents the total quantity of energy proportion features in the training sample;
and classifying the plurality of energy ratio characteristics of the energy ratio characteristic space according to the energy ratios of different interference types, and taking the interference type corresponding to the energy ratio characteristic with the largest quantity as the interference type of the node.
6. The interference type identification method according to claim 1, wherein the determining the interference type of the interference signal in the navigation signal data to be identified from a plurality of interference type classification results according to a relative majority voting rule comprises:
voting the interference type classification results of each decision tree by using a relative majority voting method, and determining the interference type classification result with the most votes in the multiple interference type classification results as the interference type of the interference signal in the navigation signal data to be identified.
7. An interference type identification device, comprising:
the acquisition module is used for acquiring baseband digital signal data of the navigation signal data to be identified;
the dimension reduction module is used for carrying out dimension reduction processing on the energy ratio characteristic of the baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data comprises a plurality of low-dimensional baseband digital signals;
the training module is used for randomly extracting low-dimensional baseband digital signals from the low-dimensional baseband digital signal data for multiple times, and taking the energy ratio characteristics of the low-dimensional baseband digital signals extracted each time in preset number as a training sample to obtain a training sample set; the training sample set comprises a plurality of training samples;
the construction module is used for constructing a random forest classification recognition model; the random forest classification recognition model comprises a decision tree constructed based on each training sample;
the first identification module is used for respectively aiming at the decision tree of each training sample, taking the interference type with the maximum ratio on each node of the decision tree as the interference type of the node, and determining the interference type classification result corresponding to the decision tree according to the interference types of all the nodes on the decision tree;
and the second identification module is used for determining the interference type of the interference signal in the navigation signal data to be identified from a plurality of interference type classification results according to a relative majority voting rule.
8. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the interference type identification method according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the interference type identification method according to any one of claims 1 to 6.
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| CN116973945B (en) * | 2023-09-21 | 2023-12-08 | 山东科技大学 | Interference detection method and system based on GNSS data of intelligent terminal |
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