Interference type identification method and apparatus, terminal device, and medium

Abstract

The application is suitable for the field of wireless communication technology, and provides a kind of interference type identification method, device, terminal equipment and medium. Among them, the method obtains the baseband digital signal data of the navigation signal data to be identified;The energy proportion feature of baseband digital signal data is reduced dimension, and low-dimensional baseband digital signal data is obtained;Randomly extract low-dimensional baseband digital signal from low-dimensional baseband digital signal data, and obtain training sample set;Each training sample is used to construct decision tree, and random forest classification identification model is constructed according to all decision trees;The interference type with the largest proportion on each node of each 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 type of all nodes on the decision tree;The interference type of interference signal in the navigation signal data to be identified is determined according to relative majority voting rule.The application can identify different interference types.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and in particular relates to an interference type identification method, apparatus, terminal equipment and medium. Background Technology

[0002] Satellite navigation receivers receive navigation signals broadcast by satellites to achieve positioning. During the transmission of these signals from satellites to the ground receiver, they are subject to various electromagnetic interferences. These interferences may be unintentional interference from other electronic devices or malicious interference specifically targeting navigation signals. Severe interference can prevent the receiver from achieving proper positioning. Common electromagnetic interferences can be categorized based on their signal spectrum characteristics, such as single-frequency interference, frequency-sweeping interference, and narrowband interference.

[0003] Currently, interference detection and identification are mainly based on post-processing. After the navigation signal is acquired, it is transformed into the frequency domain, and the type of interference is judged manually based on the spectral characteristics. There is no method for real-time interference type identification in the receiver.

[0004] As high-precision positioning becomes increasingly widely used in the field of intelligent vehicles, the requirements for positioning reliability in various complex electromagnetic environments are becoming increasingly stringent. Real-time detection and identification of interference types are crucial. Only by correctly detecting and identifying the type of interference can effective anti-interference algorithms be adopted to achieve optimal anti-interference results. However, current interference type identification methods cannot recognize different types of interference. Summary of the Invention

[0005] This application provides an interference type identification method, apparatus, terminal device, and medium, which can solve the problem that interference type identification methods cannot identify different interference types.

[0006] In a first aspect, an embodiment of this application provides a method for identifying interference types, comprising:

[0007] Acquire baseband digital signal data of the navigation signal data to be identified;

[0008] The energy proportion characteristics of baseband digital signal data are reduced in dimensionality to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data includes multiple low-dimensional baseband digital signals.

[0009] Multiple random samplings of low-dimensional baseband digital signals are performed from the low-dimensional baseband digital signal data. The energy proportion characteristics of a predetermined number of low-dimensional baseband digital signals extracted each time are used as training samples to obtain a training sample set. The training sample set includes multiple training samples.

[0010] Construct a random forest classification and recognition model; the random forest classification and recognition model includes a decision tree built based on each training sample;

[0011] For each training sample's decision tree, the interference type with the largest proportion at each node of the decision tree is taken as the interference type of that node, and the interference type classification result corresponding to the decision tree is determined based on the interference types of all nodes in the decision tree.

[0012] The interference type of the interfering signal in the navigation signal data to be identified is determined from the classification results of multiple interference types based on the relative majority voting rule.

[0013] Optionally, the baseband digital signal data for acquiring the navigation signal data to be identified includes:

[0014] The navigation signal data to be identified is down-converted, and the down-conversion result is then converted to analog-to-digital (AD) signal to obtain baseband digital signal data A; where A = {a1, a2, ..., a...} i ,...,a n}, a i Let i represent the i-th baseband digital signal among all n baseband digital signals, where i = 1, 2, ..., n.

[0015] Optionally, the energy proportion characteristics of the baseband digital signal data are subjected to dimensionality reduction processing to obtain low-dimensional baseband digital signal data, including:

[0016] Construct the transformation matrix T based on the baseband digital signal data A;

[0017] The formula B = T is used for calculation. T *A obtains low-dimensional baseband digital signal data B.

[0018] Optionally, a transformation matrix T is constructed based on the baseband digital signal data A, including:

[0019] Calculate the mean μ of the baseband digital signal data A, and perform mean-removal processing on the baseband digital signal data A based on the mean to obtain new baseband digital signal data A′;

[0020] The formula Cx = A′ * A′ is used for calculation. T The autocorrelation function Cx of the new baseband digital signal data A′ is obtained;

[0021] Multiple eigenvalues ​​λ of the new baseband digital signal data A′ are obtained by calculating the formula Cx*t=λ*t; where t represents the optimal transform vector;

[0022] Arrange the eigenvalues ​​in descending order to obtain the transformation matrix T, T = {λ1, λ2, ..., λ...} n}

[0023] Optionally, for each training sample's decision tree, the interference type with the highest proportion at each node of the decision tree is taken as the interference type for that node, including:

[0024] For each node in the decision tree of each training sample, m energy proportion features are randomly selected to construct an energy proportion feature space; where m = log2d, and d represents the total number of energy proportion features in the training sample.

[0025] Based on the energy proportion of different interference types, multiple energy proportion features in the energy proportion feature space are classified, and the interference type corresponding to the most numerous energy proportion feature is taken as the interference type of the node.

[0026] Optionally, the interference type of the interfering signal in the navigation signal data to be identified can be determined from multiple interference type classification results according to the relative majority voting rule, including:

[0027] The interference type classification results of each decision tree are voted on using the relative majority voting method, and the interference type classification result with the most votes among multiple interference type classification results is determined as the interference type of the interference signal in the navigation signal data to be identified.

[0028] Secondly, embodiments of this application provide an interference type identification device, comprising:

[0029] The acquisition module is used to acquire the baseband digital signal data of the navigation signal data to be identified;

[0030] The dimensionality reduction module is used to perform dimensionality reduction processing on the energy proportion characteristics of baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data includes multiple low-dimensional baseband digital signals.

[0031] The training module is used to randomly extract low-dimensional baseband digital signals multiple times from the low-dimensional baseband digital signal data, and use the 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 multiple training samples.

[0032] The building block is used to construct a random forest classification and recognition model; the random forest classification and recognition model includes a decision tree built based on each training sample;

[0033] The first identification module is used to identify the interference type with the largest proportion at each node of the decision tree for each training sample, and to determine the interference type classification result corresponding to the decision tree based on the interference types of all nodes in the decision tree.

[0034] The second identification module is used to determine the type of interference signal in the navigation signal data to be identified from multiple interference type classification results based on the relative majority voting rule.

[0035] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned interference type identification method.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned interference type identification method.

[0037] The above-mentioned solution in this application has the following beneficial effects:

[0038] In some embodiments of this application, baseband digital signal data of the navigation signal data to be identified is acquired, and then the energy proportion features of the baseband digital signal data are subjected to dimensionality reduction processing to obtain low-dimensional baseband digital signal data. Low-dimensional baseband digital signals are then randomly sampled multiple times from this low-dimensional data, and the energy proportion features of a predetermined number of sampled low-dimensional baseband digital signals are used as training samples. A random forest classification and recognition model is then constructed, and the interference type with the largest proportion at each node of each decision tree is taken as the interference type of that node. The corresponding interference type classification result of the decision tree is then determined based on the interference types of all nodes in the decision tree. Finally, the interference type of the interference signal in the navigation signal data to be identified is determined from multiple interference type classification results using a relative majority voting rule. In this random forest classification and recognition model, each decision tree can identify and classify the type of interference signal in the navigation signal data to be identified based on the energy proportion features, and then determine the interference type of the interference signal based on the relative majority voting rule, thereby enabling the identification of different interference types.

[0039] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart of an interference type identification method provided in an embodiment of this application;

[0042] Figure 2aA time-domain waveform diagram of an interference-free baseband digital signal provided in an embodiment of this application;

[0043] Figure 2b This application provides a time-domain waveform diagram of a baseband digital signal in the presence of single-frequency interference, as shown in one embodiment of the present application.

[0044] Figure 2c This is a time-domain waveform diagram of a baseband digital signal in the presence of frequency sweep interference, provided in an embodiment of this application.

[0045] Figure 2d This application provides a time-domain waveform diagram of a baseband digital signal in the presence of narrowband interference, as shown in one embodiment.

[0046] Figure 3 This is a schematic diagram of the structure of an interference type identification device provided in an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0049] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0050] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0051] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0052] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0054] To address the problem that current interference classification and identification methods cannot identify different interference types, this application proposes an interference type identification method. This method acquires baseband digital signal data from the navigation signal data to be identified, then performs dimensionality reduction processing on the energy proportion features of the baseband digital signal data to obtain low-dimensional baseband digital signal data. Next, it randomly extracts low-dimensional baseband digital signals multiple times from this low-dimensional data, using the energy proportion features of a predetermined number of extracted low-dimensional baseband digital signals as training samples. A random forest classification and identification model is then constructed, and the interference type with the largest proportion at each node of each decision tree is taken as the interference type for that node. The classification result of the corresponding interference type for that decision tree is determined based on the interference types of all nodes. Finally, the interference type of the interference signal in the navigation signal data to be identified is determined from multiple interference type classification results using a relative majority voting rule. In this method, each decision tree in the random forest classification and identification model can identify and classify the type of interference signal in the navigation signal data to be identified based on the energy proportion features, and then determine the interference type based on the relative majority voting rule, thereby enabling the identification of different interference types.

[0055] like Figure 1 As shown, the interference type identification method provided in this application includes the following steps:

[0056] Step 11: Obtain the baseband digital signal data of the navigation signal data to be identified.

[0057] Navigation signals can be received by the antenna in the receiver, but due to various electromagnetic interferences, the received navigation signals are mixed with various interference signals, including single-frequency interference, frequency sweeping interference, narrowband interference, etc.

[0058] 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 that the interference type can be distinguished according to the energy ratio characteristics in the baseband digital signal data. The above conversion process is completed in the radio frequency front end.

[0059] Step 12: Dimensionality reduction processing is performed on the energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data.

[0060] Different types of interference have different energy proportion characteristics, such as Figure 2a As shown, when there is no interference, the signal energy of the baseband digital signal in the baseband digital signal data is concentrated in the middle region; as Figure 2b As shown, when single-frequency interference exists, the signal energy of the baseband digital signal in the baseband digital signal data is concentrated in the high-energy range; for example... Figure 2c As shown, when frequency sweep interference exists, the signal energy of the baseband digital signal in the baseband digital signal data is concentrated in the high-energy and low-energy ranges, with the proportions of the high-energy and low-energy ranges being basically the same; for example... Figure 2d As shown in the figure above, when narrowband interference exists, the signal energy distribution of the baseband digital signal in the baseband digital signal data is disordered and not concentrated in each energy range. Figure 2a , Figure 2b , Figure 2c , Figure 2d In the diagram, the horizontal axis represents the baseband digital signal number, and the vertical axis represents the energy corresponding to the baseband digital signal.

[0061] Based on the above characteristics, navigation signals with interference signals can be detected and identified according to the energy ratio.

[0062] Each baseband digital signal contains many energy proportion features, which makes subsequent calculations very complex. Here, we perform dimensionality reduction processing on the energy proportion features to reduce the number of energy proportion features and reduce the amount of computation.

[0063] The aforementioned low-dimensional baseband digital signal data includes multiple low-dimensional baseband digital signals, and each low-dimensional baseband digital signal includes multiple energy percentage characteristics.

[0064] It should be noted that, considering that reducing the feature dimension to a very small value would result in a very small feature value, making feature recognition impossible, in some embodiments of this application, the dimension of the energy proportion feature is uniformly reduced to 10. In this way, both the recognition effect and the amount of computation can be guaranteed.

[0065] Step 13: Randomly extract low-dimensional baseband digital signals multiple times from the low-dimensional baseband digital signal data, and use the 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.

[0066] This step can improve the accuracy of interference type identification.

[0067] Step 14: Construct a random forest classification and recognition model.

[0068] The aforementioned random forest classification and recognition model includes a decision tree built based on each training sample.

[0069] Step 15: For each training sample's decision tree, the interference type with the largest proportion at each node of the decision tree is taken as the interference type of that node, and the interference type classification result corresponding to the decision tree is determined based on the interference types of all nodes in the decision tree.

[0070] By determining the interference type classification result of the decision tree based on the interference type of all nodes, the interference of unexpected factors can be eliminated, thereby improving the accuracy of interference type identification.

[0071] Step 16: Determine the interference type of the interference signal in the navigation signal data to be identified from the multiple interference type classification results according to the relative majority voting rule.

[0072] Plurality voting is a simple voting method that uses the principle of majority rule. In classification problems, the prediction result for a sample is usually the one with the most votes, which becomes the final classification category; if more than one category receives the most votes, it is randomly selected as the final category.

[0073] Specifically, the relative majority voting method is used to vote on the interference type classification results of each decision tree, and the interference type classification result with the most votes among multiple interference type classification results is determined as the interference type of the interference signal in the navigation signal data to be identified.

[0074] The following is an illustrative description of the specific process of step 11 (acquiring the baseband digital signal data of the navigation signal data to be identified).

[0075] Specifically, the navigation signal data to be identified is down-converted, and the down-conversion result is then converted to digital signal data A using an analog-to-digital converter (AD converter); where A = {a1, a2, ..., a...} i ,...,a n}, a i Let i represent the i-th baseband digital signal among all n baseband digital signals, where i = 1, 2, ..., n.

[0076] The baseband digital signal data contains multiple baseband digital signals, and each baseband digital signal contains multiple energy percentage characteristics.

[0077] The following is an illustrative example of the specific process of step 12 (reducing the dimensionality of the energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data).

[0078] Step 12.1: Construct the transformation matrix T based on the baseband digital signal data A.

[0079] Step 12.1.1: Calculate the mean μ of the baseband digital signal data A, and perform mean removal processing on the baseband digital signal data A based on the mean to obtain new baseband digital signal data A′.

[0080] Specifically, A′={a1-μ,a2-μ,...,a i -μ,...,a n -μ}.

[0081] Step 12.1.2, using the calculation formula

[0082] Cx=A′*A′ T

[0083] The autocorrelation function Cx of the new baseband digital signal data A′ is obtained.

[0084] Step 12.1.3, using the calculation formula

[0085] Cx*t=λ*t

[0086] Multiple eigenvalues ​​λ are obtained for the new baseband digital signal data A′; where t represents the optimal transform vector.

[0087] Step 12.1.4: Arrange the eigenvalues ​​in descending order to obtain the transformation matrix T, T = λ1, λ2, ..., λ n .

[0088] Step 12.2, calculate using the formula B = T T *A obtains low-dimensional baseband digital signal data B.

[0089] The following is an illustrative explanation of the specific process of determining the interference type classification result of the decision tree based on the interference types of all nodes on the decision tree in step 15 (for each training sample's decision tree, the interference type with the largest proportion on each node of the decision tree is taken as the interference type of that node, and the interference type classification result of the decision tree is determined based on the interference types of all nodes on the decision tree).

[0090] Step 15.1: For each node in the decision tree of each training sample, randomly select m energy proportion features to construct an energy proportion feature space; where m = log2d, and d represents the total number of energy proportion features in the training sample.

[0091] Step 15.2: Based on the energy proportion of different interference types, classify multiple energy proportion features in the energy proportion feature space, and take the interference type corresponding to the most numerous energy proportion feature as the interference type of the node.

[0092] As can be seen, the interference type identification method provided in this application obtains the baseband digital signal data of the navigation signal data to be identified, then performs dimensionality reduction processing on the energy proportion features of the baseband digital signal data to obtain low-dimensional baseband digital signal data. Next, it randomly extracts low-dimensional baseband digital signals multiple times from the low-dimensional baseband digital signal data, using the energy proportion features of a preset number of low-dimensional baseband digital signals extracted each time as training samples. Then, it constructs a random forest classification model, taking the interference type with the largest proportion at each node of each decision tree as the interference type of that node. It then determines the corresponding interference type classification result of the decision tree based on the interference types of all nodes in the decision tree. Finally, it determines the interference type of the interference signal in the navigation signal data to be identified from multiple interference type classification results using a relative majority voting rule. Specifically, each decision tree in the random forest classification model can identify and classify the type of interference signal in the navigation signal data to be identified based on the energy proportion features, and then determine the interference type of the interference signal according to the relative majority voting rule, thereby enabling the identification of different interference types.

[0093] The interference type identification device provided in this application will be described exemplarily below with reference to specific embodiments.

[0094] like Figure 3 As shown, an embodiment of this application provides an interference type identification device, the interference type identification device 300 including:

[0095] The acquisition module 301 is used to acquire the baseband digital signal data of the navigation signal data to be identified.

[0096] The dimensionality reduction module 302 is used to perform dimensionality reduction processing on the energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data; the low-dimensional baseband digital signal data includes multiple low-dimensional baseband digital signals.

[0097] The training module 303 is used to randomly extract low-dimensional baseband digital signals multiple times from the low-dimensional baseband digital signal data, and use the energy proportion features 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 multiple training samples.

[0098] Module 304 is used to build a random forest classification and recognition model; the random forest classification and recognition model includes a decision tree built based on each training sample.

[0099] The first identification module 305 is used to identify the interference type with the largest proportion at each node of the decision tree for each training sample, and to determine the interference type classification result corresponding to the decision tree based on the interference types of all nodes in the decision tree.

[0100] The second identification module 306 is used to determine the interference type of the interference signal in the navigation signal data to be identified from multiple interference type classification results according to the relative majority voting rule.

[0101] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0103] like Figure 4 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 4 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0104] Specifically, when the processor D100 executes the computer program D102, it acquires the baseband digital signal data of the navigation signal data to be identified, then performs dimensionality reduction processing on the energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data. It then randomly extracts low-dimensional baseband digital signals multiple times from this low-dimensional data, using the energy proportion characteristics of a preset number of extracted low-dimensional baseband digital signals as training samples. A random forest classification model is then constructed, and the interference type with the largest proportion at each node of each decision tree is taken as the interference type of that node. The classification result of the corresponding interference type for that decision tree is determined based on the interference types of all nodes in that decision tree. Finally, the interference type of the interference signal in the navigation signal data to be identified is determined from multiple interference type classification results using a relative majority voting rule. In this random forest classification model, each decision tree can identify and classify the type of interference signal in the navigation signal data to be identified based on the energy proportion characteristics, and then determine the interference type based on the relative majority voting rule, thereby enabling the identification of different interference types.

[0105] The processor D100 can be a central processing unit (CPU), or it can 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 can be a microprocessor or any conventional processor.

[0106] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0107] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0108] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to an interference type identification device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0110] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0111] The interference type identification method provided in this application has the characteristics of high real-time performance, high detection success rate, and low computational load. It effectively solves the problem of the lack of real-time interference detection and identification methods in high-precision satellite navigation receivers and can be applied to the design and manufacturing of high-precision satellite navigation and positioning chips.

[0112] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0113] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for identifying interference types, characterized in that, include: Acquire baseband digital signal data of the navigation signal data to be identified; The energy proportion characteristics of the baseband digital signal data are reduced in dimension to obtain low-dimensional baseband digital signal data. The low-dimensional baseband digital signal data includes multiple low-dimensional baseband digital signals; Multiple low-dimensional baseband digital signals are randomly extracted from the low-dimensional baseband digital signal data, and the energy proportion characteristics of a preset number of low-dimensional baseband digital signals extracted each time are used as training samples to obtain a training sample set; the training sample set includes multiple training samples. A random forest classification and recognition model is constructed; the random forest classification and recognition model includes a decision tree constructed based on each training sample; For each training sample's decision tree, the interference type with the largest proportion at each node of the decision tree is taken as the interference type of that node, and the interference type classification result corresponding to the decision tree is determined based on the interference types of all nodes in the decision tree. The interference type of the interference signal in the navigation signal data to be identified is determined from the classification results of multiple interference types based on the relative majority voting rule.

2. The interference type identification method according to claim 1, characterized in that, The baseband digital signal data used to acquire the navigation signal data to be identified includes: The navigation signal data to be identified is down-converted, and the down-conversion result is then converted to analog-to-digital (A / D) signal to obtain the baseband digital signal data A; where A = {a1, a2, ..., a...} i , ..., a n }, a i Let i represent the i-th baseband digital signal among all n baseband digital signals, where i = 1, 2, ..., n.

3. The interference type identification method according to claim 2, characterized in that, The step of reducing the dimensionality of the energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data includes: Construct a transformation matrix T based on the baseband digital signal data A; The formula B = T is used for calculation. T *A obtains the low-dimensional baseband digital signal data B.

4. The interference type identification method according to claim 3, characterized in that, The step of constructing the transformation matrix T based on the baseband digital signal data A includes: Calculate the mean μ of the baseband digital signal data A, and perform mean-removal processing on the baseband digital signal data A based on the mean to obtain new baseband digital signal data A′; The formula Cx = A′ * A′ is used for calculation. T The autocorrelation function Cx of the new baseband digital signal data A′ is obtained; Multiple feature values ​​λ of the new baseband digital signal data A′ are obtained by calculating Cx*t=λ*t; where t represents the optimal transform vector; Arrange the eigenvalues ​​in descending order to obtain the transformation matrix T, T = {λ1, λ2, ..., λ3}. n } 5. The interference type identification method according to claim 1, characterized in that, The decision tree for each training sample, which uses the most prevalent interference type at each node as the interference type for that node, includes: For each node in the decision tree of each training sample, m energy proportion features are randomly selected to construct an energy proportion feature space; where m = log2 d, and d represents the total number of energy proportion features in the training sample. Based on the energy proportion of different interference types, multiple energy proportion features in the energy proportion feature space are classified, and the interference type corresponding to the most numerous energy proportion feature is taken as the interference type of the node.

6. The interference type identification method according to claim 1, characterized in that, The step of determining the interference type of the interference signal in the navigation signal data to be identified from multiple interference type classification results according to the relative majority voting rule includes: The interference type classification results of each decision tree are voted on using a relative majority voting method, and the interference type classification result with the most votes among the multiple interference type classification results is determined as the interference type of the interference signal in the navigation signal data to be identified.

7. An interference type identification device, characterized in that, include: The acquisition module is used to acquire the baseband digital signal data of the navigation signal data to be identified; The dimensionality reduction module is used to perform dimensionality reduction processing on the energy proportion characteristics of the baseband digital signal data to obtain low-dimensional baseband digital signal data. The low-dimensional baseband digital signal data includes multiple low-dimensional baseband digital signals; The training module is used to randomly extract low-dimensional baseband digital signals multiple times from the low-dimensional baseband digital signal data, and use the 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 multiple training samples. A construction module is used to build a random forest classification and recognition model; the random forest classification and recognition model includes a decision tree built based on each training sample; The first identification module is used to identify the interference type with the largest proportion at each node of the decision tree for each training sample, and to determine the interference type classification result corresponding to the decision tree based on the interference types of all nodes in the decision tree. The second identification module is used to determine the interference type of the interference signal in the navigation signal data to be identified from multiple interference type classification results according to the 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, When the processor executes the computer program, it implements the interference type identification method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the interference type identification method as described in any one of claims 1 to 6.

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