CN112651349B - Wireless interference detection method, device, equipment and readable storage medium - Google Patents

Wireless interference detection method, device, equipment and readable storage medium Download PDF

Info

Publication number
CN112651349B
CN112651349B CN202011599036.8A CN202011599036A CN112651349B CN 112651349 B CN112651349 B CN 112651349B CN 202011599036 A CN202011599036 A CN 202011599036A CN 112651349 B CN112651349 B CN 112651349B
Authority
CN
China
Prior art keywords
signal
sliding window
time domain
signal characteristics
window function
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011599036.8A
Other languages
Chinese (zh)
Other versions
CN112651349A (en
Inventor
刘恒
苏金领
卢嘉益
马征
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest Jiaotong University
Original Assignee
Southwest Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest Jiaotong University filed Critical Southwest Jiaotong University
Priority to CN202011599036.8A priority Critical patent/CN112651349B/en
Publication of CN112651349A publication Critical patent/CN112651349A/en
Application granted granted Critical
Publication of CN112651349B publication Critical patent/CN112651349B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Monitoring And Testing Of Transmission In General (AREA)

Abstract

The invention relates to the technical field of rail transit wireless interference detection, in particular to a wireless interference detection method, a wireless interference detection device, wireless interference detection equipment and a readable storage medium. The method comprises the following steps: receiving and collecting wireless signal information, selecting an expression domain according to needs and constructing an expression F (x) of the wireless signal in the expression domain; setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure DDA0002870571530000011
i is 1, 2, …, n, and
Figure DDA0002870571530000012
a corresponding time domain value; calculating the said
Figure DDA0002870571530000013
And
Figure DDA0002870571530000014
signal characteristics of the time domain values of (a); and transmitting the signal characteristics back to a calculation center, so that the calculation center performs model training by using the signal characteristics. The invention intercepts the F (x) through a sliding window, and calculates the local characteristics of the signal, so as to obtain higher interference signal detection and identification precision in various machine learning algorithms.

Description

Wireless interference detection method, device, equipment and readable storage medium
Technical Field
The invention relates to the technical field of rail transit wireless interference detection, in particular to a wireless interference detection method, a wireless interference detection device, wireless interference detection equipment and a readable storage medium.
Background
With the continuous development of communication technology in China, wireless communication becomes an important application technology in the field of information communication in China, and the wide application of the wireless communication technology makes China get a new breakthrough in the fields of communication, aviation navigation, broadcast television and the like, and promotes the informatization construction in China. However, with the rapid development of wireless communication services, the problem of wireless signal interference also occurs in the process, the wireless signal interference can seriously affect the normal operation of the wireless communication services, and has great harm to the fields of communication, navigation, broadcast television and the like, especially malicious wireless communication interference signals, which seriously affect the national information security and social stability, so that the detection and identification of the wireless communication interference signals are important work.
The prior art has the following two working modes: 1. when the model for analyzing the signal characteristics is deployed on the signal acquisition equipment, the signal acquisition equipment needs to undertake all the work of receiving signals, processing signals, calculating characteristics, analyzing characteristics and the like, and the consequences of overlarge equipment load, slow calculation, space shortage and the like can be caused. 2. The signal acquisition device is only responsible for acquiring signals and transmitting the signals back to the cloud computing center, and the following disadvantages are caused: 1) a large number of centralized cloud computing demands may not match the explosively increasing mass of edge data; 2) a large amount of signal transmission requirements will cause the increase of transmission bandwidth load, generate longer transmission delay, and cannot meet the transmission requirements of real-time data; 3) for sensitive data, the security of the data in the transmission process is greatly challenged; 4) consuming a large amount of electric energy.
Meanwhile, in the prior art, the feature extraction of the wireless signal mainly focuses on the overall features of the signal, such as the statistical features of the whole frequency spectrum or the whole time domain of the signal, and does not deeply research and analyze the local features of the signal, so that the detection accuracy is low.
Disclosure of Invention
The present invention is directed to a method, an apparatus, a device and a readable storage medium for detecting radio interference, so as to improve the above-mentioned problems.
In order to achieve the above object, the embodiments of the present application provide the following technical solutions:
a method of wireless interference detection, comprising:
receiving and collecting wireless signal information, selecting an expression domain according to needs and constructing an expression F (x) of the wireless signal in the expression domain;
setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure BDA0002870571510000021
i is 1, 2, …, n, and
Figure BDA0002870571510000022
a corresponding time domain value;
calculating the said
Figure BDA0002870571510000023
And
Figure BDA0002870571510000024
signal characteristics of the time domain values of (a);
and transmitting the signal characteristics back to a calculation center, so that the calculation center performs model training by using the signal characteristics.
Further, the representation domain of the signal covers all the representation domains used for signal detection analysis.
Further, the sliding window function is set, and the sliding window function is utilized to automatically intercept the F (x) to obtain a plurality of parts of the F (x)
Figure BDA0002870571510000025
And
Figure BDA0002870571510000026
corresponding time domain values, including:
defining a sliding window function:
Figure BDA0002870571510000027
wherein the content of the first and second substances,
Figure BDA0002870571510000028
to representIth window, start(i)Denotes the start of the ith window, w(i)Indicates the width of the ith window,
Figure BDA0002870571510000029
respectively representing the internal coefficient and the external coefficient of the ith window, wherein x is the abscissa of the constructed F (x), and the type of x depends on the selected representation domain;
automatically intercepting the F (x) by using the sliding window function to obtain the F (x)
Figure BDA0002870571510000031
Figure BDA0002870571510000032
Wherein F (x) is the representation of the signal on the selected representation domain,
Figure BDA0002870571510000033
an i-th part (i ═ 1, 2, …, n) representing f (x);
to the above
Figure BDA0002870571510000034
And (3) performing correlation transformation calculation to obtain a corresponding time domain value:
Figure BDA0002870571510000035
wherein, F-1[·]Show that
Figure BDA0002870571510000036
And transforming to the time domain.
Further, of said F (x)
Figure BDA0002870571510000037
Including energy concentration regions and edge regions.
Further, the width of the window of the energy concentration area is smaller than or equal to the width of the window of the edge area.
Further, the signal characteristics of the local power spectrum are calculated and selected according to actual needs, and all the signal characteristics can be selected.
Further, the returning of the signal features to the computation center, so that the computation center performs model training using the signal features, includes:
obtaining mass signal characteristics through a field acquisition mode or a mode of transmitting the signal characteristics back to a computing center by utilizing a 4G/5G network or other possible modes, and providing the mass signal characteristics for the training of an initial model by the computing center;
deploying the initial model in an application scene, and in the actual application process of the model, using the characteristics of the newly acquired signals for incremental training of the initial model to obtain an updated model; the incremental training is a circularly repeated process, and a new model with better performance is obtained by continuously carrying out incremental training on the old model.
A wireless interference detection device comprises an acquisition module, a preprocessing module, a feature calculation module and a return module;
the acquisition module is used for receiving and acquiring wireless signal information, selecting an expression domain according to needs and constructing an expression F (x) of the wireless signal in the expression domain;
setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure BDA0002870571510000041
i is 1, 2, …, n, and
Figure BDA0002870571510000042
a corresponding time domain value;
the feature calculation module is used for calculating the feature
Figure BDA0002870571510000043
And
Figure BDA0002870571510000044
signal characteristics of the time domain values of (a);
and the back transmission module is used for transmitting the signal characteristics back to the calculation center, so that the calculation center performs model training by using the signal characteristics.
A wireless interference detection device, the device comprising a memory for storing a computer program and a processor; the processor is configured to implement the steps of the above-mentioned radio interference detection method when executing the computer program.
A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described radio interference detection method.
The invention has the beneficial effects that: according to the invention, by utilizing technologies such as edge equipment, edge gateways, edge clouds and the like, data is subjected to preprocessing, precomputation, feature calculation and the like in an edge platform, namely wireless interference detection equipment, and then the features are transmitted back to a computing center, so that the load of the computing center is reduced, the utilization rate and the computing efficiency of the edge equipment platform are improved, and the whole working architecture is clearer and more efficient.
The invention intercepts the frequency domain amplitude spectrum through the sliding window, deeply analyzes the local characteristics of the signal, and excavates the information of the signal hidden in the local part so as to obtain higher interference signal detection and identification precision in various machine learning algorithms.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart illustrating a radio interference detection method according to embodiment 1 of the present invention;
FIG. 2 is a normalized frequency domain amplitude spectrum in example 1 of the present invention;
FIG. 3 is a view showing a sliding window in an embodiment 1 of the present invention;
fig. 4 is a local spectrum diagram obtained by interception in embodiment 1 of the present invention;
FIG. 5 shows local spectral and time domain values in example 1 of the present invention;
fig. 6 is a first schematic structural diagram of a radio interference detection apparatus according to embodiment 2 of the present invention;
fig. 7 is a schematic structural diagram of a radio interference detection apparatus according to embodiment 2 of the present invention;
fig. 8 is a schematic structural diagram of a wireless interference detection device according to embodiment 3 of the present invention.
Reference numerals
100-a wireless interference detection device; 101-an acquisition module; 102-a pre-processing module; 103-a feature calculation module; 104-backhaul module; 200-a computing center; 300-an electronic device; 301-an electronic device; 302-a memory; 303-multimedia components; 304-input/output (I/O) interface; 305-a communication component.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers or letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, the present embodiment provides a method for detecting radio interference, which includes step S1, step S2, step S3 and step S4.
S1, receiving and collecting wireless signal information, selecting an expression domain according to needs and constructing an expression F (x) of the wireless signal in the expression domain; the representation domain of the present embodiment is applicable to any representation domain of a signal, such as: time domain, z domain, s domain, etc., as well as some function derived from the signal, such as an autocorrelation function, etc.
S2, setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure BDA0002870571510000071
i is 1, 2, …, n, and
Figure BDA0002870571510000072
a corresponding time domain value;
wherein, the
Figure BDA0002870571510000073
Can be divided into an energy concentration area and an edge area;
for some signals with more concentrated energy, a narrower window is taken for a key focus area of the signals, and a wider window is taken for the rest areas, so that the calculation amount and the calculation complexity are reduced under the condition of ensuring that key information of the signals is not lost.
The width of the window of the energy concentration area is smaller than or equal to the width of the window of the edge area, so that the signal features included in the energy concentration area can be extracted.
Specifically, the S2 includes:
s21, defining a sliding window function:
Figure BDA0002870571510000074
wherein the content of the first and second substances,
Figure BDA0002870571510000075
indicates the ith window, start(i)Denotes the start of the ith window, w(i)Indicates the width of the ith window,
Figure BDA0002870571510000076
respectively representing the internal coefficient and the external coefficient of the ith window, wherein x is the abscissa of constructed F (x) and depends on the selected representation domain;
s22, automatically intercepting the F (x) by using the sliding window function to obtain the F (x)
Figure BDA0002870571510000077
Figure BDA0002870571510000078
Wherein F (x) is a representation on the signal representation domain,
Figure BDA0002870571510000079
an i-th part (i ═ 1, 2, …, n) representing f (x);
s23, to
Figure BDA00028705715100000710
And (3) performing correlation transformation calculation to obtain a corresponding time domain value: :
Figure BDA00028705715100000711
wherein, F-1[·]Show that
Figure BDA00028705715100000712
And transforming to the time domain.
S3, calculating
Figure BDA00028705715100000713
And
Figure BDA00028705715100000714
signal characteristics of the time domain values of (a);
and S4, transmitting the signal characteristics back to a calculation center, and enabling the calculation center to perform model training by using the signal characteristics.
Specifically, the S4 includes:
s41, obtaining mass signal characteristics through a field acquisition mode or a mode of transmitting the signal characteristics back to a computing center by utilizing a 4G/5G network or other possible modes, and providing the mass signal characteristics for the training of an initial generation model by the computing center;
s42, deploying the primary model in an application scene, and in the actual application process of the model, using the newly acquired signal characteristics for incremental training of the primary model to obtain an updated model;
in the embodiment, a model is constructed by an incremental learning method, new signal characteristics are continuously read by the model and are predicted in the application process, the model feeds new sample data back to the model when a prediction result is given, and the model is continuously optimized by using the new sample data.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Referring to fig. 2 and fig. 3, setting a sliding window function, and automatically intercepting an LTE-M signal frequency domain magnitude spectrum with a bandwidth of 10MHz by using the sliding window function to obtain a plurality of local frequency spectrums and time domain values corresponding to the local frequency spectrums includes:
(1) defining a sliding window function:
Figure BDA0002870571510000081
wherein the content of the first and second substances,
Figure BDA0002870571510000082
represents the ith window;
in particular, in different signal scenarios, for start(i)Can be set according to the actual demand by oneself, as follows:
the window can be separated from the window (start)(i)+w(i)＜start(i+1)) Tangent of the two
Figure BDA0002870571510000083
Intersect (start)(i)+w(i)＞start(i+1))；
The setting of the starting point between the windows may be in equal step size (start)(i+1)-start(i)C is a fixed constant), and may be freely set;
(2) automatically intercepting a frequency domain magnitude spectrum by using the sliding window function to obtain a local frequency spectrum:
Figure BDA0002870571510000091
wherein, the
Figure BDA0002870571510000092
Representing the local frequency spectrum, f (f) is the frequency domain amplitude spectrum of the signal;
calculating a time domain value by using the local spectrum:
Figure BDA0002870571510000093
assuming a signal bandwidth of BHz, the signal will have B/w local spectra and their corresponding time domain values according to the sliding window definition and arrangement described above.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
For the
Figure BDA0002870571510000094
The following conditions are satisfied:
start(i)+w(i)＝start(i+1)； (7)
w(i)＝C,C＝2.5MHz； (8)
referring to fig. 4, a frequency domain amplitude spectrum f (f) of a signal is intercepted by using 4 windows with widths of 2.5MHz, after the window is intercepted, 4 local frequency spectrums are obtained, and a corresponding time domain value is obtained by using the local frequency spectrums.
(3) The signal characteristics of the local spectrum are calculated, such as:
local spectral mean:
Figure BDA0002870571510000095
local spectral variance:
Figure BDA0002870571510000096
local time domain peak-to-average ratio:
Figure BDA0002870571510000097
the signal features are simple features, the signal features can be calculated in both energy concentration regions and edge regions, for regions with concentrated energy, key feature extraction and feature calculation can be considered for the parts with concentrated energy, and specific calculation modes are not listed one by one here. Since several features can be calculated for each local part of the signal, the number of local features of the signal will be reached
Figure BDA0002870571510000098
A, wherein NiRepresenting the characteristic quantity of the ith part of the signal.
For example: using the above three formulas to perform feature calculation on 4 local spectra, please refer to fig. 5, 12 signal features can be obtained, as shown in table 1:
Figure BDA0002870571510000101
TABLE 1
Based on the method, the characteristic quantity of the signals is greatly expanded, and the characteristics carry abundant signal local information, so that the method is favorable for carrying out characteristic extraction on the interference mixed in the signals from the local parts of the signals, and provides characteristic data for interference analysis, identification and research.
Example 2
Corresponding to the above method embodiments, the embodiments of the present disclosure further provide a wireless interference detection apparatus, and a wireless interference detection apparatus described below and a wireless interference detection method described above may be referred to in correspondence.
Referring to fig. 6 and 7, the wireless interference detecting apparatus 100 includes an acquisition module 101, a preprocessing module 102, a feature calculating module 103 and a backhaul module 104,
the acquisition module 101 is used for receiving and acquiring wireless signal information and constructing a frequency domain amplitude spectrum;
the preprocessing module 102 is configured to set a sliding window function, and automatically intercept a frequency domain magnitude spectrum by using the sliding window function to obtain a plurality of local frequency spectrums and time domain values corresponding to the local frequency spectrums;
the feature calculation module 103 is configured to calculate signal features of the local spectrum and the time domain value;
the back transmission module 104 is configured to transmit the signal characteristics back to the computing center 200, so that the computing center 200 trains an initial model by using the signal characteristics to obtain an updated model.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3
Corresponding to the above method embodiment, the present disclosure further provides a wireless interference detection device, and a wireless interference detection device described below and a wireless interference detection method described above may be referred to in correspondence.
Referring to fig. 8, the electronic device 300 may include: a processor 301 and a memory 302. The electronic device 300 may also include one or more of a multimedia component 303, an input/output (I/O) interface 304, and a communication component 305.
The processor 301 is configured to control the overall operation of the electronic device 300, so as to complete all or part of the steps in the above-mentioned wireless interference detection method. The memory is used to store various types of data to support operation at the electronic device 300, such as instructions for any application or method operating on the electronic device 300 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and so forth. The Memory 302 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 303 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 302 or transmitted through the communication component 305. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 304 provides an interface between the processor 301 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 305 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described wireless interference detection method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above described radio interference detection method is also provided. For example, the computer readable storage medium may be the memory 302 described above comprising program instructions that are executable by the processor 301 of the electronic device 300 to perform the wireless interference detection method described above.
Example 4
Corresponding to the above method embodiment, the present disclosure also provides a readable storage medium, and a readable storage medium described below and a wireless interference detection method described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the radio interference detection method of the above-mentioned method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for wireless interference detection, comprising:
receiving and collecting wireless signal information, selecting an expression domain according to needs and constructing an expression F (x) of the wireless signal in the expression domain;
setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure FDA0003333684960000011
And
Figure FDA0003333684960000012
a corresponding time domain value;
calculating the said
Figure FDA0003333684960000013
And
Figure FDA0003333684960000014
signal characteristics of the time domain values of (a);
transmitting the signal characteristics back to a calculation center, and enabling the calculation center to perform model training by using the signal characteristics;
wherein, the
Figure FDA0003333684960000015
Can be divided intoEnergy concentration areas and edge areas;
for signals in the energy concentration area, a narrow window is taken for a key attention area, and wide windows are taken for the rest areas;
setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure FDA0003333684960000016
And
Figure FDA0003333684960000017
corresponding time domain values, including:
defining a sliding window function:
Figure FDA0003333684960000018
wherein the content of the first and second substances,
Figure FDA0003333684960000019
indicates the ith window, start(i)Denotes the start of the ith window, w(i)Indicates the width of the ith window,
Figure FDA00033336849600000110
respectively representing the internal coefficient and the external coefficient of the ith window, wherein x is the abscissa of the constructed F (x), and the type of x depends on the selected representation domain;
automatically intercepting the F (x) by using the sliding window function to obtain the F (x)
Figure FDA00033336849600000111
Figure FDA00033336849600000112
Wherein F (x) is the representation of the signal at the selected locationThe representation on the domain is carried out,
Figure FDA0003333684960000021
an i-th part (i ═ 1, 2, …, n) representing f (x);
to the above
Figure FDA0003333684960000022
And (3) performing correlation transformation calculation to obtain a corresponding time domain value:
Figure FDA0003333684960000023
wherein, F-1[·]Show that
Figure FDA0003333684960000024
An operation of transforming to a time domain;
wherein the relationship between the windows comprises a phase separation, a tangent or an intersection;
the starting point between the windows is set in an equal step length mode.
2. The method of claim 1, wherein the representation domain of the signal covers all representation domains used for signal detection analysis.
3. The method of claim 1, wherein a width of a window of the energy concentration region is less than or equal to a width of a window of the edge region.
4. The method of claim 1, wherein the calculating the radio interference is performed in a fixed manner
Figure FDA0003333684960000025
And
Figure FDA0003333684960000026
the signal characteristics of the time domain value can be selected according to actual needs, and all the signal characteristics can be selected.
5. The method of claim 1, wherein the step of transmitting the signal characteristics back to a computing center to enable the computing center to perform model training using the signal characteristics comprises:
obtaining mass signal characteristics through a field acquisition mode or a mode of transmitting the signal characteristics back to a computing center by utilizing a 4G/5G network or other possible modes, and providing the mass signal characteristics for the training of an initial model by the computing center;
deploying the initial model in an application scene, and in the actual application process of the model, using the characteristics of the newly acquired signals for incremental training of the initial model to obtain an updated model; the incremental training is a circularly repeated process, and a new model with better performance is obtained by continuously carrying out incremental training on the old model.
6. A wireless interference detection device is characterized by comprising an acquisition module, a preprocessing module, a feature calculation module and a return module;
the acquisition module is used for receiving and acquiring wireless signal information, selecting an expression domain according to needs and constructing an expression F (x) of the wireless signal in the expression domain;
setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure FDA0003333684960000031
And
Figure FDA0003333684960000032
a corresponding time domain value;
the feature calculation module is used for calculating the feature
Figure FDA0003333684960000033
And
Figure FDA0003333684960000034
signal characteristics of the time domain values of (a);
the back transmission module is used for transmitting the signal characteristics back to the calculation center, so that the calculation center performs model training by using the signal characteristics;
wherein, the
Figure FDA0003333684960000035
Can be divided into an energy concentration area and an edge area;
for signals with more concentrated energy, a narrower window is selected for a key attention area, and wider windows are selected for the rest areas;
setting a sliding window function, and automatically intercepting the F (x) by using the sliding window function to obtain a plurality of local parts of the F (x)
Figure FDA0003333684960000036
And
Figure FDA0003333684960000037
corresponding time domain values, including:
defining a sliding window function:
Figure FDA0003333684960000038
wherein the content of the first and second substances,
Figure FDA0003333684960000039
indicates the ith window, start(i)Denotes the start of the ith window, w(i)Indicates the width of the ith window,
Figure FDA00033336849600000310
respectively representing the internal coefficient and the external coefficient of the ith window, wherein x is the abscissa of the constructed F (x), and the type of x depends on the selected representation domain;
automatically intercepting the F (x) by using the sliding window function to obtain the F (x)
Figure FDA00033336849600000311
Figure FDA00033336849600000312
Wherein F (x) is the representation of the signal on the selected representation domain,
Figure FDA0003333684960000041
an i-th part (i ═ 1, 2, …, n) representing f (x);
to the above
Figure FDA0003333684960000042
And (3) performing correlation transformation calculation to obtain a corresponding time domain value:
Figure FDA0003333684960000043
wherein, F-1[·]Show that
Figure FDA0003333684960000044
And transforming to the time domain.
7. A wireless interference detection device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the radio interference detection method according to any of claims 1 to 5 when executing the computer program.
8. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the radio interference detection method according to any of claims 1 to 5.
CN202011599036.8A 2020-12-30 2020-12-30 Wireless interference detection method, device, equipment and readable storage medium Active CN112651349B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011599036.8A CN112651349B (en) 2020-12-30 2020-12-30 Wireless interference detection method, device, equipment and readable storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011599036.8A CN112651349B (en) 2020-12-30 2020-12-30 Wireless interference detection method, device, equipment and readable storage medium

Publications (2)

Publication Number Publication Date
CN112651349A CN112651349A (en) 2021-04-13
CN112651349B true CN112651349B (en) 2022-01-07

Family

ID=75363910

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011599036.8A Active CN112651349B (en) 2020-12-30 2020-12-30 Wireless interference detection method, device, equipment and readable storage medium

Country Status (1)

Country Link
CN (1) CN112651349B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116304580A (en) * 2023-05-10 2023-06-23 工业富联(佛山)产业示范基地有限公司 Data detection method, electronic device and computer readable storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102272617A (en) * 2008-12-30 2011-12-07 真实定位公司 Interference detection, characterization and location in a wireless communications or broadcast system
CN109655794A (en) * 2018-11-14 2019-04-19 上海无线电设备研究所 A kind of detection recognition method of narrowband self-defence noise jamming
CN110808930A (en) * 2019-11-06 2020-02-18 紫光展锐(重庆)科技有限公司 Channel estimation method, device and storage medium
CN111259798A (en) * 2020-01-16 2020-06-09 西安电子科技大学 Modulation signal identification method based on deep learning

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1914898A1 (en) * 2006-10-16 2008-04-23 STMicroelectronics N.V. Method and apparatus for detecting narrowband interferences within a multi-carrier signal, in particular an OFDM modulated UWB signal
US20180070364A1 (en) * 2016-09-08 2018-03-08 Qualcomm Incorporated Interference mitigation via space-time subspace projection
JP7353006B2 (en) * 2017-12-01 2023-09-29 オリジン ワイヤレス， インコーポレイテッド Apparatus, system and method for recognizing events based on wireless signals
CN110856206B (en) * 2018-08-20 2022-11-01 富士通株式会社 Interference identification method and device and terminal equipment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102272617A (en) * 2008-12-30 2011-12-07 真实定位公司 Interference detection, characterization and location in a wireless communications or broadcast system
CN109655794A (en) * 2018-11-14 2019-04-19 上海无线电设备研究所 A kind of detection recognition method of narrowband self-defence noise jamming
CN110808930A (en) * 2019-11-06 2020-02-18 紫光展锐(重庆)科技有限公司 Channel estimation method, device and storage medium
CN111259798A (en) * 2020-01-16 2020-06-09 西安电子科技大学 Modulation signal identification method based on deep learning

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
无线电同频干扰的检测及识别研究;潘文分;《中国优秀硕士学位论文全文数据库 信息科技辑》;20200715(第07期);第I136-499页，正文第4、20-24、33页 *

Also Published As

Publication number Publication date
CN112651349A (en) 2021-04-13

Similar Documents

Publication Publication Date Title
US9990347B2 (en) Borderless table detection engine
CN110245132B (en) Data anomaly detection method, device, computer readable storage medium and computer equipment
US20130191366A1 (en) Pattern Matching Engine
CN113450147B (en) Product matching method, device, equipment and storage medium based on decision tree
US20230244862A1 (en) Form processing method and apparatus, device, and storage medium
EP4134921A1 (en) Method for training video label recommendation model, and method for determining video label
CN103854661A (en) Method and device for extracting music characteristics
CN112651349B (en) Wireless interference detection method, device, equipment and readable storage medium
Van Balen et al. Corpus Analysis Tools for Computational Hook Discovery.
Prado et al. Maximum entropy principle in recurrence plot analysis on stochastic and chaotic systems
Kochetkov et al. Smart cities and 5G networks: An emerging technological area?
CN110737820B (en) Method and apparatus for generating event information
Huang et al. Multi-format speech biohashing based on spectrogram
Dayal et al. Lightweight deep convolutional neural network for background sound classification in speech signals
KR102588688B1 (en) Method and system for analyzing data
Chowdhury et al. Crime monitoring from newspaper data based on sentiment analysis
CN114598832B (en) Dual-mode cloud law enforcement recording terminal control method, terminal and device
CN110717182A (en) Webpage Trojan horse detection method, device and equipment and readable storage medium
Chen et al. Similarity fusion scheme for cover song identification
CN114547257B (en) Class matching method and device, computer equipment and storage medium
US20210256456A1 (en) Work log posting system
CN113010782A (en) Demand amount acquisition method and device, electronic equipment and computer readable medium
CN111950282A (en) Network behavior characteristic-based line expansion analysis method and device
KR101640426B1 (en) Apparatus for analysis of mobile big data
Wang Application of logo standardization design system based on cloud service

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant