CN111010240B - Radio frequency gene library system and illegal radio wave detection system - Google Patents

Radio frequency gene library system and illegal radio wave detection system Download PDF

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CN111010240B
CN111010240B CN201910253245.8A CN201910253245A CN111010240B CN 111010240 B CN111010240 B CN 111010240B CN 201910253245 A CN201910253245 A CN 201910253245A CN 111010240 B CN111010240 B CN 111010240B
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CN111010240A (en
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岳新东
陶洪波
彭潇
吕玉琦
谢军
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STATE RADIO MONITORING CENTER TESTING CENTER
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Abstract

The invention provides a radio frequency gene library system and a violation radio wave detection system, wherein the radio frequency gene library system comprises: the individual dividing device is configured to determine the range of the target geographic area, determine an individual dividing mode according to the communication environment of the target geographic area, and divide the target geographic area into a plurality of individuals according to the individual dividing mode; the wireless detector is configured to detect each individual and acquire basic radio frequency data of each individual; the radio frequency gene library generating device is configured to perform deep neural network calculation based on the basic radio frequency data of each individual to obtain the radio frequency gene of each individual; and storing the radio frequency gene of each individual to generate a radio frequency gene library of the target geographical area. The technical scheme provided by the invention can realize the intellectualization and automation of radio monitoring work, reduce unnecessary waste of people, property, materials and time, improve the work efficiency of radio management and perfect a radio management system.

Description

Radio frequency gene library system and illegal radio wave detection system
Technical Field
The invention relates to the field of wireless communication, in particular to a radio frequency gene library system and a violation radio wave detection system.
Background
In the traditional radio monitoring work, technical personnel mainly analyze the radio environment monitoring data to judge whether illegal violation signals exist. The traditional radio monitoring has high dependence on technicians on accuracy and timeliness, and the professional quality and related working experience of the technicians have high influence on the analysis and judgment results of the monitoring data; in addition, due to the randomness and discontinuity of the operation of the radio equipment, technicians are required to analyze the monitoring data from time to time, which results in the defects of large workload, high repeatability, poor timeliness and the like of radio management.
Disclosure of Invention
The present invention provides a radio frequency gene bank system and a violation radio wave detection system to overcome the above problems or at least partially solve the above problems.
According to an aspect of the present invention, there is provided a radio frequency gene bank system comprising:
the individual dividing device is configured to determine the range of a target geographic area, determine an individual dividing mode according to the communication environment of the target geographic area, and divide the target geographic area into a plurality of individuals according to the individual dividing mode;
a wireless detector configured to detect for each of the individuals, obtaining basic radio frequency data for each of the individuals;
the radio frequency gene library generating device is configured to perform deep neural network calculation based on the basic radio frequency data of each individual to obtain the radio frequency gene of each individual; and storing the radio frequency gene of each individual to generate a radio frequency gene library of the target geographical area.
Optionally, the radiofrequency gene comprises at least one of: longitude and latitude coordinates, coverage area range, radio monitoring frequency band, total power of a monitoring receiving signal, modulation mode of the monitoring receiving signal and a frequency spectrogram of the monitoring receiving signal.
Optionally, the individual dividing apparatus is further configured to:
when the communication environment of the target geographic area is mainly public network communication, dividing the individuals in a cellular form;
when the communication environment of the target geographic area is mainly direct communication, dividing the individuals in a grid form;
when the primary communication mode of the communication environment of the target geographical area cannot be determined, the individuals are divided in a cellular form.
Optionally, the radio frequency gene library generating device is further configured to:
initializing a deep neural network parameter by adopting a greedy layer-by-layer pre-training algorithm based on the basic radio frequency data;
and training a deep neural network model, adjusting parameters of the deep neural network layer by layer, and fitting the deep neural network model to an approximate real model.
Optionally, the radio frequency gene library generating device is further configured to:
transmitting the basic radio frequency data to an input layer of a deep neural network to serve as observable data of a first layer of a hidden layer;
training the observable data of the first layer of the hidden layer by adopting an unsupervised learning algorithm to generate initial parameters of the first layer of the hidden layer;
taking the initial parameters of the first layer of the hidden layer as observable data of the second layer of the hidden layer, and continuing to train the second layer of the hidden layer by adopting the unsupervised learning algorithm to generate the initial parameters of the second layer of the hidden layer;
repeating the steps until all layers of the hidden layer are initialized to obtain initialization parameters of each layer;
and inputting the last layer of initialization parameters of the hidden layer into an output layer of the deep neural network, and initializing the parameters of the output layer by adopting a supervised learning algorithm.
Optionally, the radio frequency gene library generating device is further configured to:
and repeatedly executing the steps of obtaining the basic radio frequency data and calculating the corresponding radio frequency genes, and updating the radio frequency gene library.
According to another aspect of the present invention, there is also provided a violation radio wave detection system including:
any of the above radio frequency gene bank systems;
and the wireless detection device is configured to perform radio wave detection in each individual, acquire the radio frequency parameters of the radio waves existing in each individual, and determine whether illegal radio waves exist or not by comparing the detected radio frequency parameters of the radio waves existing in each individual with the radio frequency genes of the individuals in the radio frequency gene bank.
The invention provides a system which can realize the intellectualization and automation of radio monitoring work on the basis of a radio management integrated platform, thereby reducing unnecessary waste of people, property, things and time, improving the work efficiency of radio management and perfecting a radio management system.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a block diagram of the architecture of a radio frequency gene bank system according to an embodiment of the present invention;
FIG. 2 is a schematic representation of a radio frequency gene bank according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of partitioning individuals in a cellular format in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of partitioning individuals in a grid according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a deep learning neural network;
fig. 6 is a block diagram of the structure of a violation radio wave detection system according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that the technical features of the embodiments and the preferred embodiments of the present invention can be combined with each other without conflict.
FIG. 1 is a block diagram of a radio frequency gene library system according to an embodiment of the present invention. As shown in fig. 1, the rf gene bank system 100 according to the embodiment of the present invention includes:
the individual dividing device 102 is configured to determine the range of the target geographic area, determine an individual dividing mode according to the communication environment of the target geographic area, and divide the target geographic area into a plurality of individuals according to the individual dividing mode;
a wireless detector 104 configured to detect for each individual, obtaining basic radio frequency data of each individual;
a radio frequency gene library generating device 106 configured to perform deep neural network calculation based on the basic radio frequency data of each individual to obtain a radio frequency gene of each individual; and storing the radio frequency gene of each individual to generate a radio frequency gene library of the target geographical area.
Aiming at the defects in the prior art, the embodiment of the invention provides the radio frequency gene bank system 100 which can realize the intellectualization and automation of radio monitoring work on the basis of a radio management integrated platform, thereby reducing unnecessary waste of people, property, things and time, improving the work efficiency of radio management and perfecting a radio management system.
In the technical scheme provided by the embodiment, the concept of the radio frequency gene is provided based on the radio frequency fingerprint. Radio Frequency Fingerprint (RFF) of Radio equipment is the essential characteristics of the physical layer of the equipment extracted by analyzing the output signal of the Radio equipment, including the information of the operating Frequency, signal power, modulation mode, etc. of the Radio equipment. The radio frequency gene of the radio communication environment refers to the basic characteristics of the radio communication environment in a certain geographic area range, including the longitude and latitude coordinates (ignoring altitude), the coverage area range, the radio monitoring frequency band, the total power of the monitored received signal, the modulation mode of the monitored received signal, the frequency spectrum schematic diagram of the monitored received signal, and the like of the radio communication environment, and the radio frequency gene library is the set of all the radio frequency genes in a certain geographic area range, as shown in fig. 2. The radio frequency gene library covering the radio monitoring frequency band is constructed, standard prior information of a radio communication environment in a certain geographic area can be provided, and the efficiency and accuracy of radio management work are improved.
The establishment of the radio frequency gene library is a progressive and long process, a radio management mechanism is required to store monitoring data with a certain magnitude in the initial construction, and the radio frequency gene library data is continuously updated in the later period according to the radio monitoring data. The coverage range of the monitoring equipment is used as the coverage range of an individual, the monitoring result of the radio-magnetic environment in the individual is used as the basic radio frequency data of the individual, and the basic radio frequency data of each individual is trained through an artificial intelligence technology to obtain the radio frequency gene of each individual.
In this embodiment, "individual" is a basic unit for determining a radio frequency gene, and is a smaller geographical area obtained by dividing a geographical area using a specific geometric shape. Preferably, the individual dividing means 102 may be configured to divide the individuals using:
when the communication environment of the target geographic area is mainly public network communication, dividing individuals in a cellular mode;
when the communication environment of the target geographic area is mainly direct communication, dividing the individuals in a grid form;
when the primary communication mode of the communication environment of the target geographical area cannot be determined, the individuals are divided in the form of cells.
In this embodiment, the individual dividing apparatus 102 preferentially adopts an adaptive individual dividing method, and can adaptively adopt different individual dividing methods under different communication environments. When the communication environment is mainly public network communication, dividing the individuals into honeycomb shapes, as shown in fig. 3; when the communication environment is mainly direct communication, dividing the individuals in a grid shape, as shown in fig. 4; when it is not possible to determine, the individuals are divided in a honeycomb shape.
In order to determine the rf genes of the individual as accurately as possible, in this embodiment, the rf gene library generating device 106 uses a deep learning algorithm to calculate the rf genes of the individual. The deep learning is an artificial intelligence learning method for realizing complex function fitting by learning a deep nonlinear network structure, which is essentially a deep neural network learning algorithm, wherein the deep neural network structure comprises an input layer, a hidden layer and an output layer, and is shown in fig. 5. The input layer is input data for learning, the hidden layer is used for extracting and analyzing the characteristics of the input data, and the output layer is output data for learning. The completeness of the deep neural network enables the deep neural network to represent any function, and therefore, the fitting of any function can be achieved through multiple layers of parameters and different network structures.
The greedy layer-by-layer pre-training algorithm is an unsupervised learning algorithm, and is an unsupervised deep learning method based on the greedy layer-by-layer pre-training algorithm. A semi-supervised algorithm of a greedy layer-by-layer pre-training algorithm is adopted, an unsupervised training learning method is adopted for a hidden layer, a supervised learning method is adopted for an output layer, and a better learning result can be obtained.
Based on the above-mentioned technology, preferably, the rf gene bank generating device 106 may be configured to perform deep neural network calculation by:
initializing the deep neural network parameters by adopting a greedy layer-by-layer pre-training algorithm based on the basic radio frequency data
And (3) carrying out deep neural network model training, adjusting parameters of the deep neural network layer by layer, and fitting the deep neural network model to an approximate real model.
And the rf gene bank generating device 106 may be configured to perform the initialization process described above by:
transmitting the basic radio frequency data to an input layer of a deep neural network to be used as observable data of a first layer of a hidden layer;
training observable data of the first layer of the hidden layer by adopting an unsupervised learning algorithm to generate initial parameters of the first layer of the hidden layer;
taking the initial parameters of the first layer of the hidden layer as observable data of the second layer of the hidden layer, and continuing to train the second layer of the hidden layer by adopting an unsupervised learning algorithm to generate the initial parameters of the second layer of the hidden layer;
repeating the steps until all layers of the hidden layer are initialized to obtain initialization parameters of each layer;
and inputting the last layer of initialization parameters of the hidden layer into an output layer of the deep neural network, and initializing the parameters of the output layer by adopting a supervised learning algorithm.
Preferably, in order to obtain more accurate data, after the rf gene library of the target geographic area is generated, the rf gene library generating device 106 may be further configured to repeatedly perform the steps of acquiring the basic rf data and calculating the corresponding rf gene, so as to update the rf gene library.
As can be seen from the above description, the embodiment of the present invention provides a technical solution:
1. establishing a radio frequency gene library of a radio communication environment in a certain geographical area: providing a standard radio frequency gene template (radio frequency gene library) in a certain geographic area and providing standard prior information of a radio communication radio frequency environment in a certain area. The radio frequency gene of the radio communication environment refers to basic characteristics of the radio communication environment in a certain geographic area range, and comprises information of the geographic position, the coverage area range, the working frequency, the signal power, the modulation mode and the like of the radio communication environment; and the radio frequency gene library is a set of all radio frequency genes in a certain geographic region.
2. Self-adaptive individual division mode: and an adaptive individual division mode under different communication environments is provided. When the communication environment is mainly public network communication, dividing the individuals into honeycomb shapes, as shown in fig. 2; when the communication environment is mainly direct communication, dividing the individuals in a grid shape, as shown in fig. 3; when it cannot be determined, the individuals are divided in a honeycomb shape. Taking a certain place in a certain area as an example, firstly selecting an individual dividing mode (honeycomb or grid shape) according to a radio communication environment, and then building a radio frequency gene of the area of the place by using a semi-supervised learning algorithm.
4. Intelligence and automation of radio management work: based on software and hardware infrastructures such as a radio monitoring system, a radio detection system and a simulation system, a user realizes intellectualization and automation of radio monitoring work on the basis of a radio management integrated platform, and a semi-supervised deep learning method adopting a greedy layer-by-layer pre-training algorithm is provided for constructing a radio frequency gene library covering a certain geographic area.
5. Maximizing the resource utilization rate: unnecessary waste of people, property, things and time is reduced, the working efficiency of radio management is improved, and a radio management system is perfected.
The above embodiment is described below by way of a specific preferred embodiment. The preferred embodiment provides a self-adaptive individual division radio frequency gene library system built based on a semi-supervised deep learning method, which can realize the following functions:
1) and determining individual division modes. When the communication environment is mainly public network communication, dividing the individuals in a honeycomb manner; when the communication environment is mainly direct communication, dividing the individuals in a grid shape; when it cannot be determined, the individuals are divided in a honeycomb shape.
2) Determining the size of the coverage area of the divided individuals;
3) determining the geographical area range to be covered by the radio frequency gene library;
4) dividing the geographical area determined in 3) in a manner determined in 1) by taking an individual as a basic unit, as shown in FIG. 2 or FIG. 3;
5) the monitoring result of each individual is only used as a group of basic radio frequency data of the individual, and the basic radio frequency data collected in the construction of the radio frequency gene library ensures that the influence of factors such as extreme weather on the monitoring result is fully considered;
6) and initializing the deep neural network. Initializing the parameters of the deep neural network by adopting a greedy layer-by-layer pre-training algorithm, wherein the initialization process is as follows:
a) transmitting individual monitoring data (namely basic radio frequency data) x to an input layer of the deep neural network, wherein the observable data h0(x) is taken as a first layer of a hidden layer, namely h0(x) is x;
b) training observable data h0(x) of the first layer of the hidden layer by adopting an unsupervised learning algorithm, and generating initial parameters R1(h0(x)) of the first layer of the hidden layer;
c) taking the hidden layer first layer initial parameter R1(h0(x)) as observable data h1(x) of the hidden layer second layer, namely h1(x) is R1(h0(x)), continuing to train the hidden layer second layer by adopting an unsupervised learning algorithm, and generating an initial parameter R2(h1(x)) of the hidden layer second layer;
d) repeating the step c) to initialize all layers of the hidden layer to obtain initialization parameters R1(h0(x)), R2(h1(x)), R3(h2(x)), … and RL (hL-1(x)) of each layer;
e) inputting a final layer initialization parameter RL (hL-1(x)) of the hidden layer into the output layer, and initializing the output layer parameter by adopting a supervised learning algorithm;
7) and (5) deep neural network model training. Model training, namely a process of adjusting parameters layer by layer and gradually fitting, and finely adjusting the whole deep neural network by adopting a supervised learning algorithm to enable a neural network model to approach a real model;
8) outputting the deep neural network model to obtain individual radio frequency genes;
9) training radio frequency genes of all individuals in a geographic region range to obtain a radio frequency gene library;
10) repeat functions 5) -7), continually optimizing and supplementing the radiofrequency gene bank.
In another embodiment of the present invention, based on the above-mentioned rf gene bank system, there is further provided a violation radio wave detection system, as shown in fig. 6, including:
the above-described radio frequency gene bank system 100;
a wireless detection device 200 configured to perform radio wave detection in each individual, and acquire a radio frequency parameter of a radio wave existing in the individual, such as a radio frequency fingerprint; and determining whether illegal radio waves exist or not by comparing the detected radio frequency parameters of the radio waves existing in each individual with the radio frequency genes of the individuals in the radio frequency gene library.
When there exists a radio wave in an individual whose radio frequency parameters (such as radio frequency fingerprint) are greatly different from the radio frequency genes of the individual, the radio break is suspected to have illegal violation. Such operations and determinations can be automatically performed by the illegal radio wave detection system.
As can be seen from the above description, the technical solution provided by the embodiment of the present invention can be well applied to a radio monitoring system. Firstly, the technical scheme provided by the embodiment of the invention provides a concept of a radio frequency gene library, and suspected illegal radio transmitting equipment in a certain monitored geographic area can be rapidly found by comparing a radio monitoring result with related information of the radio frequency gene library; secondly, the radio frequency gene library system provided by the embodiment of the invention adopts a self-adaptive individual division method, and different individual division modes are selected according to different communication environments; thirdly, the radio frequency gene library system provided by the embodiment of the invention adopts a propagation path model map constructed based on a semi-supervised deep learning method, the essence of the deep learning algorithm is a deep neural network learning algorithm, a semi-supervised algorithm of a greedy layer-by-layer pre-training algorithm is adopted to adopt an unsupervised training mode for a hidden layer, a supervised learning mode is adopted for an output layer, and a better learning result can be obtained; finally, from the aspect of resource utilization, the technical scheme provided by the embodiment of the invention realizes the effect of saving human, property, material and time cost.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced within the spirit and principle of the present invention; such modifications or substitutions do not depart from the scope of the present invention.

Claims (6)

1. A radio frequency gene bank system comprising:
the individual dividing device is configured to determine the range of a target geographic area, determine an individual dividing mode according to the communication environment of the target geographic area, and divide the target geographic area into a plurality of individuals according to the individual dividing mode;
a wireless detector configured to detect for each of the individuals, acquiring basic radio frequency data for each of the individuals;
the radio frequency gene library generating device is configured to perform deep neural network calculation based on the basic radio frequency data of each individual, acquire the radio frequency gene of each individual, store the radio frequency gene of each individual and generate a radio frequency gene library of the target geographic area; wherein
The radiofrequency gene comprises at least one of: longitude and latitude coordinates, coverage area range, radio monitoring frequency band, total power of monitoring received signals, modulation mode of monitoring received signals and spectrogram of monitoring received signals;
the rf gene bank is a collection of all rf genes of the target geographic region.
2. The system of claim 1, wherein the individual partitioning apparatus is further configured to:
when the communication environment of the target geographic area is mainly public network communication, dividing the individuals in a cellular form;
when the communication environment of the target geographic area is mainly direct communication, dividing the individuals in a grid form;
when the primary communication mode of the communication environment of the target geographical area cannot be determined, the individuals are divided in a cellular form.
3. The system of claim 1, wherein the radio frequency gene bank generating device is further configured to:
initializing a deep neural network parameter by adopting a greedy layer-by-layer pre-training algorithm based on the basic radio frequency data;
and training a deep neural network model, adjusting parameters of the deep neural network layer by layer, and fitting the deep neural network model to an approximate real model.
4. The system of claim 3, wherein the radio frequency gene bank generating device is further configured to:
transmitting the basic radio frequency data to an input layer of a deep neural network to serve as observable data of a first layer of a hidden layer;
training observable data of the first layer of the hidden layer by adopting an unsupervised learning algorithm to generate initial parameters of the first layer of the hidden layer;
taking the initial parameters of the first layer of the hidden layer as observable data of the second layer of the hidden layer, and continuing to train the second layer of the hidden layer by adopting the unsupervised learning algorithm to generate the initial parameters of the second layer of the hidden layer;
repeating the steps until all layers of the hidden layer are initialized to obtain initialization parameters of each layer;
and inputting the last layer of initialization parameters of the hidden layer into an output layer of the deep neural network, and initializing the parameters of the output layer by adopting a supervised learning algorithm.
5. The system of claim 1, wherein the radio frequency gene bank generating device is further configured to:
and repeatedly executing the steps of obtaining the basic radio frequency data and calculating the corresponding radio frequency genes, and updating the radio frequency gene library.
6. A violation radio wave detection system comprising:
the radiofrequency gene bank system of any one of claims 1 to 5;
and the wireless detection device is configured to perform radio wave detection in each individual, acquire the radio frequency parameters of the radio waves existing in each individual, and determine whether illegal radio waves exist or not by comparing the detected radio frequency parameters of the radio waves existing in each individual with the radio frequency genes of the individuals in the radio frequency gene bank.
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