CN113341382A - Detection, identification and development integrated platform for intelligent cognitive radio - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/04—Systems determining presence of a target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
Abstract
The invention discloses a detection and identification integrated development platform of intelligent cognitive radio based on three key modules of a front end of a broadband reconfigurable radio frequency receiver, frequency rapid detection and intelligent signal cognition. The intelligent cognitive radio system development platform is provided for the industry aiming at the problems of high development cost, high difficulty and difficulty in verification of the existing broadband wireless cognitive module and system, and has practical application value. The platform is organically connected with the three key modules, so that feedback and real-time adjustment of three links of receiving, sensing and knowing can be realized, and the anti-interference and signal cognition capabilities of the receiver are improved.
Description
Technical Field
The invention relates to the field of wireless communication/radar/electronic countermeasure, in particular to a receiver front-end device and a baseband signal processing algorithm.
Background
Modern electromagnetic wave communication environment is increasingly complex, and the quality of communication signals needs to be improved urgently. The present patent seeks a receiver simulation environment based on software implementation that can quickly adapt to signal characteristics.
The document "zhuantongsu. radio frequency experiment platform-design of radio frequency front end of receiver [ D ]. university of electronic technology 2012" (reference 1) introduces a design scheme of each main module in the radio frequency receiving front end circuit in the experiment platform, including a radio frequency filter, a low noise amplifier, a local vibration source, and the like, and uses ADS2008 software to respectively complete the simulation of schematic diagrams and layout of a hairpin type band pass filter and an elliptic function low pass filter.
Document "zhangli, FPGA implementation of wideband radio frequency digital receiver experimental platform [ D ]. university of electronic technology, 2002" (reference 2) uses the online programming characteristic of FPGA, and each module can be independently debugged without changing the hardware structure, and the FPGA implementation of a multi-term filtering down-conversion structure is proposed.
The document "Liuyangang" a software radio platform design and realization based on FPGA [ D ]. Sichuan: 2016 (reference 3) of university of electronic technology initially establishes a software radio platform based on FPGA, which supports a PC end to directly perform real-time online reconfiguration on a radio frequency module and an FPGA functional module.
The literature "Yangjie, summer, communication signal modulation identification research based on convolutional neural networks [ J ] computer measurement and control, 2020" (ref 4) describes a method of combining complex baseband signals with automatic modulation identification of convolutional neural networks, which realizes identification and classification of seven digital communication signal types, 2FSK, 4FSK, BPSK, QPSK and 64 QAM.
Document "zhang luo. TDC-based all-digital phase-locked loop research and design [ D ]. jiangsu: nanjing post and telecommunications university, 2014 "(reference 5) introduces an all-digital phase-locked loop based on TDC, wherein the output frequency range of the phase-locked loop is 64 MHz-640 MHz, the frequency division coefficient range is 27-70, and when the reference frequency is 10MHz, the loop locking time is within 12 mus.
The above documents relate to the design of multiple integrated common signals of radar-electronic warfare-communication, the design of an integrated receiver of radar communication, the design of an integrated radio frequency front end of radar communication, etc., and there are also documents researching a software radio hardware platform including a transmitting/receiving baseband processing module, etc., and a verification hardware platform of a CSS communication detection system, but the systems or platforms introduced by these documents are all different from the broadband receiving module including an automatic design (EDA) design of a visual interactive platform auxiliary circuit, and an integrated device for intelligent signal recognition and fast frequency detection. The techniques described in the prior art documents are developed for a single environment and have no reconfigurability in different reception environments.
Disclosure of Invention
In view of the defects of the prior art, the detection and identification integrated development platform for the smart cognitive radio, which is designed by the invention, provides a development idea of a signal smart cognitive system based on a core idea of detection-reception-identification integrated collaborative design, deals with wide-range unknown signal cognition in space, and is applied to the cognitive radio application fields of full-spectrum access communication, radar/electronic countermeasure integration, space energy collection and the like so as to meet various application scenarios of communication/radar/electronic countermeasure and the like.
In order to achieve the purpose, the technical scheme of the invention is as follows: a detection, identification and development integrated platform for intelligent cognitive radio comprises a broadband receiving module, a signal intelligent cognitive module and a frequency detection module; the broadband receiving module is automatically designed through a visual interaction platform auxiliary circuit; the receiving module internally comprises a low noise amplifier, a mixer, a filter, a programmable gain amplifier and a digital-to-analog converter basic component, the architecture can select a super heterodyne receiver or a zero intermediate frequency receiver, and simultaneously supports user-defined circuit architecture and customized technical indexes; when a user inputs signal data in a frequency domain form, the signal data can display input and output signal waveforms after being processed by a receiver program;
the signal intelligent cognitive module processes signal data received by the broadband receiving module and rapidly identifies signal types and modulation modes based on an image processing/neural network algorithm; through time-frequency diagram and constellation diagram analysis, various signal modulation modes of LFM, SFM, BPSK, QPSK,8PSK, 16QAM and CDMA are distinguished;
the frequency detection module utilizes the all-digital phase-locked loop to quickly lock frequency, quickly detects the frequency of a high-power signal in a space through the frequency rough estimation module and the frequency precise measurement and tracking and frequency estimation algorithm module based on the all-digital phase-locked loop, and feeds the result back to the broadband receiving module to dynamically adjust the parameters of the internal components of the broadband receiving module according to different conditions.
The broadband receiving module comprises a radio frequency signal reading function and is used for reading signal sampling data stored in a computer memory in a text form; and the radio frequency receiver simulation function is used for processing signal data on a time/frequency domain according to the receiver type and key parameters set by a user, so that the reconfigurable and behavior level simulation functions of the receiver are realized.
The signal intelligent cognitive module comprises a digital signal data reading function and can read the digital signal processed by the broadband receiving module; and (3) extracting the characteristics of a signal time-frequency diagram and a constellation diagram by using an image processing/neural network algorithm, and distinguishing the signal type and the modulation mode.
The frequency detection module comprises a frequency rough estimation module, a radio frequency signal frequency accurate measurement and tracking module based on an all-digital phase-locked loop and a frequency estimation algorithm module; firstly, a frequency rough estimation module is used for realizing frequency rough estimation, and then a frequency accurate measurement and tracking module is used for realizing signal frequency accurate measurement; the function of the frequency estimation algorithm module is to obtain the frequency of the current input signal according to the frequency control word of the numerically controlled oscillator. The broadband receiving module is implemented on a computer by using a programming language C + +, and combining with a programming tool.
The intelligent signal cognition module is realized on a computer by using a programming language C + +, and combining with a programming tool. Wherein the frequency detection module is implemented on the computer using programming language C.
The frequency detection module is realized on the FPGA by using a hardware description language Verilog HDL.
Compared with the prior art, the invention has the following advantages: 1) the scheme can realize the selection of the broadband radio frequency receiver architecture and the establishment of various technical indexes of basic components in the receiver, greatly reduce the calculated amount in the process of designing the circuit by radio frequency engineers, shorten the design time, improve the working efficiency and provide a visual automatic aided design interface for the radio frequency engineers. 2) The scheme can realize accurate measurement of signal frequency in a broadband range, and has the advantages of simple circuit structure, strong anti-interference capability and short measurement time. The problems of small frequency measurement range, low measurement precision, high device requirement and the like of the existing frequency measurement technology are solved. 3) The scheme integrates the traditional and self-innovative advanced algorithm group for signal characteristic analysis, combines signal processing and image enhancement ideas aiming at the time-frequency domain and vector domain analysis of baseband signals, integrates image processing and signal processing technologies and indirectly enhances the signal-to-noise ratio. Classifying linear frequency modulation signals, sinusoidal frequency modulation signals and the like by extracting curve form characteristics according to the obtained time-frequency image; adopting a constellation diagram to identify and classify the digital modulation signals by using a clustering analysis and edge detection algorithm; and dynamically adjusting the rest modules according to the signal type, thereby improving the overall self-adaptive capacity of the system.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the apparatus of the present invention;
FIG. 2 is a schematic illustration of a main interface of the integrated platform;
fig. 3 is a schematic diagram of the intermediate process of the broadband receiving module.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, which is defined in the appended claims, as may be amended by those skilled in the art upon reading the present invention, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Example 1: referring to fig. 1-3, a detection, identification and development integrated platform for smart cognitive radio includes a broadband receiving module, a signal smart cognitive module and a frequency detection module;
the broadband receiving module is automatically designed through a visual interaction platform auxiliary circuit; the receiving module internally comprises a low noise amplifier, a mixer, a filter, a programmable gain amplifier and a digital-to-analog converter basic component, the architecture can select a super heterodyne receiver or a zero intermediate frequency receiver, and simultaneously supports user-defined circuit architecture and customized technical indexes; when a user inputs signal data in a frequency domain form, the signal data can display input and output signal waveforms after being processed by a receiver program;
the signal intelligent cognitive module processes signal data received by the broadband receiving module and rapidly identifies signal types and modulation modes based on an image processing/neural network algorithm; through time-frequency diagram and constellation diagram analysis, various signal modulation modes of LFM, SFM, BPSK, QPSK,8PSK, 16QAM and CDMA are distinguished;
the frequency detection module utilizes the all-digital phase-locked loop to quickly lock frequency, quickly detects the frequency of a high-power signal in a space through the frequency rough estimation module and the frequency precise measurement and tracking and frequency estimation algorithm module based on the all-digital phase-locked loop, and feeds the result back to the broadband receiving module to dynamically adjust the parameters of the internal components of the broadband receiving module according to different conditions. The broadband receiving module comprises a radio frequency signal reading function and is used for reading signal sampling data stored in a computer memory in a text form; and the radio frequency receiver simulation function is used for processing signal data on a time/frequency domain according to the receiver type and key parameters set by a user, so that the reconfigurable and behavior level simulation functions of the receiver are realized. The signal intelligent cognitive module comprises a digital signal data reading function and can read the digital signal processed by the broadband receiving module; and (3) extracting the characteristics of a signal time-frequency diagram and a constellation diagram by using an image processing/neural network algorithm, and distinguishing the signal type and the modulation mode. The frequency detection module comprises a frequency rough estimation module, a radio frequency signal frequency accurate measurement and tracking module based on an all-digital phase-locked loop and a frequency estimation algorithm module; firstly, a frequency rough estimation module is used for realizing frequency rough estimation, and then a frequency accurate measurement and tracking module is used for realizing signal frequency accurate measurement; the function of the frequency estimation algorithm module is to obtain the frequency of the current input signal according to the frequency control word of the numerically controlled oscillator. The broadband receiving module is realized on a computer by using a programming language C + +, the signal intelligent cognition module is realized on the computer by combining a programming tool, the frequency detection module is realized on the computer by using the programming language C, and the frequency detection module is realized on the FPGA by using a hardware description language Verilog HDL. The working process is as follows:
as shown in fig. 1, the device includes a broadband rf receiving module, a signal fast frequency detection module, and a signal intelligent identification module. Based on three modules of sensing and knowing, the modules realize feedback and real-time adjustment to form organic connection, and realize signal receiving with high signal-to-noise ratio and signal identification with high accuracy. The broadband radio frequency receiving module realizes wide-range reconfigurable radio frequency signal receiving and comprises a low noise amplifier, a mixer, a filter, a programmable gain amplifier and an analog to digital converter (ADC). The method comprises the steps of receiving unknown signals in a wide range, and transmitting the received signals to a signal fast frequency detection module and a signal intelligent identification module. Adjusting corresponding parameters of a receiver according to the frequency of the received signals input by the other signal rapid frequency detection modules by using a reconfigurable principle to carry out trapping; according to the signal modulation type inputted by the signal intelligent identification module, the receiver structure and index are adjusted. The signal fast frequency detection module comprises a frequency rough estimation module, a radio frequency signal frequency accurate measurement lock tracking module based on an All Digital Phase-locked Loop (ADPLL) and a frequency estimation algorithm module. The input signal is connected to a frequency rough estimation module to adjust the input signal to be in a proper frequency range. The frequency of the radio frequency signal based on the ADPLL is accurately measured, and a lock tracking module locks the frequency of the input signal after frequency division. The frequency estimation algorithm module takes the frequency dividing ratio of the frequency rough estimation module and the ADPLL as input, calculates to obtain a frequency estimation value of the input signal FREF, and feeds the value back to the broadband radio frequency receiving module. The signal intelligent identification module comprises a time frequency information extraction module and a time frequency graph analysis module. The time-frequency information extraction module carries out frequency estimation and time-frequency transformation on the input signal and outputs a time-frequency graph of the input signal. The time-frequency image analysis module extracts the characteristics of the input image through an image processing technology, carries out classification and identification through extracting the characteristics of a curve form and a constellation diagram in the image, outputs the modulation mode of the input signal and feeds back the result to the broadband radio frequency receiving module.
The invention relates to an implementation mode of a detection and identification integrated development platform for intelligent cognitive radio, which comprises the following steps:
and after the software is started, popping up a software main interface. A user may select a source file of data therein as a representation of the input radio frequency signal, and the data type of the source file-either frequency domain data or time domain data. The two different data types correspond to different processing algorithms and mapping patterns. Clicking the drawing button after selecting a source file can display a visual representation of the data in the currently selected file.
Firstly, a fast frequency detection module processes signal source data. The broadband radio frequency signal frequency detection and tracking device based on the ADPLL comprises a frequency rough estimation module, a radio frequency signal frequency accurate measurement and tracking module based on the ADPLL and a frequency estimation algorithm module. The frequency rough estimation module comprises a high-frequency counter CNT0, and is a self-adaptive adjustable frequency divider N0 for adapting to the frequency detection of the broadband radio frequency signal; the frequency precision measuring and tracking module of the radio frequency signal based on the ADPLL comprises a system clock generating module C realized by using a D trigger, two counters CNT 1-CNT 2, an adder M, a loop filter LP, a time-to-digital converter TDC, a numerical control oscillator DCO, a frequency divider NC with a fixed frequency dividing ratio and an automatic frequency calibration module; and the frequency estimation algorithm module calculates the frequency of the current input signal according to the frequency control word OTW of the numerically controlled oscillator DCO and the frequency dividing ratio of the frequency divider N0.
The circuit input signal is connected to the input end of the high frequency counter CNT0 and the input end of the adjustable frequency divider N0 in the frequency rough estimation module, and the frequency divider N0 receives the counting result of the counter CNT0 and calculates the frequency dividing ratio of the frequency divider N0.
The output of the Frequency divider N0 is used as the input of an ADPLL-based rf signal Frequency accurate measurement and tracking module, and during the detection process, the ADPLL is accelerated by using an Automatic Frequency Calibration module (AFC). The module adopts output signals of counters CNT 1-CNT 2 as input, and determines a coarse adjustment position and a middle adjustment position of a frequency control word OTW of a numerical control LC oscillator DCO bit by using a coarse adjustment and middle adjustment process based on a binary algorithm to obtain a final frequency control word OTW.
And the frequency estimation algorithm module takes the frequency division ratio of the self-adaptive adjustable frequency divider N0 and the frequency control word OTW of the numerically controlled oscillator DCO as input, and calculates to obtain the current output frequency f _ { DCO } of the numerically controlled oscillator DCO. f _ { DCO } assists subsequent procedures in building the receiver system.
And subsequently entering a receiver configuration option box, wherein the configurable content comprises a receiver architecture-a zero intermediate frequency architecture or a super heterodyne architecture, and important parameters of internal components of the receiver, including gains, noise coefficients, 1dB compression points, third-order intermodulation impedance points, a frequency range, the sampling rate of the ADC, sampling precision and the like. All design parameters are stored in the memory created for the software by the computer.
After the selection is completed, the software helps to construct a complete receiver system according to key parameters such as signal frequency extracted by the frequency detection module and the like, and processes data extracted from the source file previously. The processing process comprises 1) calling fft function, carrying out Fourier transform on time domain signals to transfer the time domain signals to a frequency domain, wherein codes are realized by C + + language and are embedded into sub-functions for calling by a main program; 2) calling a ftshift function to enable frequency domain data to accord with the rule that low frequency is in the middle and high frequency is on two sides; 3) performing frequency-selective amplification on signals in a frequency band, namely simulating the amplification function of various amplifiers in a receiver realized by hardware, wherein the amplification multiple is determined by the previously set parameters of the receiver through software internal calculation; 4) an ifft function is called to perform inverse Fourier transform on the frequency-selective amplified signal, a real part of data of the signal is taken and converted into time domain representation, and a result of signal processing of a receiver can be visually embodied by a time domain diagram; 5) and sampling, quantizing and coding the processed time domain signal to convert the time domain signal into a coded digital baseband signal. Finally, the obtained digital signal is submitted to a signal intelligent cognition module to judge information such as a modulation mode and the like.
The intelligent signal cognition module receives a digital baseband signal transmitted by the radio frequency receiving module, firstly carries out time-frequency transformation to obtain a time-frequency image of the signal, then adopts a time-frequency image processing algorithm based on form fitting, and can obtain a straight line fitting equation and a fitting standard deviation of each curve in the time-frequency image through three steps of gray vector calculation extraction, sliding window peak searching and least square method straight line fitting, so as to distinguish sinusoidal modulation SFM (frequency modulated signal) and linear frequency modulation LFM (linear frequency modulation) signals. If the resolution is not successful, the modulated signals are transmitted into a constellation diagram analysis module, firstly, the modulated signals are decomposed and expressed by a group of orthogonal bases to obtain a constellation diagram of the signals, then, a Sobel edge detection method is adopted, the method comprises two steps of opening operation and edge detection, the outermost periphery edge of the constellation diagram is extracted, the coordinate position of the outermost periphery edge of the constellation diagram is calculated to determine the number and arrangement mode of clusters in the constellation diagram, and then, the modulation mode of the signals is judged. The signals of BPSK, QPSK,8PSK, QAM and the like can be successfully distinguished by using a constellation image processing algorithm.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and all equivalent substitutions or substitutions made on the basis of the above-mentioned technical solutions belong to the scope of the present invention.
Claims (8)
1. A detection, identification and development integrated platform for intelligent cognitive radio is characterized by comprising a broadband receiving module, a signal intelligent cognitive module and a frequency detection module;
the broadband receiving module is automatically designed through a visual interaction platform auxiliary circuit; the receiving module internally comprises a low noise amplifier, a mixer, a filter, a programmable gain amplifier and a digital-to-analog converter basic component, the architecture can select a super heterodyne receiver or a zero intermediate frequency receiver, and simultaneously supports user-defined circuit architecture and customized technical indexes; when a user inputs signal data in a frequency domain form, the signal data can display input and output signal waveforms after being processed by a receiver program;
the signal intelligent cognitive module processes signal data received by the broadband receiving module and rapidly identifies signal types and modulation modes based on an image processing/neural network algorithm; through time-frequency diagram and constellation diagram analysis, various signal modulation modes of LFM, SFM, BPSK, QPSK,8PSK, 16QAM and CDMA are distinguished;
the frequency detection module utilizes the all-digital phase-locked loop to quickly lock frequency, quickly detects the frequency of a high-power signal in a space through the frequency rough estimation module and the frequency precise measurement and tracking and frequency estimation algorithm module based on the all-digital phase-locked loop, and feeds the result back to the broadband receiving module to dynamically adjust the parameters of the internal components of the broadband receiving module according to different conditions.
2. The intelligent cognitive radio-oriented detection, identification and development integrated platform as claimed in claim 1, wherein the broadband receiving module comprises a radio frequency signal reading function for reading signal sampling data stored in a computer memory in a text form; and the radio frequency receiver simulation function is used for processing signal data on a time/frequency domain according to the receiver type and key parameters set by a user, so that the reconfigurable and behavior level simulation functions of the receiver are realized.
3. The integrated platform for intelligent cognitive radio-oriented detection, identification and development according to claim 1, wherein the signal intelligent cognitive module comprises a digital signal data reading function capable of reading digital signals processed by the broadband receiving module; and (3) extracting the characteristics of a signal time-frequency diagram and a constellation diagram by using an image processing/neural network algorithm, and distinguishing the signal type and the modulation mode.
4. The integrated platform for intelligent cognitive radio-oriented detection, identification and development according to claim 1, wherein the frequency detection module comprises a frequency rough estimation module, an all-digital phase-locked loop-based radio frequency signal frequency precise measurement and tracking module and a frequency estimation algorithm module; firstly, a frequency rough estimation module is used for realizing frequency rough estimation, and then a frequency accurate measurement and tracking module is used for realizing signal frequency accurate measurement; the function of the frequency estimation algorithm module is to obtain the frequency of the current input signal according to the frequency control word of the numerically controlled oscillator.
5. The intelligent cognitive radio-oriented detection, identification and development integrated platform as claimed in claim 2, wherein the broadband receiving module is implemented on a computer by using a programming language C + +, in combination with a programming tool.
6. The integrated platform for intelligent cognitive radio-oriented probe recognition development according to claim 3, wherein the signal intelligent cognitive module is implemented on a computer by using a programming language C + +, in combination with a programming tool.
7. The integrated platform for intelligent cognitive radio-oriented probe recognition development according to claim 4, wherein the frequency detection module is implemented on a computer using programming language C.
8. The intelligent cognitive radio-oriented detection, identification and development integrated platform as claimed in claim 4, wherein the frequency detection module is implemented on FPGA using a hardware description language Verilog HDL.
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