Active interference broadband spectrum monitoring and identification method and hardware platform

Abstract

The present application provides a method and hardware platform for monitoring and identifying wideband spectrum of active interference, which can solve the problem that radar anti-interference in complex electromagnetic environment is difficult to realize in engineering in related technologies, thereby providing a solution for realizing engineering of wideband monitoring and identification of radar interference in complex electromagnetic environment, providing prior information for the selection of subsequent radar anti-interference measures, and reversing the passive situation of radar anti-active interference at present. The method for monitoring and identifying wideband spectrum of active interference includes: receiving wideband analog intermediate frequency signal; performing digital-to-analog conversion and quantization reception from the wideband analog intermediate frequency signal to obtain a narrowband intermediate frequency signal after digital signalization; performing channelization detection on the narrowband intermediate frequency signal, and obtaining information of the interference signal according to frequency measurement and interference intensity estimation algorithm; extracting interference characteristic parameters in time domain and frequency domain from the narrowband intermediate frequency signal; determining the interference signal in the narrowband intermediate frequency signal according to the information of the interference signal and the interference characteristic parameters in time domain and frequency domain.

Description

Technical Field

[0001] The present application relates to the field of radar anti-interference, and in particular to a method and hardware platform for active interference broadband spectrum monitoring and identification. Background Art

[0002] At present, with the increasing maturity of jamming technology and the increasing deployment of jamming equipment, radar is often at a disadvantage in electronic confrontation. Radar is often unable to decide what anti-interference measures to use due to its inability to perceive the battlefield electromagnetic environment, and thus cannot suppress interference in a targeted manner, thus losing the active advantage of anti-interference.

[0003] At present, there are two main types of methods for active interference identification: the first method is to establish a signal detection probability model through the maximum likelihood method, and the second method is to extract the characteristic parameters of the interference and classify them through various identifiers. Among them, the second method is the most widely used. However, most of the existing algorithms are complex in operation and are limited to theoretical simulation research, and few are actually used in engineering implementation. Summary of the invention

[0004] The embodiments of the present application provide a method and hardware platform for monitoring and identifying wideband spectrum of active interference, which can solve the problem in related technologies that radar anti-interference in complex electromagnetic environments is difficult to implement in engineering, thereby providing an engineering implementation solution for wideband monitoring and identification of radar interference in complex electromagnetic environments, effectively reducing the signal data rate, and providing prior information for the selection of subsequent radar anti-interference measures, thus reversing the current passive situation of radar anti-active interference.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a method for monitoring and identifying an active interference broadband spectrum is provided, comprising: receiving a broadband analog intermediate frequency signal; performing digital-to-analog conversion and quantized reception on the broadband analog intermediate frequency signal to obtain a narrowband intermediate frequency signal after digitization; performing channelization detection on the narrowband intermediate frequency signal, and obtaining information of the interference signal based on a frequency measurement and interference strength estimation algorithm; wherein the information of the interference signal includes frequency, bandwidth and interference strength; extracting interference characteristic parameters in time domain and frequency domain from the narrowband intermediate frequency signal; and determining the interference signal in the narrowband intermediate frequency signal based on the information of the interference signal and the interference characteristic parameters in time domain and frequency domain.

[0007] Based on the active interference broadband spectrum monitoring and identification method, the present application performs digital-to-analog conversion and quantized reception in broadband analog intermediate frequency signals, and can realize full-band monitoring of the interference environment through channelized detection, frequency measurement and interference intensity estimation algorithms; in addition, by extracting interference characteristic parameters in the time domain and frequency domain from radar signals for interference identification, it can provide prior information for the selection of subsequent radar anti-interference measures, solving the problem of difficult engineering implementation of radar anti-interference in complex electromagnetic environments in related technologies, thereby providing an engineering implementation solution for radar interference broadband monitoring and identification in complex electromagnetic environments, effectively reducing the signal data rate, and providing prior information for the selection of subsequent radar anti-interference measures, reversing the current passive situation of radar anti-active interference.

[0008] Optionally, determining the interference signal in the narrow-band intermediate frequency signal based on the information of the interference signal and the interference characteristic parameters in the time domain and the frequency domain includes: inputting the information of the interference signal and the interference characteristic parameters in the time domain and the frequency domain into a trained decision tree model; and determining the interference signal in the narrow-band intermediate frequency signal based on type identification information output by the trained decision tree model.

[0009] Optionally, extracting interference characteristic parameters in the time domain and frequency domain from the narrowband intermediate frequency signal includes: extracting a time domain moment skewness coefficient, a frequency domain moment skewness coefficient, a frequency domain moment kurtosis coefficient and an envelope fluctuation parameter from the narrowband intermediate frequency signal.

[0010] Optionally, extracting the time domain moment skewness coefficient from the narrowband intermediate frequency signal includes:

[0011] The time domain moment skewness coefficient is obtained by the following formula:

[0012]

[0013] Where s is the time domain moment skewness coefficient; x(t) is a narrowband intermediate frequency signal with time t as the variable, with a mean of μ1 and a standard deviation of σ1, E((x(t)-μ) 3 ) is the third moment of x(t).

[0014] Optionally, extracting the frequency domain moment skewness coefficient from the narrowband intermediate frequency signal includes:

[0015] The frequency domain moment skewness coefficient is obtained by the following formula:

[0016]

[0017] Wherein, S is the frequency domain moment skewness coefficient; x(t) is a narrowband intermediate frequency signal with time t as a variable, the spectrum of x(t) is X(ω), X(ω) = FFT(s(t)), FFT is Fourier transform, the mean of X(ω) is μ2, and the standard deviation is σ2.

[0018] Optionally, extracting the frequency domain moment kurtosis coefficient from the narrow-band intermediate frequency signal includes:

[0019] The frequency domain moment kurtosis coefficient is obtained by the following formula:

[0020]

[0021] Wherein, K is the frequency domain moment kurtosis coefficient; x(t) is a narrowband intermediate frequency signal with time t as a variable, the spectrum of x(t) is X(ω), X(ω) = FFT(s(t)), FFT is Fourier transform, the mean of X(ω) is μ2, and the standard deviation is σ2.

[0022] Optionally, extracting an envelope fluctuation parameter from the narrow-band intermediate frequency signal includes:

[0023] Assume the narrowband intermediate frequency signal is Where A(t) is the signal amplitude, ω0 is the angular frequency of the signal, is the phase of the signal, its sampling discrete sequence is x(n), n is the number of sampling points; the square of the instantaneous envelope of the signal is |x(n)| 2 =Re 2 [x(n)]+Im 2 [x(n)]; wherein Re is the real part of the sampled discrete sequence x(n), Im is the imaginary part of the sampled discrete sequence x(n), and the envelope fluctuation parameter is obtained by the following formula:

[0024]

[0025] Where R is the envelope fluctuation parameter, σ3 2 is the square variance of the instantaneous envelope of the signal, μ3 2 is the mean of the square of the instantaneous envelope of the signal.

[0026] In a second aspect, a hardware platform for active interference broadband monitoring and identification is provided, comprising: a programmable logic device, a processor, a memory, and a spectrum monitoring and interference identification algorithm capable of running on the hardware platform, wherein the algorithm, when executed by the hardware platform, implements the steps of the active interference spectrum monitoring and identification method described in any possible implementation method of the first aspect.

[0027] Optionally, the hardware platform includes an active interference broadband spectrum interference identification module, and the active interference broadband spectrum interference identification module includes: a first FPGA chip; a first DSP chip, connected to the first FPGA chip; a second DSP chip, connected to the first FPGA chip; a second FPGA chip, connected to the first FPGA chip; a third DSP chip, connected to the second FPGA chip; a fourth DSP chip, connected to the second FPGA chip; wherein the first DSP chip to the fourth DSP chip are used to extract interference characteristic parameters and identify interference signals; the first FPGA chip is used to receive, manage and distribute data, and the second FPGA chip is used to splice and package the data before identification according to time segmentation and the segmented data after identification, and the data includes the narrowband intermediate frequency signal and the interference signal information after digital signalization; wherein the information of the interference signal includes frequency, bandwidth and interference intensity.

[0028] Optionally, the hardware platform includes an active interference wideband spectrum monitoring module, which includes: a dual-channel analog-to-digital converter with a sampling rate of 500MSPS, used to quantize the received wideband analog intermediate frequency signal to obtain a narrowband intermediate frequency signal after digitization; a third FPGA chip, connected to the analog-to-digital converter and the first FPGA chip; the third FPGA chip is used to perform channelization detection on the narrowband intermediate frequency signal and detect the interference signal according to the frequency measurement and interference intensity estimation algorithm to obtain the data, and send the data to the first FPGA chip; wherein the data includes the frequency, bandwidth and interference intensity information of the interference signal and the spectrum notch information within the 200MHz bandwidth range.

[0029] Based on the active interference broadband spectrum monitoring and identification equipment, this application adopts a hardware platform combining high-speed FPGA+DSP. In the design scheme of this application, considering the high data rate and high throughput of radar intermediate frequency signals, channelization processing requires high computing speed, so FPGA is used for implementation; and the interference feature extraction and identification algorithm is complex, so multi-core DSP is used for implementation. At the same time, FPGA and DSP cooperate to form a reconfigurable hardware platform to realize the perception and classification of active interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flowchart of a method for monitoring and identifying a wideband spectrum of active interference provided in an embodiment of the present application;

[0031] Figure 2 A schematic diagram of a process for identifying interference signals using a decision tree model provided in an embodiment of the present application;

[0032] Figure 3A schematic diagram of the structure of a hardware platform for active interference broadband spectrum monitoring and identification provided in an embodiment of the present application;

[0033] Figure 4 A flowchart of another method for monitoring and identifying a wideband spectrum of active interference provided in an embodiment of the present application;

[0034] Figure 5 A schematic diagram of the structure of an active jamming broadband spectrum monitoring module provided in an embodiment of the present application;

[0035] Figure 6 A schematic diagram of the structure of an active interference broadband spectrum interference identification module provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0037] First, combine Figure 1-Figure 2 The active interference broadband spectrum monitoring and identification method provided in the embodiment of the present application is specifically described.

[0038] For example, Figure 1 The following is a flow chart of a method for monitoring and identifying active interference broadband spectrum provided in an embodiment of the present application. Figure 1 As shown, the active interference broadband spectrum monitoring and identification method includes the following steps:

[0039] Step S11, broadband analog intermediate frequency signal.

[0040] Step S12, performing digital-to-analog conversion and quantization reception on the broadband analog intermediate frequency signal to obtain a digitized narrowband intermediate frequency signal.

[0041] Wherein, the intermediate frequency signal can be a pure echo, a smart noise signal or a noise frequency modulation signal. The present application can quantize the received broadband analog intermediate frequency signal through an analog-to-digital converter. Optionally, a dual-channel analog-to-digital converter with a sampling rate of 500MSPS can be used to quantize the received broadband analog intermediate frequency signal, convert the broadband analog intermediate frequency signal into a digital signal, and then obtain a narrowband intermediate frequency signal.

[0042] After obtaining the digitized narrow-band intermediate frequency signal, step S13 is executed.

[0043] Step S13: performing channelization detection on the narrowband intermediate frequency signal, and obtaining information of the interference signal according to a frequency measurement and interference strength estimation algorithm.

[0044] The information of the interference signal includes frequency, bandwidth and interference intensity. In this application, the distribution of narrowband intermediate frequency signals, 128-channel channelization, interference signal detection, frequency measurement, interference intensity estimation and other functions can be realized through a high-speed FPGA chip, thereby obtaining information such as the frequency, bandwidth and interference intensity of the interference.

[0045] After obtaining the information of the interference signal including the frequency, bandwidth and interference intensity information, step S14 is executed.

[0046] Step S14: extracting interference characteristic parameters in the time domain and the frequency domain from the narrowband intermediate frequency signal.

[0047] The interference characteristic parameters include the time domain moment skewness coefficient. Assume x(t) is a narrowband intermediate frequency signal with time t as the variable, its mean is μ1, and its standard deviation is σ1, E((x(t)-μ) 3 ) is the third-order moment of x(t), then the time domain moment skewness coefficient s is defined as:

[0048]

[0049] The time domain moment skewness coefficient s characterizes the degree of asymmetry of a distribution. If the tail on the right side of the distribution curve is longer than that on the left side, the distribution is said to have positive skewness. Conversely, if the tail on the right side of the distribution curve is shorter than that on the left side, the distribution is said to have negative skewness. By setting the threshold, the time domain moment skewness coefficient s can distinguish smart noise from pure echo, smart noise and noise FM signal.

[0050] The interference characteristic parameters also include the frequency domain moment skewness coefficient. Assume that x(t) is a narrowband intermediate frequency signal with time t as a variable, the spectrum of x(t) is X(ω), X(ω) = FFT(s(t)), FFT is Fourier transform, the mean of X(ω) is μ2, and the standard deviation is σ2. In order to examine the distribution of its power spectrum, the frequency domain moment skewness coefficient of its power spectrum is calculated. The frequency domain moment skewness coefficient S is defined as:

[0051]

[0052] The three signal styles of pure echo, smart noise and noise FM signal all have positive skewness, which means that the tail on the right side of their distribution curves is longer than that on the left side. The frequency domain moment skewness of pure echo and smart noise signals basically does not change with the change of JNR (interference-to-noise ratio); while as JNR increases, the value of noise FM interference gradually increases with the increase of JNR. Therefore, the frequency domain moment skewness can be used to distinguish noise FM interference from other signals.

[0053] The interference characteristic parameters also include the frequency domain moment kurtosis coefficient. Assume that x(t) is a narrowband intermediate frequency signal with time t as a variable, the spectrum of x(t) is X(ω), X(ω) = FFT(s(t)), FFT is Fourier transform, the mean of X(ω) is μ2, and the standard deviation is σ2, then the frequency domain moment kurtosis coefficient K is defined as:

[0054]

[0055] The frequency domain moment kurtosis coefficient K characterizes the steepness of the distribution. It is usually relative to the normal distribution (K=3). When K>3, it means a peak distribution, and when K<3, it means a flat peak distribution. The frequency domain moment kurtosis coefficient K of various interference patterns is greater than 3, showing a peak distribution. The frequency domain moment kurtosis of pure echo signal and smart noise is relatively stable as the JNR increases. The frequency domain moment kurtosis value of noise frequency modulation interference is larger, so the frequency domain moment kurtosis coefficient K can be used to distinguish the noise frequency modulation signal.

[0056] Wherein, the interference characteristic parameter also includes an envelope fluctuation parameter. Assume that the narrowband intermediate frequency signal is Where A(t) is the signal amplitude, ω0 is the angular frequency of the signal, is the phase of the signal, its sampling discrete sequence is x(n), n is the number of sampling points; the square of the instantaneous envelope of the signal is |x(n)| 2 =Re 2 [x(n)]+Im 2 [x(n)]; wherein Re is the real part of the sampled discrete sequence x(n), Im is the imaginary part of the sampled discrete sequence x(n), and the envelope fluctuation parameter R is obtained by the following formula:

[0057]

[0058] Where R is the envelope fluctuation parameter, σ3 2 is the square variance of the instantaneous envelope of the signal, μ3 2 is the mean value of the square of the instantaneous envelope of the signal. The envelope fluctuation parameter R reflects the degree of change of the signal envelope. The envelope fluctuation parameter R can be used to distinguish the smart noise interference from the three signals of pure echo, smart noise and noise frequency modulation signal.

[0059] After the interference characteristic parameters are extracted from the intermediate frequency signal, step S15 is executed.

[0060] Step S15: determining the interference signal in the narrowband intermediate frequency signal according to the information of the interference signal and interference characteristic parameters in the time domain and the frequency domain.

[0061] In the present application, the interference signal in the narrowband intermediate frequency signal can be determined in the following manner: the information of the interference signal and the interference characteristic parameters in the time domain and frequency domain are input into a trained decision tree model, and the interference signal in the narrowband intermediate frequency signal is determined based on the type identification information output by the trained decision tree model, and the interference characteristic parameters are input into the trained decision tree model.

[0062] The decision tree model is a method based on decision tree classification in the active interference identification algorithm. Interference identification based on decision tree classification is a typical classification identification method, which can effectively classify a group of disordered and irregular cases. The decision tree is composed of several nodes from top to bottom, starting from the root node and ending with the leaf node. Attribute comparison is performed at each node, that is, threshold judgment, to determine the direction of the branch until the leaf node, which is the desired classification result.

[0063] Optionally, before executing step S13, the embodiment of the present application may further include the step of establishing a decision tree, namely: obtaining a training sample set, the training sample set including a large number of intermediate frequency signals and time domain moment skewness coefficients, frequency domain moment skewness coefficients, frequency domain moment kurtosis coefficients and envelope fluctuation parameters extracted from the intermediate frequency signals; according to the training sample set, training the decision tree model to be trained to obtain a trained decision tree model.

[0064] Step S14, determining the interference signal in the intermediate frequency signal according to the type identification information output by the trained decision tree model.

[0065] The trained decision tree model can distinguish pure echo, smart noise information and noise frequency modulation signal according to the input time domain moment skewness coefficient, frequency domain moment skewness coefficient, frequency domain moment kurtosis coefficient and envelope fluctuation parameter.

[0066] Figure 2 A schematic diagram of a process for identifying interference signals using a decision tree model provided in an embodiment of the present application. Figure 2 As shown, the process of identifying interference signals by the trained decision tree model includes the following steps:

[0067] First, determine whether the time domain moment skewness coefficient s is greater than T1 or whether the envelope fluctuation parameter R is greater than T2. ​​If the time domain moment skewness coefficient s is greater than T1 or the envelope fluctuation parameter R is greater than T2, the intermediate frequency signal is a noise frequency modulation signal. Among them, T1 is the symmetry of the spectrum distribution, and the spectrum distribution symmetry of noise frequency modulation interference is worse than that of echo and smart noise interference; T2 is the envelope fluctuation degree, and the envelope fluctuation of noise frequency modulation interference is greater than that of echo and smart noise interference. T1 and T2 are threshold values ​​obtained based on simulation training.

[0068] Secondly, if the time domain moment skewness coefficient s is not greater than T1 and the envelope fluctuation parameter R is not greater than T2, then determine whether the frequency domain moment skewness coefficient S is greater than T3 or whether the frequency domain moment kurtosis coefficient K is greater than T4. If the frequency domain moment skewness coefficient S is greater than T3 or the frequency domain moment kurtosis coefficient K is greater than T4, then the intermediate frequency signal is smart noise information. If the frequency domain moment skewness coefficient S is not greater than T3 and the frequency domain moment kurtosis coefficient K is not greater than T4, then the intermediate frequency signal is a pure echo. Among them, T3 represents the symmetry of the signal time domain distribution, and the symmetry of the time domain signal distribution of smart noise interference is worse than that of echo and noise frequency modulation interference; T4 represents the steepness of the spectrum, and the steepness of the spectrum of smart noise interference is greater than that of echo and noise frequency modulation interference. T3 and T4 are threshold values ​​obtained based on simulation training.

[0069] The present application can extract interference feature parameters in the time domain and frequency domain from radar signals, and then use the trained decision tree model to identify interference, which can provide prior information for the selection of subsequent radar anti-interference measures, and solve the problem that radar anti-interference in complex electromagnetic environments is difficult to implement in engineering in related technologies, thereby providing a radar anti-interference engineering implementation solution in complex electromagnetic environments, effectively reducing the signal data rate, and providing prior information for the selection of subsequent radar anti-interference measures, reversing the current passive situation of radar anti-active interference.

[0070] Combination of the above Figure 1-Figure 2 The active interference identification method provided by the embodiment of the present application is described in detail. Figure 3 - Figure 6 illustrates in detail a hardware platform for executing the active interference identification method provided in an embodiment of the present application.

[0071] Please refer to Figure 3 , Figure 3 The schematic diagram of the structure of a hardware platform for active interference broadband monitoring and identification provided by the embodiment of the present application. Figure 3 As shown, the hardware platform includes: a programmable logic device (not shown in the figure), a processor, a memory (not shown in the figure), and a spectrum monitoring and interference identification algorithm that can be run on the hardware platform. When the algorithm is executed by the hardware platform, the steps of the above-mentioned active interference spectrum monitoring and identification method are implemented.

[0072] Alternatively, if Figure 3As shown, the processor includes an active interference broadband spectrum interference identification module, and the active interference broadband spectrum interference identification module includes: a first FPGA chip; a first DSP chip, connected to the first FPGA chip; a second DSP chip, connected to the first FPGA chip; a second FPGA chip, connected to the first FPGA chip; a third DSP chip, connected to the second FPGA chip; a fourth DSP chip, connected to the second FPGA chip; wherein the first DSP chip to the fourth DSP chip are used to extract interference feature parameters and identify interference signals; the first FPGA chip is used to receive, manage and distribute data, and the second FPGA chip is used to splice and package the data before identification according to time segments and the segmented data after identification, and the data includes the narrowband intermediate frequency signal and the interference signal information after digital signalization; wherein the information of the interference signal includes frequency, bandwidth and interference intensity.

[0073] Alternatively, if Figure 3 As shown, the processor includes an active interference wideband spectrum monitoring module, and the active interference wideband spectrum monitoring module includes: a dual-channel analog-to-digital converter with a sampling rate of 500MSPS, which is used to quantize the received wideband analog intermediate frequency signal to obtain a narrowband intermediate frequency signal after digital signalization; a third FPGA chip, connected to the analog-to-digital converter and the first FPGA chip; the third FPGA chip is used to perform channelization detection on the narrowband intermediate frequency signal and detect the interference signal according to the frequency measurement and interference intensity estimation algorithm to obtain the data, and send the data to the first FPGA chip; wherein the data includes the frequency point, bandwidth and interference intensity information of the interference signal and the spectrum notch information within the 200MHz bandwidth range.

[0074] Figure 4 FIG. 5 is a schematic diagram of the structure of an active jammer wideband spectrum monitoring module provided by an embodiment of the present application. Figure 4 and Figure 5As shown, the design scheme of the active interference broadband spectrum monitoring module is: the dual-channel ADC (analog-to-digital converter) with a sampling rate of 500MSPS can use TI's ADC12D500RF (dual-channel 12bit / 500MSPS) to complete the quantization reception of the intermediate frequency signal; the third FPGA chip uses a high-speed FPGA chip with a product model of XC7VSX690T to realize the distribution of digital signals, 128-channel channelization, interference signal detection, frequency measurement, interference intensity estimation and other functions, and outputs with a channelized structure with a bandwidth of 3.90625MHz, combined with energy accumulation to complete the broadband spectrum monitoring of the interference environment and the spectrum notch information within the bandwidth of 200MHz. At the same time, the frequency, bandwidth and interference intensity of the interference are obtained through FFT. The above information is packaged and output according to the protocol to realize broadband spectrum monitoring within a bandwidth of 200MHz, and provide input parameters for subsequent active interference feature extraction and frequency agility.

[0075] Figure 6 A schematic diagram of the structure of the active interference broadband spectrum interference identification module provided in the embodiment of the present application is shown in FIG. Figure 4 As shown in FIG6 , the active interference broadband spectrum interference identification module design is as follows: in view of the fact that the front-end pre-synthesized left and right fronts have a total of 8 main signals, and considering the hardware processing capability and the complexity of the interference identification algorithm, the interference identification module is composed of 4 DSP+FPGA boards of the same structure. Each DSP+FPGA board contains 4 6678DSPs and 2 XC7VLX690T FPGAs, which are responsible for processing the digital signals of the same wave position of the left and right fronts respectively. The 4 DSPs complete the active interference feature extraction and identification, 1 FPGA is responsible for data reception, management and distribution, and 1 FPGA is responsible for data time segmentation before identification and splicing and packaging after identification.

[0076] Based on a hardware platform for active interference broadband spectrum monitoring and identification, this application adopts a hardware platform combining high-speed FPGA + DSP. FPGA is suitable for operations with simple algorithm structure and high throughput, but programming is relatively complex; multi-core DSP is suitable for operations with complex algorithms, programming is relatively easy, but the speed is slow and it is difficult to perform large throughput operations. In the design scheme of this application, considering that the radar intermediate frequency signal has a high data rate and a large throughput, and the channelization processing has high requirements for computing speed, FPGA is used for implementation; and the interference feature extraction and identification algorithm is complex, so multi-core DSP is used for implementation. At the same time, FPGA and DSP cooperate to form a reconfigurable hardware platform to realize the perception and classification of active interference.

Claims

1. A method for monitoring and identifying active interference broadband spectrum, characterized in that: include: Receive broadband analog intermediate frequency signals; Performing digital-to-analog conversion and quantization reception on the broadband analog intermediate frequency signal to obtain a narrowband intermediate frequency signal after digitalization; Channelize the narrowband intermediate frequency signal and obtain information of the interference signal according to a frequency measurement and interference strength estimation algorithm; wherein the information of the interference signal includes frequency, bandwidth and interference strength; Extract interference characteristic parameters in time domain and frequency domain from the narrowband intermediate frequency signal; Determine the interference signal in the narrowband intermediate frequency signal according to the information of the interference signal and interference characteristic parameters in the time domain and the frequency domain; Determining the interference signal in the narrowband intermediate frequency signal according to the information of the interference signal and the interference characteristic parameters in the time domain and the frequency domain includes: inputting the information of the interference signal and the interference characteristic parameters in the time domain and the frequency domain into a trained decision tree model; determining the interference signal in the narrowband intermediate frequency signal according to the type identification information output by the trained decision tree model; The step of extracting interference characteristic parameters in the time domain and frequency domain from the narrowband intermediate frequency signal comprises: extracting a time domain moment skewness coefficient, a frequency domain moment skewness coefficient, a frequency domain moment kurtosis coefficient and an envelope fluctuation parameter from the narrowband intermediate frequency signal.

2. The active interference broadband spectrum monitoring and identification method according to claim 1 is characterized in that: The step of extracting the time domain moment skewness coefficient from the narrowband intermediate frequency signal includes: the time domain moment skewness coefficient is obtained by the following formula: ; Wherein, s is the time domain moment skewness coefficient; is a narrowband intermediate frequency signal with time t as a variable, and its mean is , the standard deviation is , yes The third moment of .

3. The active interference broadband spectrum monitoring and identification method according to claim 1 is characterized in that: The step of extracting the frequency domain moment skewness coefficient from the narrowband intermediate frequency signal includes: the frequency domain moment skewness coefficient is obtained by the following formula: ; Wherein, S is the frequency domain moment skewness coefficient; is a narrowband intermediate frequency signal with time t as a variable, The spectrum is , , FFT is Fourier transform, The mean value of , the standard deviation is .

4. The method for monitoring and identifying active interference broadband spectrum according to claim 1, characterized in that: The step of extracting the frequency domain moment kurtosis coefficient from the narrowband intermediate frequency signal includes: the frequency domain moment kurtosis coefficient is obtained by the following formula: ; Wherein, K is the frequency domain moment kurtosis coefficient; is a narrowband intermediate frequency signal with time t as a variable, The spectrum is , , FFT is Fourier transform, The mean value of , the standard deviation is .

5. The method for monitoring and identifying active interference broadband spectrum according to claim 1, characterized in that: The step of extracting the envelope fluctuation parameter from the narrowband intermediate frequency signal comprises: assuming that the narrowband intermediate frequency signal is ;in, is the signal amplitude, is the angular frequency of the signal, is the phase of the signal, and its sampling discrete sequence is , n is the number of sampling points; the square of the instantaneous envelope of the signal is ;in, For sampling discrete sequence The real part of Im is the sampled discrete sequence The imaginary part of the envelope fluctuation parameter is obtained by the following formula: ; Wherein, R is the envelope fluctuation parameter, is the square of the instantaneous envelope of the signal, is the mean of the square of the instantaneous envelope of the signal.

6. A hardware platform for active interference broadband spectrum monitoring and identification, characterized in that: include: A programmable logic device, a processor, a memory, and a spectrum monitoring and interference identification algorithm that can run on the hardware platform, wherein the algorithm, when executed by the hardware platform, implements the steps of the active interference broadband spectrum monitoring and identification method as described in any one of claims 1-5.

7. The hardware platform for active interference broadband spectrum monitoring and identification according to claim 6, characterized in that: The processor includes an active jammer wideband spectrum interference identification module, and the active jammer wideband spectrum interference identification module includes: The first FPGA chip; A first DSP chip, connected to the first FPGA chip; A second DSP chip connected to the first FPGA chip; A second FPGA chip, connected to the first FPGA chip; A third DSP chip, connected to the second FPGA chip; a fourth DSP chip, connected to the second FPGA chip; Among them, the first DSP chip to the fourth DSP chip are used to extract interference characteristic parameters and identify interference signals; the first FPGA chip is used to receive, manage and distribute data, and the second FPGA chip is used to segment the data before identification according to time and to splice and pack the segmented data after identification, and the data includes the narrowband intermediate frequency signal and the interference signal information after digital signalization; wherein the information of the interference signal includes frequency, bandwidth and interference intensity.

8. The hardware platform according to claim 7, characterized in that: The processor also includes an active interference wideband spectrum monitoring module, and the active interference wideband spectrum monitoring module includes: A dual-channel analog-to-digital converter with a sampling rate of 500 MSPS is used to quantize the received wideband analog intermediate frequency signal to obtain a narrowband intermediate frequency signal after digitalization; A third FPGA chip is connected to the analog-to-digital converter and the first FPGA chip; the third FPGA chip is used to perform channelization detection on the narrowband intermediate frequency signal and detect the interference signal according to a frequency measurement and interference strength estimation algorithm to obtain the data, and send the data to the first FPGA chip.

Images