Broadband signal intelligent detection and identification method and system

Abstract

The invention relates to the technical field of modulation signal detection, in particular to a broadband signal intelligent detection and identification method and system. Converting the to-be-identified broadband signal to a time-frequency domain by using short-time Fourier transform to obtain an amplitude spectrum and a phase spectrum of the to-be-identified broadband signal; carrying out fusion processing on the signal amplitude spectrum and the phase spectrum by utilizing an HSV color model to obtain a time-frequency spectrogram combining the amplitude spectrum and the phase spectrum, and constructing the HSV color model according to the signal amplitude spectrum and the phase spectrum; and detecting and identifying a burst signal in the broadband signal to be identified based on the time-frequency spectrogram, and visually displaying a detection and identification result of the burst signal. According to the method, the accuracy of detecting and identifying the target signal under the complex noise background can be improved, real-time intelligent detection and modulation mode identification can be carried out on the burst signal in the broadband data, and intelligent analysis of the signal in the variable electromagnetic environment becomes possible.

Description

Technical Field

[0001] The present invention relates to the technical field of modulation signal detection, and in particular to a method and system for intelligent detection and identification of broadband signals. Background Art

[0002] In the current field of signal processing, broadband data acquisition has become an important means of obtaining a large amount of data, especially in situations where real-time monitoring and analysis of complex signal environments are required. The burst signals in broadband data often carry important information, so it is particularly important to detect and identify the burst signals in real time. Therefore, exploring efficient and real-time signal detection and recognition algorithms has important research value and practical significance. At present, signal modulation mode recognition technology is mainly divided into the following routes. The first is based on the likelihood decision theory method: including the average likelihood ratio test (ALRT), the generalized likelihood ratio test (GLRT) and the improved ALRT algorithm. ALRT calculates the likelihood function value of the received signal and classifies the modulation mode based on these values. GLRT and improved ALRT are suitable for situations with long data length or multi-antenna reception, and reduce the computational complexity by optimizing the likelihood ratio test. The second is the traditional feature extraction method: including classification based on the instantaneous characteristics of the signal, such as using the instantaneous amplitude, phase and frequency of the signal to identify the modulation mode; classification based on the signal constellation diagram (IQ data) features, by analyzing the IQ diagram features of the signal, extracting information related to the modulation mode; classification based on the high-order cumulants of the signal, using the high-order cumulants of the noise to be zero, and identifying the modulation mode through the high-order cumulants of the signal; classification based on the cyclic spectrum characteristics of the signal, using the cyclic stationary characteristics of the signal, and identifying the modulation mode through the cyclic spectrum characteristics of the signal. The third is the method based on deep learning: including extracting the effective information features contained in the signal image based on the Convolutional Neural Network (CNN), and using these features to identify the modulation mode; extracting the signal time series features based on the Recurrent Neural Network (RNN) and identifying the modulation mode.

[0003] Although traditional signal detection methods can meet the needs to a certain extent, they have limitations in real-time and accuracy when processing large amounts of data and complex electromagnetic environments, and are affected by factors such as the channel environment and high computational complexity. In the field of signal modulation recognition, although traditional modulation recognition methods based on likelihood decision theory and feature extraction methods perform well in some scenarios, they have high requirements for prior knowledge. When faced with complex signals and changing environments, traditional methods have poor robustness and limited recognition accuracy. Deep learning technology, especially algorithms based on neural networks, can automatically learn and extract features with high discrimination by analyzing the characteristics of the signal set to be identified, thereby solving the above problems to a certain extent. However, most of the algorithms based on neural networks today mainly analyze the characteristics of the signal set to be identified, select a single signal feature with high discrimination or directly splice multi-domain signal features as network input, or optimize the network structure to extract deeper features to improve performance. This often ignores the complementarity of different modal features and features extracted by different classifiers. Summary of the invention

[0004] To this end, the present invention provides a broadband signal intelligent detection and recognition method and system to solve the problem that the signal recognition accuracy is affected when facing complex signals and changing environments in existing signal detection and recognition.

[0005] According to the design scheme provided by the present invention, on the one hand, a method for intelligent detection and identification of broadband signals is provided, comprising:

[0006] receiving a broadband intermediate frequency signal to be identified;

[0007] The broadband signal to be identified is transformed into the time-frequency domain by using short-time Fourier transform to obtain the amplitude spectrum and phase spectrum of the broadband signal to be identified; and the amplitude spectrum and phase spectrum of the signal are fused by using the HSV color model to obtain a time-frequency spectrum diagram of the combined amplitude spectrum and phase spectrum, wherein the HSV color model is constructed based on the amplitude spectrum and phase spectrum of the signal;

[0008] The burst signal in the broadband signal to be identified is detected and identified based on the time-frequency spectrum, and the detection and identification results of the burst signal are visualized.

[0009] As the broadband signal intelligent detection and identification method of the present invention, further, receiving a broadband intermediate frequency signal to be identified comprises:

[0010] A broadband signal acquisition card is used to receive the broadband intermediate frequency signal to be identified and pre-process the broadband intermediate frequency signal. The broadband signal acquisition card acquires the broadband intermediate frequency signal to be identified based on an orthogonal two-way IQ signal data acquisition and storage system.

[0011] As the broadband signal intelligent detection and recognition method of the present invention, further, the broadband signal to be identified is transformed into the time-frequency domain by short-time Fourier transform, comprising:

[0012] Acquire a complex time-frequency matrix of the broadband signal to be identified based on short-time Fourier transform, and acquire an amplitude spectrum and a phase spectrum of the broadband signal to be identified based on the complex time-frequency matrix;

[0013] The phase spectrum is preprocessed with phase jump using the triangular transformation method, and a phase spectrum with symmetrical texture features in the phase spectrum is obtained.

[0014] As the broadband signal intelligent detection and recognition method of the present invention, further, the signal amplitude spectrum and phase spectrum are fused using the HSV color model, including:

[0015] The signal amplitude is used as saturation, the sine value of the phase spectrum trigonometric function is used as chromaticity, and the cosine value of the phase spectrum trigonometric function is used as brightness to obtain the HSV color model;

[0016] The HSV color model is used to fuse the amplitude spectrum and the phase spectrum to obtain a time-frequency spectrum diagram of the joint phase spectrum and amplitude spectrum.

[0017] As a method for intelligent detection and identification of broadband signals of the present invention, further, based on the time-frequency spectrum diagram, a burst signal in the broadband signal to be identified is detected and identified, comprising:

[0018] The signal detection problem is converted into a regression problem by using a pre-trained single-stage detector to detect and identify a burst signal in a to-be-identified broadband signal. The single-stage detector is constructed by using a single-stage target detection algorithm.

[0019] As the broadband signal intelligent detection and recognition method of the present invention, further, the single-stage detector adopts a lightweight YOLOv5 network structure, and the lightweight YOLOv5 network structure includes a backbone network for extracting multi-scale feature data of an input image based on a feature pyramid, a neck network for fusing the multi-scale feature data, and a head network for performing target detection and recognition based on multi-scale fusion features.

[0020] As the broadband signal intelligent detection and recognition method of the present invention, further, the single-stage detector training process includes:

[0021] Using the signal-to-noise ratio parameter to simulate the wireless communication environment signal and generate corresponding broadband time-frequency spectrum sample data, and annotate the sample data with label information, wherein the label includes modulation mode, signal center time, signal duration, signal carrier frequency and signal bandwidth;

[0022] At each signal-to-noise ratio, the sample data is divided into a training set, a validation set, and a test set according to a preset ratio;

[0023] The training set is used to train the one-stage detector, and the validation set and test set are used to adjust parameters and evaluate the performance of the trained one-stage detector.

[0024] In another aspect, the present invention further provides a broadband signal intelligent detection and recognition system, comprising: a signal receiving module, a feature fusion module and a target detection module, wherein:

[0025] A signal receiving module, used for receiving a broadband intermediate frequency signal to be identified;

[0026] A feature fusion module is used to transform the broadband signal to be identified into the time-frequency domain using short-time Fourier transform to obtain the amplitude spectrum and phase spectrum of the broadband signal to be identified; and to fuse the amplitude spectrum and phase spectrum of the signal using the HSV color model to obtain a time-frequency spectrum diagram of the combined amplitude spectrum and phase spectrum, wherein the HSV color model is constructed based on the amplitude spectrum and phase spectrum of the signal;

[0027] The target detection module is used to detect and identify the burst signal in the broadband signal to be identified based on the time-frequency spectrum diagram, and visualize the detection and identification results of the burst signal.

[0028] Beneficial effects of the present invention:

[0029] The present invention adopts an amplitude-phase feature fusion method based on HSV to make the time-frequency spectrum features of the signal have a richer latent space representation, significantly improve the feature contrast between the signal and the noise, and improve the accuracy of the target signal being detected and identified under a complex noise background, and can perform real-time intelligent detection and modulation mode identification for burst signals in broadband data; the target detection network is applied to the field of signal detection and identification, showing obvious advantages in the real-time and accuracy of detection and identification. It is verified by experimental data that when the broadband signal-to-noise ratio is higher than -10dB, the accuracy and recall rates are both maintained above 90%, and it has a better detection and identification effect on weak signals. It can realize end-to-end processing capability, real-time response speed, automatic feature learning capability, low power consumption and computing requirements in one-stop signal intelligent real-time analysis, reduce the complexity and cost of hardware deployment, and make it possible to perform intelligent analysis of signals in a changeable electromagnetic environment, and has good application prospects in the field of signal intelligent detection and classification and identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of the broadband signal intelligent detection and identification process in the embodiment;

[0031] Figure 2 Schematic diagram of the QPSK time-frequency amplitude spectrum (a) and phase spectrum (b) in the embodiment;

[0032] Figure 3 This is a schematic diagram of the YOLOv5s model network structure in the embodiment;

[0033] Figure 4 Schematic diagram of the accuracy (a) and recall (b) curves of the model under various signal-to-noise ratio data in the embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0035] In view of the problems that traditional signal detection has limitations in real-time and accuracy when processing large amounts of data and complex electromagnetic environments, and is easily affected by factors such as channel environment and high computational complexity, an embodiment of the present invention provides a method for intelligent detection and identification of broadband signals, such as Figure 1 As shown, including:

[0036] S101, receiving a broadband intermediate frequency signal to be identified.

[0037] Specifically, a broadband signal acquisition card is used to receive the broadband intermediate frequency signal to be identified and preprocess the broadband intermediate frequency signal, and the broadband signal acquisition card acquires the broadband intermediate frequency signal to be identified based on an orthogonal two-way IQ signal data acquisition and storage system. The orthogonal two-way IQ signal data acquisition and storage system can be implemented using an existing inter-channel data acquisition system.

[0038] S102. Use short-time Fourier transform to transform the broadband signal to be identified into the time-frequency domain to obtain the amplitude spectrum and phase spectrum of the broadband signal to be identified; and use the HSV color model to fuse the signal amplitude spectrum and phase spectrum to obtain a time-frequency spectrum diagram of the combined amplitude spectrum and phase spectrum, wherein the HSV color model is constructed based on the signal amplitude spectrum and phase spectrum.

[0039] Among them, using short-time Fourier transform to transform the broadband signal to be identified into the time-frequency domain may include:

[0040] Acquire a complex time-frequency matrix of the broadband signal to be identified based on short-time Fourier transform, and acquire an amplitude spectrum and a phase spectrum of the broadband signal to be identified based on the complex time-frequency matrix;

[0041] The phase spectrum is preprocessed with phase jump using the triangular transformation method, and a phase spectrum with symmetrical texture features in the phase spectrum is obtained.

[0042] For amplitude-phase modulated signals, the time-frequency characteristics of the signal are mainly affected by the phase and amplitude, and its time-frequency spectrum shows more levels and changes. There are no obvious differences in the time-frequency spectrum of signals with different amplitude-phase modulation methods, so it is impossible to accurately identify the modulation methods of each amplitude-phase signal by relying solely on the time-frequency amplitude spectrum characteristics. In order to more comprehensively describe the time-frequency characteristics of the signal, in the embodiment of this case, the phase feature is added on the basis of the original amplitude spectrum feature, and the time-frequency spectrum of the joint phase spectrum and amplitude spectrum is used to identify the signal modulation method to improve the recognition accuracy.

[0043] The time domain signal S is processed by STFT to obtain the complex time-frequency matrix Where T and F correspond to the number of time frames and frequency points. The amplitude spectrum and phase spectrum corresponding to the time-frequency matrix can be expressed as:

[0044]

[0045] P=∠S∈[-π,π] T×F

[0046] Among them, ∠ is the operation for finding radians, such as Figure 2 As shown, this is a schematic diagram of the amplitude spectrum and phase spectrum of the QPSK signal.

[0047] In the process of calculating the phase, the time-frequency matrix S will compress the continuous phase of (-∞, +∞) into a phase matrix in the range of [-π, +π], resulting in a phase jump between +π and -π, which in turn leads to phase jumps and phase ambiguity on the phase spectrum, making the phase spectrum lack symmetrical texture features compared to the amplitude spectrum. This is also the main reason why the amplitude spectrum in the time-frequency spectrum is used as the signal feature in most cases.

[0048] In order to reduce the impact of phase jump and phase ambiguity on the phase spectrum, in the embodiment of this case, a triangular transformation method is used to obtain a phase spectrum with symmetrical texture features. The phases of +π and -π have the same value after triangular transformation, which overcomes the impact of phase jump. The phase spectrum after triangular transformation can be expressed as:

[0049] P cos =cos(P)∈[-1,1] T×F

[0050] P sin = sin(P)∈[-1,1] T×F

[0051] Among them, cos and sin are trigonometric cosine and sine transforms.

[0052] In the field of signal processing, especially in an environment with low signal-to-noise ratio, the impact of noise on phase becomes particularly significant, which directly affects the detection and recognition ability of the time-frequency spectrum for signal targets. In this embodiment, the signal amplitude spectrum and phase spectrum are fused using the HSV color model, which can be designed to include:

[0053] The signal amplitude is used as saturation, the sine value of the phase spectrum trigonometric function is used as chromaticity, and the cosine value of the phase spectrum trigonometric function is used as brightness to obtain the HSV color model;

[0054] The HSV color model is used to fuse the amplitude spectrum and the phase spectrum to obtain a time-frequency spectrum diagram of the joint phase spectrum and amplitude spectrum.

[0055] By introducing the HSV color model, the amplitude spectrum and phase spectrum are effectively fused to enhance the characteristic contrast between the signal and the noise. The HSV color model, namely the hue, saturation, and value model, processes the time-frequency spectrum through color, saturation, and value. In the embodiment of this case, the amplitude of the signal is regarded as saturation (S), the sine value of the trigonometric function is used to represent the hue (H), and the cosine value corresponds to the value (V). Through mapping, the amplitude not only determines the strength of the signal, but also controls the saturation of the color in the time-frequency spectrum. Specifically, by adjusting the saturation, the significance of the signal phase in the entire time-frequency spectrum can be improved, while effectively reducing the interference of noise on the signal detection and recognition process.

[0056] By fusing the HSV color model, the characteristic contrast between signal and noise can be significantly improved, making signal targets easier to detect and identify in a complex noise background; since the importance of signal phase is enhanced, signal targets can be identified more accurately, improving detection accuracy even when the signal-to-noise ratio is low; by controlling saturation, the impact of noise on signal detection and recognition is reduced, the robustness of signal processing is improved, and the impact of noise is reduced, providing new tools and ideas for the field of signal processing.

[0057] S103: Detect and identify a burst signal in the to-be-identified broadband signal based on the time-frequency spectrum diagram, and visualize the detection and identification results of the burst signal.

[0058] Specifically, a pre-trained single-stage detector can be used to transform the signal detection problem into a regression problem to detect and identify the burst signal in the broadband signal to be identified. The single-stage detector is constructed using a single-stage target detection algorithm.

[0059] Single-stage object detection algorithms have occupied an important position in the field of object detection with their excellent computational efficiency and simplified model structure. They can quickly predict the bounding box and category of the target directly from the image, omitting the step of candidate region generation, thereby significantly reducing the amount of calculation and improving the running speed, which is suitable for application scenarios with strong real-time requirements. At the same time, the end-to-end training method of the single-stage detector simplifies the training process and enhances the consistency of the model. Its good generalization ability and adaptability enable it to maintain stable performance in different data sets and environmental changes. In addition, the single-stage detector is easy to integrate and expand, reducing hardware costs and energy consumption, and improving the feasibility of large-scale deployment.

[0060] Among them, the single-stage detector can adopt a lightweight YOLOv5 network structure, which includes a backbone network for extracting multi-scale feature data of an input image based on a feature pyramid, a neck network for fusing the multi-scale feature data, and a head network for performing target detection and recognition based on multi-scale fusion features.

[0061] In the current field of signal processing, broadband data acquisition has become an important means of obtaining large amounts of data, especially in situations where real-time monitoring and analysis of complex signal environments are required. Burst signals in broadband data often carry important information, so real-time detection and identification of burst signals is particularly important. Therefore, exploring efficient and real-time signal detection and identification algorithms has important research value and practical significance.

[0062] In the embodiment of this case, the single-stage target detection algorithm YOLOv5 is selected to realize the rapid recognition of objects in the image. An end-to-end regression problem is used to solve the target detection task. The position and category information of all objects in the image can be output through a single forward pass, which can improve the detection speed and meet the application scenarios with real-time requirements.

[0063] like Figure 3 As shown in the figure, the lightweight network structure mainly includes the backbone network, feature pyramid network, neck network and head network. Based on the low number of parameters and computational complexity, the model can complete the target detection task in a shorter time, which is very important for real-time application scenarios or resource-constrained devices. At the same time, the YOLOv5 model is highly convenient in use and deployment, and can quickly build a customized target detection model and migrate and apply it in different application scenarios.

[0064] The single-stage detector training process can be designed to include:

[0065] Using the signal-to-noise ratio parameter to simulate the wireless communication environment signal and generate corresponding broadband time-frequency spectrum sample data, and annotate the sample data with label information, wherein the label includes modulation mode, signal center time, signal duration, signal carrier frequency and signal bandwidth;

[0066] At each signal-to-noise ratio, the sample data is divided into a training set, a validation set, and a test set according to a preset ratio;

[0067] The training set is used to train the one-stage detector, and the validation set and test set are used to adjust parameters and evaluate the performance of the trained one-stage detector.

[0068] The sample signal set is {2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, 128QAM, 16APSK, MSK, 4CPFSK}. By setting a variety of signal-to-noise ratio parameters, the diversity and authenticity of the signal in the wireless communication environment are simulated. Under each signal-to-noise ratio, the training set, validation set, and test set contain {420, 120, 60} broadband time-frequency spectrograms and {modulation mode, signal center time x, signal duration w, carrier frequency y, bandwidth h} annotation information to guide the YOLOv5 model to learn the characteristics of the signal on the time-frequency amplitude spectrogram.

[0069] The broadband time-spectrum diagram parameter settings are shown in Table 1:

[0070] Table 1 Broadband time-frequency spectrum parameter setting table

[0071]

[0072] After the dataset is ready, the YOLOv5s model is trained using the dataset, and the training parameters are shown in Table 2. After the model training is completed, the signal detection model is tested and comprehensively evaluated.

[0073] Table 2 Training parameter settings

[0074]

[0075] The performance of the model on the test set is as follows Figure 4 As shown in the figure. The test results show that the performance of the target detection model gradually improves with the increase of the signal-to-noise ratio and tends to converge to 1. In the case of high signal-to-noise, it can perform accurate and comprehensive signal detection and recognition tasks based on the fused time-spectrogram. When the signal-to-noise ratio is higher than -10dB, the accuracy and recall rates are both maintained above 90%.

[0076] Furthermore, based on the above method, an embodiment of the present invention also provides a broadband signal intelligent detection and recognition system, comprising: a signal receiving module, a feature fusion module and a target detection module, wherein:

[0077] A signal receiving module, used for receiving a broadband intermediate frequency signal to be identified;

[0078] A feature fusion module is used to transform the broadband signal to be identified into the time-frequency domain using short-time Fourier transform to obtain the amplitude spectrum and phase spectrum of the broadband signal to be identified; and to fuse the amplitude spectrum and phase spectrum of the signal using the HSV color model to obtain a time-frequency spectrum diagram of the combined amplitude spectrum and phase spectrum, wherein the HSV color model is constructed based on the amplitude spectrum and phase spectrum of the signal;

[0079] The target detection module is used to detect and identify the burst signal in the broadband signal to be identified based on the time-frequency spectrum diagram, and visualize the detection and identification results of the burst signal.

[0080] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0081] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0082] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0083] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0084] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A broadband signal intelligent detection and identification method, characterized in that: Include: receiving a broadband intermediate frequency signal to be identified; The broadband signal to be identified is transformed into the time-frequency domain by using short-time Fourier transform to obtain the amplitude spectrum and phase spectrum of the broadband signal to be identified; and the amplitude spectrum and phase spectrum of the signal are fused by using the HSV color model to obtain a time-frequency spectrum diagram of the combined amplitude spectrum and phase spectrum, wherein the HSV color model is constructed based on the amplitude spectrum and phase spectrum of the signal; The burst signal in the broadband signal to be identified is detected and identified based on the time-frequency spectrum, and the detection and identification results of the burst signal are visualized.

2. The method for intelligent detection and identification of broadband signals according to claim 1, characterized in that: Receive the broadband intermediate frequency signal to be identified, including: A broadband signal acquisition card is used to receive the broadband intermediate frequency signal to be identified and pre-process the broadband intermediate frequency signal. The broadband signal acquisition card acquires the broadband intermediate frequency signal to be identified based on an orthogonal two-way IQ signal data acquisition and storage system.

3. The method for intelligent detection and identification of broadband signals according to claim 1, characterized in that: The short-time Fourier transform is used to transform the broadband signal to be identified into the time-frequency domain, including: A complex time-frequency matrix of the broadband signal to be identified is obtained based on short-time Fourier transform, and an amplitude spectrum and a phase spectrum of the broadband signal to be identified are obtained based on the complex time-frequency matrix.

4. The method for intelligent detection and identification of broadband signals according to claim 1 or 3, characterized in that: The signal amplitude spectrum and phase spectrum are fused using the HSV color model, including: The signal amplitude is used as saturation, the sine value of the phase spectrum trigonometric function is used as chromaticity, and the cosine value of the phase spectrum trigonometric function is used as brightness to obtain the HSV color model; The HSV color model is used to fuse the amplitude spectrum and the phase spectrum to obtain a time-frequency spectrum diagram of the joint phase spectrum and amplitude spectrum.

5. The method for intelligent detection and identification of broadband signals according to claim 1, characterized in that: Detecting and identifying burst signals in a broadband signal to be identified based on a time-frequency spectrum diagram, including: The signal detection problem is converted into a regression problem by using a pre-trained single-stage detector to detect and identify a burst signal in a to-be-identified broadband signal. The single-stage detector is constructed by using a single-stage target detection algorithm.

6. The method for intelligent detection and identification of broadband signals according to claim 5, characterized in that: The single-stage detector adopts a lightweight YOLOv5 network structure, which includes a backbone network for extracting multi-scale feature data of an input image based on a feature pyramid, a neck network for fusing the multi-scale feature data, and a head network for performing target detection and recognition based on multi-scale fusion features.

7. The method for intelligent detection and identification of broadband signals according to claim 5 or 6, characterized in that: The single-stage detector training process includes: Using the signal-to-noise ratio parameter to simulate the wireless communication environment signal and generate corresponding broadband time-frequency spectrum sample data, and annotate the sample data with label information, wherein the label includes modulation mode, signal center time, signal duration, signal carrier frequency and signal bandwidth; At each signal-to-noise ratio, the sample data is divided into a training set, a validation set, and a test set according to a preset ratio; The training set is used to train the one-stage detector, and the validation set and test set are used to adjust parameters and evaluate the performance of the trained one-stage detector.

8. A broadband signal intelligent detection and recognition system, characterized in that: It includes: signal receiving module, feature fusion module and target detection module, among which, A signal receiving module, used for receiving a broadband intermediate frequency signal to be identified; A feature fusion module is used to transform the broadband signal to be identified into the time-frequency domain using short-time Fourier transform to obtain the amplitude spectrum and phase spectrum of the broadband signal to be identified; and to fuse the amplitude spectrum and phase spectrum of the signal using the HSV color model to obtain a time-frequency spectrum diagram of the combined amplitude spectrum and phase spectrum, wherein the HSV color model is constructed based on the amplitude spectrum and phase spectrum of the signal; The target detection module is used to detect and identify the burst signal in the broadband signal to be identified based on the time-frequency spectrum diagram, and visualize the detection and identification results of the burst signal.

9. An electronic device, characterized in that: include: at least one processor, and a memory coupled to the at least one processor; The memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 can be implemented.

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