A specific protocol signal recognition method based on deep convolutional network of CBAM

Abstract

The present invention discloses a specific protocol signal recognition method based on a deep convolutional network (CBAM), which includes: obtaining signal information to be recognized; performing pre-processing on the signal information to be recognized to obtain target processing signal information; and performing recognition processing on the target processing signal information to obtain target signal recognition result information.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a signal recognition method and device. Background Art

[0002] With the rapid development of computer technology and the widespread adoption of its applications, information technology has become widely pervasive and deeply ingrained in our lives. In a rapidly changing external environment, all types of information must be transmitted instantly, interconnected and communicated anytime and anywhere, and closely connected to each other. This has led to the emergence of a wide variety of wireless communication technologies and systems. In radio communication frequency bands, communication protocols and procedures are numerous, bandwidth is limited, signals are highly dense, and the electromagnetic environment is complex. This presents significant challenges for non-cooperative receivers who lack prior knowledge and attempt to identify the communication protocol through signal analysis. Currently, most traditional methods for identifying communication signal protocols remain at the expert feature extraction level, relying on extensive manual analysis to extract the attribute characteristics of the target signal within an ideal model or environment. This approach requires significant manual analysis time and is not well-suited for complex environments or perception tasks. The final recognition results are easily affected by the external environment, and the applicability of the perception task cannot be guaranteed. This algorithm, based on manually extracted features and employing feature template matching, performs well when sufficient prior information is available, but performs poorly for identifying unknown signals. It also suffers from poor scalability and is highly sensitive to signal-to-noise ratios, easily causing false alarms at low signal-to-noise ratios. Current specific protocol signal recognition methods based on template matching and deep learning suffer from problems such as a limited number of training signal samples and an inability to detect and identify weak signals in complex electromagnetic environments, resulting in low recognition accuracy. Therefore, a signal recognition method and device are provided to improve signal recognition accuracy and thereby address the low recognition accuracy of specific protocol signals in complex electromagnetic environments, such as low signal-to-noise ratios and harsh transmission environments. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a signal recognition method and device that are conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a signal recognition method, the method comprising:

[0005] Obtaining information of a signal to be identified;

[0006] Pre-processing the signal information to be identified to obtain target processed signal information;

[0007] The target processing signal information is identified and processed to obtain target signal identification result information.

[0008] A second aspect of an embodiment of the present invention discloses a signal recognition device, comprising:

[0009] An acquisition module, used to acquire information of a signal to be identified;

[0010] A first processing module is used to pre-process the signal information to be identified to obtain target processed signal information;

[0011] The second processing module is used to perform identification processing on the target processing signal information to obtain target signal identification result information.

[0012] A third aspect of the present invention discloses another signal recognition device, comprising:

[0013] a memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the signal recognition method disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the signal recognition method disclosed in the first aspect of the embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a schematic diagram of a scenario of a signal recognition system provided by an embodiment of the present invention;

[0019] Figure 2 This is a flow chart of a signal recognition method disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural diagram of a signal recognition device disclosed in an embodiment of the present invention;

[0021] Figure 4 is a structural diagram of another signal recognition device disclosed in an embodiment of the present invention;

[0022] Figure 5It is a structural diagram of a target signal recognition model disclosed in an embodiment of the present invention;

[0023] Figure 6 is a structural diagram of a first feature extraction module disclosed in an embodiment of the present invention;

[0024] Figure 7 is a structural diagram of a first feature analysis module disclosed in an embodiment of the present invention;

[0025] Figure 8 is a structural diagram of a first feature processing module disclosed in an embodiment of the present invention;

[0026] Figure 9 This is a schematic diagram of the effect of a high-order spectrum disclosed in an embodiment of the present invention.

[0027] Figure 10 This is a schematic diagram of the effect of wavelet packet decomposition disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0030] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0032] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, the corresponding data exist for the computer device to process. The details will not be repeated here.

[0033] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0034] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0035] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring objects, performing further image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (OCR), video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0036] Unimodal information is data consisting of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can often be achieved on the task.

[0037] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are generally used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiment of the present application, the large model can be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongwen Qianyi model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, Wenxin Yiyan and other large-scale language models, which are not limited in the embodiment of the present application.

[0038] The embodiments of the present application provide a signal recognition method, apparatus, computer device, and computer-readable storage medium, which are described in detail below.

[0039] See also Figure 1 , Figure 1This is a schematic diagram of a signal recognition system according to an embodiment of the present application. The signal recognition system may include a computer device 100, in which a signal recognition device is integrated. Figure 1 Computer equipment in.

[0040] In the embodiment of the present application, the computer device 100 is mainly used to obtain information of a signal to be identified;

[0041] Pre-processing the signal information to be identified to obtain target processed signal information;

[0042] The target processing signal information is identified and processed to obtain target signal identification result information.

[0043] It can improve the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0044] In the embodiments of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0045] It is understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

[0046] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the signal recognition system can also include one or more other services, which are not limited here.

[0047] In addition, if Figure 1As shown, the signal recognition system may further include a memory 200 for storing data, such as image data, location information, etc.

[0048] It should be noted that Figure 1 The scenario diagram of the signal recognition system shown is only an example. The signal recognition system and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the signal recognition system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

[0049] The present invention discloses a signal recognition method and device that are beneficial for improving signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment. Detailed descriptions are given below.

[0050] Example 1

[0051] See also Figure 2 , Figure 2 This is a flow chart of a signal recognition method disclosed in an embodiment of the present invention. Figure 2 The signal recognition method described is applied to a management system, such as a local server or a cloud server for management, and is not limited in the embodiment of the present invention. Figure 2 As shown, the signal recognition method may include the following operations:

[0052] 101. Obtain information of a signal to be identified.

[0053] 102. Pre-process the signal information to be identified to obtain target processed signal information.

[0054] 103. Perform recognition processing on the target processing signal information to obtain target signal recognition result information.

[0055] It should be noted that the above-mentioned pre-processing of the signal information to be identified is to perform frequency conversion, filtering and noise reduction on the signal to achieve parameter extraction, identification and analysis of the signal at a lower signal-to-noise ratio, which is not limited in the embodiment of the present invention.

[0056] It should be noted that the above-mentioned signal information to be identified can be received by a radar, or can be obtained by converting a digital signal, which is not limited in the embodiment of the present invention.

[0057] Furthermore, before the target processing signal information is identified and processed, the target processing signal information can also be subjected to blind channel estimation and channel matching filtering processing, that is, blind channel estimation and channel matching filtering processing are used to suppress the influence of channel interference, which is beneficial to improve the robustness of feature extraction to overcome the influence of channel distortion. This is not limited in the embodiments of the present invention.

[0058] In this optional embodiment, as an optional implementation manner, the pre-processing of the signal information to be identified to obtain target processed signal information includes:

[0059] Performing noise reduction processing on the signal information to be identified to obtain first processed signal information;

[0060] performing time domain waveform feature marking processing on the first processed signal information to obtain second processed signal information;

[0061] Performing a fast Fourier transform on the second processed signal information to obtain frequency domain signal information;

[0062] Perform frequency domain feature frame marking processing on the frequency domain signal information to obtain target processed signal information.

[0063] It should be noted that the above-mentioned noise reduction processing may be to perform noise reduction processing on the signal to be identified using multi-scale wavelet transform, which is not limited in the embodiment of the present invention.

[0064] It should be noted that the above-mentioned time domain waveform feature marking processing of the first processed signal information is to mark the leading synchronization segment, signal data segment, end segment, etc. of the signal, which is not limited in the embodiment of the present invention.

[0065] It should be noted that the above frequency domain feature frame marking process for the frequency domain signal information is to mark the frequency domain frame of the signal, as well as the leading synchronization header, data segment, end segment and other feature segments of the frame.

[0066] It should be noted that, according to the unique protocol frame format of the protocol signal, different protocol signals exhibit different signal characteristics in the time domain and frequency domain. The time and frequency domain characteristics are used to distinguish the signal protocol type. At the same time, they can also assist in comprehensive detection of the signal spectrum characteristics of different powers. The high-power spectrum characteristics of the PSK protocol signal are as follows: Figure 9 As shown. Different protocol signals will have different preamble sequences, and the signal waveforms formed by different preamble sequences will be very slightly different. During the signal acquisition process, when a signal appears, the envelope waveform within a short period of time after the signal appears is extracted. After noise reduction, it is compared with the waveform features of the preamble code signal in the training library. This is not limited in the embodiment of the present invention.

[0067] It's important to note that since communication protocols specify parameters such as the digital signal's baseband modulation method, communication rate, modulation frequency and interval, signal framing format, and control code format, these characteristics are crucial for identifying their properties. Characterizing the signal's time-frequency characteristics requires a centrally symmetric sliding window to capture the observed signal. The signal within the window undergoes a Fourier transform, ultimately yielding the time-frequency spectrum composed of each signal segment. Because time resolution and frequency resolution interact inversely, a compromise must be made during the calculation process. For the ACARS air-ground data communication system structure widely used in China, timely and accurate information transmission provides reliable protection for civil aviation and shipping safety. There are many types of messages, including uplink messages and downlink messages. Different messages have different protocol formats. Feature extraction and analysis are performed on fixed positions of different protocol messages. For air-ground downlink messages and ground-air uplink message formats, their text formats are very different and the position and length of the free text segment in the message are different. These features can be extracted. After these different data sources are modulated into waveform signals, the amplitude value can be displayed on the waveform and time frequency. Feature extraction and analysis are performed by comparing the position of different signal amplitude strength values ​​such as free text and high-order spectra.

[0068] It should be noted that the signal recognition method of the present application solves the problem of identifying specific protocols under low noise, multipath delay, Doppler frequency shift, strong interference and strong aliasing conditions. It can be well used for communication signal protocol recognition, has good classification performance, and has great promotion value. The embodiments of the present invention are not limited thereto.

[0069] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0070] In an optional embodiment, the target signal processing information is identified and processed to obtain target signal identification result information, including:

[0071] Performing a first signal feature extraction on the target processed signal information to obtain first extracted signal feature information;

[0072] performing a second signal feature extraction on the target processed signal information to obtain second extracted signal feature information;

[0073] The target signal recognition model is used to perform recognition processing on the first extracted signal feature information and the second extracted signal feature information to obtain target signal recognition result information.

[0074] It should be noted that the first signal feature extraction is to perform wavelet packet decomposition, that is, the wavelet packet decomposition has the ability to characterize the local characteristics of the signal in both time and frequency domains, and perform local transformation of the signal in time and frequency to fit the signal waveform characteristics of the high-frequency signal with a short duration and the low-frequency signal with a long duration. Not only the low-frequency part is decomposed, but also the high-frequency part is decomposed twice, to achieve efficient signal denoising and signal analysis and extraction, which is not limited in the embodiment of the present invention. Figure 10 Figure 2 shows the multi-resolution analysis capabilities of wavelet packets with multi-scale decomposition. S represents the original signal; A represents low frequency; D represents high frequency; and the number at the end indicates the number of decomposition layers (i.e., the number of scales). Assuming the signal's frequency range is [Fl, Fh], the original signal S can be considered a decomposition transform at scale zero. Each wavelet packet decomposition of the signal S is equivalent to passing it through both a high-pass and a low-pass filter. After a wavelet packet transform layer, the original signal S is divided into two non-overlapping components, high-frequency D1 and low-frequency A1, with a scale parameter of 1. The frequency range of low-frequency A1 is Fl to (Fl + Fh) / 2, and the frequency range of high-frequency D1 is (Fl + Fh) / 2 to Fh. If a single decomposition layer does not meet the analysis requirements, further wavelet packet decomposition can be performed on D1 and A1. After the second-level decomposition, four frequency ranges are obtained: AA2, DA2, AD2, and DD2. Their frequency ranges are Fl to Fl + ΔF, Fl to Fl + 2ΔF, Fl to Fl + 3ΔF, and Fl to Fl + 4ΔF, respectively, where ΔF = (Fh - Fl) / 22. By analogy, continuing the decomposition, we obtain a wavelet packet decomposition tree with a scale parameter of k and a total of 2k nodes. Assuming the nodes in the kth level are numbered from left to right, i.e., 1 to 2k, the frequency range of the jth node (1 ≤ j ≤ 2k) is Fl to Fl + j × (Fn - Fl) / 2k. With each decomposition, the frequency band is further divided and subdivided, improving frequency resolution while decreasing time resolution.

[0075] In this optional embodiment, as an optional implementation manner, the first signal feature extraction of the target processed signal information to obtain the first extracted signal feature information includes:

[0076] Perform wavelet packet decomposition on the target processing signal information to obtain signal coefficient information;

[0077] Performing coefficient threshold processing on the signal coefficient information to obtain signal threshold information;

[0078] The signal coefficient information and the signal threshold information are subjected to wavelet packet reconstruction processing to obtain first extracted signal feature information.

[0079] It should be noted that the above-mentioned wavelet packet decomposition processing of the target processing signal information is to select an appropriate wavelet packet according to the signal characteristics and determine a wavelet decomposition layer number NN, where the layer number NN is determined according to the accuracy of the frequency resolution and time resolution required in the algorithm, and then the noisy signal is decomposed into NN layers of wavelet packets to obtain wavelet decomposition coefficients (signal coefficient information includes several wavelet decomposition coefficients), which is not limited in the embodiments of the present invention.

[0080] It should be noted that the above-mentioned threshold processing of the signal coefficient information is to select an appropriate threshold and threshold function for each wavelet packet decomposition coefficient to perform threshold quantization on the coefficient. The selection of the threshold is crucial. Different thresholds have obvious differences in signal-to-noise ratio. If the threshold is too large, too much signal detail will be lost, which may cause signal distortion; if the threshold is too small, the expected noise filtering effect cannot be achieved. The selection of the threshold function can be divided into hard threshold method and soft threshold method, but both have their shortcomings. The usual method is to take the threshold as Where δ is the noise standard deviation. This project will analyze the actual situation to determine the selection of the threshold (the signal threshold information includes the signal threshold corresponding to several wavelet decomposition coefficients), which is not limited in the embodiment of the present invention.

[0081] It should be noted that the above wavelet packet reconstruction processing of the signal coefficient information and the signal threshold information is based on the wavelet packet decomposition coefficients of the NNth layer and the coefficients after threshold quantization, which is not limited in the embodiment of the present invention.

[0082] It should be noted that the above-mentioned second signal feature extraction is to calculate the high-order spectrum of the signal and mark it according to its characteristics. For the marking of the signal, because the frequency domain can well present the signal characteristics, these unique features can be learned, and at the same time, the high-order spectrum characteristics can be integrated to identify the protocol. The embodiment of the present invention does not limit this.

[0083] It should be noted that, according to the signal protocol features, the signal envelope waveform, time-frequency and high-order spectrum are detected and feature marked (i.e., the first signal feature extraction and the second signal feature extraction). For the marking of the signal, because the frequency domain can well present the signal features, these unique features can be learned, and at the same time, its high-order spectrum features can be integrated to identify the protocol. Since the protocol signal generally has a specific preamble sequence and the preamble sequence is generally sent cyclically, it shows the style of the corresponding features on the envelope. After the signal waveform after noise reduction is compared with the envelope features in the training library, the protocol style of the signal can be quickly extracted, thereby achieving fast and efficient identification and classification of the signal. The embodiment of the present invention is not limited. Further, the high-order spectrum signal feature extraction adopted in the second signal feature extraction is high-order cumulant and high-order spectrum feature, which can not only suppress Gaussian noise, but also retain the amplitude and phase information of the communication signal, and is independent of time. Wherein the dual spectrum as a third-order cumulant has the advantages of high-order cumulant and is relatively simple to calculate. The embodiment of the present invention is not limited.

[0084] It should be noted that for deep learning of feature extraction, although representation learning technology has been widely used in natural language processing, image processing and other fields, it is rarely used in the field of communications, and most of them directly use existing model algorithms in other fields for corresponding processing. The signal recognition method of this application is based on the target signal recognition model by extracting and marking signal features.

[0085] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0086] In another optional embodiment, Figure 5 As shown, the target signal recognition model includes a first feature dimensionality reduction module, a second feature dimensionality reduction module, a first fusion module, a first convolution module, a first feature extraction module, a second feature extraction module, a third feature extraction module, a fourth feature extraction module, a fifth feature extraction module, a first normalization module, a first activation module, a first pooling module, a first feature analysis module, a second feature analysis module, a third feature analysis module, a fourth feature analysis module, a first feature processing module, a second feature processing module, a third feature processing module, a fourth feature processing module, a fifth feature processing module, a sixth feature processing module, a seventh feature processing module, an eighth feature processing module, a ninth feature processing module, a tenth feature processing module and an eleventh feature processing module; wherein,

[0087] The input end of the first feature dimensionality reduction module is configured to receive the first model input of the target signal recognition model, and the output end of the first feature dimensionality reduction module is connected to the input end of the first fusion module; the input end of the second feature dimensionality reduction module is configured to receive the second model input of the target signal recognition model, and the output end of the second feature dimensionality reduction module is connected to the input end of the first fusion module; the output end of the first fusion module is connected to the input end of the first convolution module; the output end of the first convolution module is connected to the input end of the first feature extraction module; the output end of the first feature extraction module is connected to the input end of the first normalization module; the output end of the first normalization module is connected to the input end of the first activation module; the output end of the first activation module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the first feature analysis module; the output end of the first feature analysis module is connected to the input end of the second feature extraction module; the output end of the second feature extraction module is connected to the input end of the first feature processing module; the output end of the first feature extraction module is connected to the input end of the second feature processing module; the output end of the second feature processing module is connected to the input end of the second feature analysis module; the output end of the second feature analysis module is connected to the third feature extraction module The input end of the module; the output end of the third feature extraction module is connected to the input end of the third processing module; the output end of the third feature processing module is connected to the input end of the fourth feature processing module; the output end of the fourth feature processing module is connected to the input end of the fifth feature processing module; the output end of the fifth feature processing module is connected to the input end of the third feature analysis module; the output end of the third feature analysis module is connected to the input end of the fourth feature extraction module; the output end of the fourth feature extraction module is connected to the input end of the sixth feature processing module; the output end of the sixth feature processing module is connected to the input end of the seventh feature processing module; the output end of the seventh feature processing module is connected to the input end of the eighth feature processing module; the output end of the eighth feature processing module is connected to the input end of the ninth feature processing module; the output end of the ninth feature processing module is connected to the input end of the tenth feature processing module; the output end of the tenth feature processing module is connected to the input end of the fourth feature analysis module; the output end of the fourth feature analysis module is connected to the input end of the fifth feature extraction module; the output end of the fifth feature extraction module is connected to the input end of the eleventh feature processing module; the output end of the eleventh feature processing module is configured to output the model output of the target signal recognition model.

[0088] It should be noted that the above-mentioned target signal recognition model has the characteristics of extracting high-level abstract features and better network performance, and will not cause the problem of network degradation as the network deepens. It can efficiently extract key features, thereby accurately realizing the recognition and classification of signals, and the embodiments of the present invention do not limit this.

[0089] It should be noted that the above-mentioned first model input and second model input respectively represent the first extracted signal feature information and the second extracted signal feature information, and the model output represents the target signal recognition result information, which is not limited in the embodiment of the present invention.

[0090] It should be noted that the model architectures of the first feature dimensionality reduction module and the second feature dimensionality reduction module are consistent, and the embodiment of the present invention does not limit this.

[0091] It should be noted that the model architectures of the above-mentioned first feature extraction module, second feature extraction module, third feature extraction module, fourth feature extraction module, and fifth feature extraction module are consistent, and the embodiments of the present invention do not limit them.

[0092] It should be noted that the model architectures of the first feature analysis module, the second feature analysis module, the third feature analysis module, and the fourth feature analysis module are consistent, and the embodiment of the present invention does not limit this.

[0093] It should be noted that the model architectures of the above-mentioned first feature processing module, second feature processing module, third feature processing module, fourth feature processing module, fifth feature processing module, sixth feature processing module, seventh feature processing module, eighth feature processing module, ninth feature processing module, tenth feature processing module and eleventh feature processing module are consistent, and the embodiments of the present invention do not limit them.

[0094] It should be noted that the above-mentioned first fusion module is constructed based on element-by-element addition, which is not limited in the embodiment of the present invention.

[0095] It should be noted that the above-mentioned first convolution module can be constructed based on the deep convolutional network of CBAM, which is not limited in the embodiment of the present invention.

[0096] It should be noted that the above-mentioned first normalization module is constructed based on the batch normalization layer, which is not limited in this embodiment of the present invention.

[0097] It should be noted that the above-mentioned first activation module is constructed based on the RELU activation function, which is not limited in this embodiment of the present invention.

[0098] It should be noted that the above-mentioned first pooling module is constructed based on the maximum pooling layer, which is not limited in the embodiment of the present invention.

[0099] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0100] In another optional embodiment, Figure 5As shown, the first feature dimensionality reduction module includes a first convolution unit, a second convolution unit, a first normalization unit, a second normalization unit, a first activation unit, a second activation unit and a first fusion unit; wherein,

[0101] The input end of the first convolution unit and the input end of the first fusion unit are configured to receive the module input of the first feature dimensionality reduction module; the output end of the first convolution unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is connected to the input end of the second activation unit; the output end of the second activation unit is configured to output the module output of the first feature dimensionality reduction module.

[0102] It should be noted that the module input of the above-mentioned first feature dimensionality reduction module represents the first model input, and the module output of the first feature dimensionality reduction module represents the data output by the output end of the second activation unit, which is not limited in the embodiment of the present invention.

[0103] It should be noted that the above-mentioned first feature dimensionality reduction module realizes dimensionality reduction processing of the data dimension through the convolution layer in the residual structure. After processing by the first feature dimensionality reduction module, the data dimension can be reduced by half to reduce the number of parameters in the subsequent model processing process, thereby reducing the amount of calculation and improving the model's processing efficiency for signal recognition and classification. The embodiments of the present invention do not limit this.

[0104] It should be noted that the convolution kernel size of the first convolution unit and the second convolution unit is 1×3, and the number of channels is 2, which is not limited in the embodiment of the present invention.

[0105] It should be noted that the above-mentioned first normalization unit and second normalization unit are constructed based on the batch normalization layer, which is not limited in this embodiment of the present invention.

[0106] It should be noted that the above-mentioned first activation unit and second activation unit are constructed based on the RELU activation function, which is not limited in the embodiment of the present invention.

[0107] It should be noted that the above-mentioned first fusion unit is constructed based on element-by-element addition operation, which is not limited in the embodiment of the present invention.

[0108] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0109] In another optional embodiment, Figure 6As shown, the first feature extraction module includes a first pooling unit, a second pooling unit, a third pooling unit, a fourth pooling unit, a fifth pooling unit, a sixth pooling unit, a multi-layer perceptron, a second fusion unit, a third fusion unit and a fourth fusion unit; wherein,

[0110] The input end of the first pooling unit, the input end of the second pooling unit and the input end of the third fusion unit are configured to receive the first module input of the first feature extraction module; the output end of the first pooling unit and the output end of the second pooling unit are both connected to the input end of the multi-layer perceptron; the output end of the multi-layer perceptron is respectively connected to the input end of the third pooling unit and the input end of the fourth pooling unit; the output end of the third pooling unit and the output end of the fourth pooling unit are both connected to the input end of the second fusion unit; the output end of the second fusion unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is respectively connected to the input end of the fifth pooling unit and the input end of the fourth fusion unit; the output end of the fifth pooling unit is connected to the input end of the sixth pooling unit; the output end of the sixth pooling unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is configured to output the first module output of the first feature extraction module.

[0111] It should be noted that the first module input of the above-mentioned first feature extraction module represents the data output by the output end of the first convolution module, and the first module output of the first feature extraction module represents the data output by the output end of the first feature extraction module, which is not limited in the embodiment of the present invention.

[0112] It should be noted that the above-mentioned first feature extraction module improves the feature expression by dynamically focusing on the important areas of the input feature map through multiple pooling units and perceptron feature extraction, extracts more targeted features, and effectively extracts and fuses features at different scales to further enhance the feature expression capability of the target signal recognition model. The embodiments of the present invention do not limit this.

[0113] It should be noted that the second pooling unit, the fourth pooling unit, and the sixth pooling unit are constructed based on the average pooling layer, which is not limited in the embodiment of the present invention. The first pooling unit, the third pooling unit, and the fifth pooling unit are constructed based on the maximum pooling layer, which is not limited in the embodiment of the present invention.

[0114] It should be noted that the above-mentioned second fusion unit, third fusion unit and fourth fusion unit are constructed based on element-by-element addition, which is not limited in the embodiment of the present invention.

[0115] It should be noted that the number of hidden layers of the multilayer perceptron is greater than 1, and the number of neurons in a single layer is not less than 20, which is not limited in the embodiment of the present invention.

[0116] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0117] In an optional embodiment, if Figure 7 As shown, the first feature analysis module includes a third convolution unit, a fourth convolution unit, a fifth convolution unit, a sixth convolution unit, a third normalization unit, a fourth normalization unit, a fifth normalization unit, a sixth normalization unit, a third activation unit, a fourth activation unit, a fifth activation unit and a fifth fusion unit; wherein,

[0118] The input end of the third convolution unit and the input end of the sixth convolution unit are both configured to receive the second module input of the first feature analysis module; the output end of the third convolution unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the fourth activation unit; the output end of the fourth activation unit is connected to the input end of the fifth convolution unit; the output end of the fifth convolution unit is connected to the input end of the fifth normalization unit; the output end of the fifth normalization unit is connected to the input end of the fifth fusion unit; the output end of the sixth convolution unit is connected to the input end of the sixth normalization unit; the output end of the sixth normalization unit is connected to the input end of the fifth fusion unit; the output end of the fifth fusion unit is connected to the input end of the fifth activation unit; the output end of the fifth activation unit is configured to output the second module output of the first feature analysis module.

[0119] It should be noted that the second module input of the above-mentioned first feature analysis module represents the data output by the output end of the first pooling module, and the second module output of the first feature analysis module represents the data output by the second module output end of the first feature analysis module, which is not limited in the embodiment of the present invention.

[0120] It should be noted that the convolution kernel sizes of the third convolution unit, the fourth convolution unit, the fifth convolution unit, and the sixth convolution unit are 1×1, 3×3, 1×1, and 1×1, respectively, and the number of channels are 4, 4, 1, and 1, respectively, which are not limited in the embodiment of the present invention.

[0121] It should be noted that the third normalization unit, the fourth normalization unit, the fifth normalization unit, and the sixth normalization unit are all constructed based on the batch normalization layer, which is not limited in this embodiment of the present invention.

[0122] It should be noted that the third activation unit, the fourth activation unit, and the fifth activation unit are all constructed based on RELU activation, which is not limited in this embodiment of the present invention.

[0123] It should be noted that the above-mentioned fifth fusion unit is constructed based on element-by-element addition operation, which is not limited in the embodiment of the present invention.

[0124] It should be noted that the above-mentioned first feature analysis module realizes multi-dimensional extraction of signal features by utilizing the coordinated effect of different channel numbers of convolution units, thereby realizing the fusion analysis of multi-dimensional feature information of the model and improving the accuracy of signal recognition and classification. The embodiments of the present invention do not limit this.

[0125] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0126] In another optional embodiment, Figure 8 As shown, the first feature processing module includes a seventh convolution unit, an eighth convolution unit, a ninth convolution unit, a seventh normalization unit, an eighth normalization unit, a ninth normalization unit, a sixth activation unit, a seventh activation unit, an eighth activation unit and a sixth fusion unit; wherein,

[0127] The input end of the seventh convolution unit and the input end of the sixth fusion unit are configured to receive the third module input of the first feature processing module; the output end of the seventh convolution unit is connected to the input end of the seventh normalization unit; the output end of the seventh normalization unit is connected to the input end of the sixth activation unit; the output end of the sixth activation unit is connected to the input end of the eighth convolution unit; the output end of the eighth convolution unit is connected to the input end of the eighth normalization unit; the output end of the eighth normalization unit is connected to the input end of the seventh activation unit; the output end of the seventh activation unit is connected to the input end of the ninth convolution unit; the output end of the ninth convolution unit is connected to the input end of the ninth normalization unit; the output end of the ninth normalization unit is connected to the input end of the sixth fusion unit; the output end of the sixth fusion unit is connected to the input end of the eighth activation unit; the output end of the eighth activation unit is configured to output the third module output of the first feature processing module.

[0128] It should be noted that the third module input of the above-mentioned first feature processing module represents the data output from the output end of the second feature extraction module, and the third module output of the first feature processing module represents the data output from the third module output end of the first feature processing module, which is not limited in the embodiment of the present invention.

[0129] It should be noted that the convolution kernel sizes of the seventh convolution unit, the eighth convolution unit, and the ninth convolution unit are 1×1, 3×3, and 1×1, respectively, and the number of channels are 4, 4, and 1, respectively, which are not limited in the embodiment of the present invention.

[0130] It should be noted that the seventh normalization unit, the eighth normalization unit, and the ninth normalization unit are all constructed based on the batch normalization layer, which is not limited in this embodiment of the present invention.

[0131] It should be noted that the sixth activation unit, the seventh activation unit, and the eighth activation unit are all constructed based on the RELU activation function, which is not limited in this embodiment of the present invention.

[0132] It should be noted that the sixth fusion unit is constructed based on element-by-element addition, which is not limited in the embodiment of the present invention.

[0133] It should be noted that the above-mentioned first feature processing module is a feature recognition and extraction processing module that does not change the data dimension, that is, the first 1×1 convolution unit is used to reduce the data dimension from 256 to 64, and then the 1×1 convolution unit corresponding to the ninth convolution unit is used to restore the data dimension to 256, thereby realizing flexible dimensionality reduction and dimensionality increase of the data dimension, reducing the amount of data processing in the intermediate process, and ensuring both the extraction of high-level abstract features and the processing efficiency of the model. The embodiment of the present invention does not limit this.

[0134] It can be seen that implementing the signal recognition method described in the embodiment of the present invention is conducive to improving the accuracy of signal recognition, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0135] Example 2

[0136] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a signal recognition device disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 3 As shown, the device may include:

[0137] An acquisition module 201 is used to acquire information of a signal to be identified;

[0138] The first processing module 202 is used to pre-process the signal information to be identified to obtain target processed signal information;

[0139] The second processing module 203 is configured to perform recognition processing on the target processing signal information to obtain target signal recognition result information.

[0140] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0141] In another optional embodiment, Figure 3 As shown, the target processing signal information is identified and processed to obtain target signal identification result information, including:

[0142] Performing a first signal feature extraction on the target processed signal information to obtain first extracted signal feature information;

[0143] performing a second signal feature extraction on the target processed signal information to obtain second extracted signal feature information;

[0144] The target signal recognition model is used to perform recognition processing on the first extracted signal feature information and the second extracted signal feature information to obtain target signal recognition result information.

[0145] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0146] In another optional embodiment, Figure 3 As shown, the target signal recognition model includes a first feature dimensionality reduction module, a second feature dimensionality reduction module, a first fusion module, a first convolution module, a first feature extraction module, a second feature extraction module, a third feature extraction module, a fourth feature extraction module, a fifth feature extraction module, a first normalization module, a first activation module, a first pooling module, a first feature analysis module, a second feature analysis module, a third feature analysis module, a fourth feature analysis module, a first feature processing module, a second feature processing module, a third feature processing module, a fourth feature processing module, a fifth feature processing module, a sixth feature processing module, a seventh feature processing module, an eighth feature processing module, a ninth feature processing module, a tenth feature processing module and an eleventh feature processing module; wherein,

[0147] The input end of the first feature dimensionality reduction module is configured to receive the first model input of the target signal recognition model, and the output end of the first feature dimensionality reduction module is connected to the input end of the first fusion module; the input end of the second feature dimensionality reduction module is configured to receive the second model input of the target signal recognition model, and the output end of the second feature dimensionality reduction module is connected to the input end of the first fusion module; the output end of the first fusion module is connected to the input end of the first convolution module; the output end of the first convolution module is connected to the input end of the first feature extraction module; the output end of the first feature extraction module is connected to the input end of the first normalization module; the output end of the first normalization module is connected to the input end of the first activation module; the output end of the first activation module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the first feature analysis module; the output end of the first feature analysis module is connected to the input end of the second feature extraction module; the output end of the second feature extraction module is connected to the input end of the first feature processing module; the output end of the first feature extraction module is connected to the input end of the second feature processing module; the output end of the second feature processing module is connected to the input end of the second feature analysis module; the output end of the second feature analysis module is connected to the third feature extraction module The input end of the module; the output end of the third feature extraction module is connected to the input end of the third processing module; the output end of the third feature processing module is connected to the input end of the fourth feature processing module; the output end of the fourth feature processing module is connected to the input end of the fifth feature processing module; the output end of the fifth feature processing module is connected to the input end of the third feature analysis module; the output end of the third feature analysis module is connected to the input end of the fourth feature extraction module; the output end of the fourth feature extraction module is connected to the input end of the sixth feature processing module; the output end of the sixth feature processing module is connected to the input end of the seventh feature processing module; the output end of the seventh feature processing module is connected to the input end of the eighth feature processing module; the output end of the eighth feature processing module is connected to the input end of the ninth feature processing module; the output end of the ninth feature processing module is connected to the input end of the tenth feature processing module; the output end of the tenth feature processing module is connected to the input end of the fourth feature analysis module; the output end of the fourth feature analysis module is connected to the input end of the fifth feature extraction module; the output end of the fifth feature extraction module is connected to the input end of the eleventh feature processing module; the output end of the eleventh feature processing module is configured to output the model output of the target signal recognition model.

[0148] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0149] In another optional embodiment, Figure 3As shown, the first feature dimensionality reduction module includes a first convolution unit, a second convolution unit, a first normalization unit, a second normalization unit, a first activation unit, a second activation unit and a first fusion unit; wherein,

[0150] The input end of the first convolution unit and the input end of the first fusion unit are configured to receive the module input of the first feature dimensionality reduction module; the output end of the first convolution unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is connected to the input end of the second activation unit; the output end of the second activation unit is configured to output the module output of the first feature dimensionality reduction module.

[0151] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0152] In another optional embodiment, Figure 3 As shown, the first feature extraction module includes a first pooling unit, a second pooling unit, a third pooling unit, a fourth pooling unit, a fifth pooling unit, a sixth pooling unit, a multi-layer perceptron, a second fusion unit, a third fusion unit and a fourth fusion unit; wherein,

[0153] The input end of the first pooling unit, the input end of the second pooling unit and the input end of the third fusion unit are configured to receive the first module input of the first feature extraction module; the output end of the first pooling unit and the output end of the second pooling unit are both connected to the input end of the multi-layer perceptron; the output end of the multi-layer perceptron is respectively connected to the input end of the third pooling unit and the input end of the fourth pooling unit; the output end of the third pooling unit and the output end of the fourth pooling unit are both connected to the input end of the second fusion unit; the output end of the second fusion unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is respectively connected to the input end of the fifth pooling unit and the input end of the fourth fusion unit; the output end of the fifth pooling unit is connected to the input end of the sixth pooling unit; the output end of the sixth pooling unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is configured to output the first module output of the first feature extraction module.

[0154] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0155] In another optional embodiment, Figure 3 As shown, the first feature analysis module includes a third convolution unit, a fourth convolution unit, a fifth convolution unit, a sixth convolution unit, a third normalization unit, a fourth normalization unit, a fifth normalization unit, a sixth normalization unit, a third activation unit, a fourth activation unit, a fifth activation unit and a fifth fusion unit; wherein,

[0156] The input end of the third convolution unit and the input end of the sixth convolution unit are both configured to receive the second module input of the first feature analysis module; the output end of the third convolution unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the fourth activation unit; the output end of the fourth activation unit is connected to the input end of the fifth convolution unit; the output end of the fifth convolution unit is connected to the input end of the fifth normalization unit; the output end of the fifth normalization unit is connected to the input end of the fifth fusion unit; the output end of the sixth convolution unit is connected to the input end of the sixth normalization unit; the output end of the sixth normalization unit is connected to the input end of the fifth fusion unit; the output end of the fifth fusion unit is connected to the input end of the fifth activation unit; the output end of the fifth activation unit is configured to output the second module output of the first feature analysis module.

[0157] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0158] In another optional embodiment, Figure 3 As shown, the first feature processing module includes a seventh convolution unit, an eighth convolution unit, a ninth convolution unit, a seventh normalization unit, an eighth normalization unit, a ninth normalization unit, a sixth activation unit, a seventh activation unit, an eighth activation unit and a sixth fusion unit; wherein,

[0159] The input end of the seventh convolution unit and the input end of the sixth fusion unit are configured to receive the third module input of the first feature processing module; the output end of the seventh convolution unit is connected to the input end of the seventh normalization unit; the output end of the seventh normalization unit is connected to the input end of the sixth activation unit; the output end of the sixth activation unit is connected to the input end of the eighth convolution unit; the output end of the eighth convolution unit is connected to the input end of the eighth normalization unit; the output end of the eighth normalization unit is connected to the input end of the seventh activation unit; the output end of the seventh activation unit is connected to the input end of the ninth convolution unit; the output end of the ninth convolution unit is connected to the input end of the ninth normalization unit; the output end of the ninth normalization unit is connected to the input end of the sixth fusion unit; the output end of the sixth fusion unit is connected to the input end of the eighth activation unit; the output end of the eighth activation unit is configured to output the third module output of the first feature processing module.

[0160] It can be seen that implementation Figure 3 The described signal recognition device is beneficial to improving the signal recognition accuracy, thereby solving the problem of low recognition accuracy of specific protocol signals in complex electromagnetic environments such as low signal-to-noise ratio and harsh transmission environment.

[0161] Example 3

[0162] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of another signal recognition device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0163] A memory 301 storing executable program code;

[0164] a processor 302 coupled to the memory 301;

[0165] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the signal recognition method described in the first embodiment.

[0166] Example 4

[0167] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the signal recognition method described in the first embodiment.

[0168] Example 5

[0169] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the signal recognition method described in the first embodiment.

[0170] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0171] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0172] Finally, it should be noted that the signal recognition method and device disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A signal recognition method, characterized in that: The method comprises: Obtaining information of a signal to be identified; Pre-processing the signal information to be identified to obtain target processed signal information; Performing identification processing on the target processing signal information to obtain target signal identification result information; The step of performing identification processing on the target processing signal information to obtain target signal identification result information includes: performing a first signal feature extraction on the target processed signal information to obtain first extracted signal feature information; performing a second signal feature extraction on the target processed signal information to obtain second extracted signal feature information; Using a target signal recognition model to perform recognition processing on the first extracted signal feature information and the second extracted signal feature information to obtain target signal recognition result information; The process of pre-processing the signal information to be identified to obtain target processed signal information includes: Performing noise reduction processing on the signal information to be identified to obtain first processed signal information; performing time domain waveform feature marking processing on the first processed signal information to obtain second processed signal information; Performing a fast Fourier transform on the second processed signal information to obtain frequency domain signal information; Perform frequency domain feature frame marking processing on the frequency domain signal information to obtain target processed signal information; The first signal feature extraction is performed on the target processed signal information to obtain first extracted signal feature information, including: Perform wavelet packet decomposition on the target processing signal information to obtain signal coefficient information; Performing coefficient threshold processing on the signal coefficient information to obtain signal threshold information; Performing wavelet packet reconstruction processing on the signal coefficient information and the signal threshold information to obtain first extracted signal feature information; Among them, the second signal feature extraction is to calculate the high-order spectrum of the signal and mark it according to its characteristics. The high-order spectrum signal feature extraction used in the second signal feature extraction is high-order cumulative amount and high-order spectrum feature; Among them, the target signal recognition model includes a first convolution module, and the first convolution module can be constructed based on a deep convolutional network of CBAM.

2. The signal recognition method according to claim 1, characterized in that: The target signal recognition model includes a first feature dimensionality reduction module, a second feature dimensionality reduction module, a first fusion module, a first convolution module, a first feature extraction module, a second feature extraction module, a third feature extraction module, a fourth feature extraction module, a fifth feature extraction module, a first normalization module, a first activation module, a first pooling module, a first feature analysis module, a second feature analysis module, a third feature analysis module, a fourth feature analysis module, a first feature processing module, a second feature processing module, a third feature processing module, a fourth feature processing module, a fifth feature processing module, a sixth feature processing module, a seventh feature processing module, an eighth feature processing module, a ninth feature processing module, a tenth feature processing module and an eleventh feature processing module; wherein, The input end of the first feature dimensionality reduction module is configured to receive the first model input of the target signal recognition model, and the output end of the first feature dimensionality reduction module is connected to the input end of the first fusion module; the input end of the second feature dimensionality reduction module is configured to receive the second model input of the target signal recognition model, and the output end of the second feature dimensionality reduction module is connected to the input end of the first fusion module; the output end of the first fusion module is connected to the input end of the first convolution module; the output end of the first convolution module is connected to the input end of the first feature extraction module; the output end of the first feature extraction module is connected to the input end of the first normalization module; the output end of the first normalization module is connected to the input end of the first activation module; the output end of the first activation module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the first feature analysis module; the output end of the first feature analysis module is connected to the input end of the second feature extraction module; the output end of the second feature extraction module is connected to the input end of the first feature processing module; the output end of the first feature extraction module is connected to the input end of the second feature processing module; the output end of the second feature processing module is connected to the input end of the second feature analysis module; the output end of the second feature analysis module is connected to the third feature extraction module The output end of the third feature extraction module is connected to the input end of the third feature processing module; the output end of the third feature processing module is connected to the input end of the fourth feature processing module; the output end of the fourth feature processing module is connected to the input end of the fifth feature processing module; the output end of the fifth feature processing module is connected to the input end of the third feature analysis module; the output end of the third feature analysis module is connected to the input end of the fourth feature extraction module; the output end of the fourth feature extraction module is connected to the input end of the sixth feature processing module; the output end of the sixth feature processing module is connected to the input end of the seventh feature processing module ; The output end of the seventh feature processing module is connected to the input end of the eighth feature processing module; the output end of the eighth feature processing module is connected to the input end of the ninth feature processing module; the output end of the ninth feature processing module is connected to the input end of the tenth feature processing module; the output end of the tenth feature processing module is connected to the input end of the fourth feature analysis module; the output end of the fourth feature analysis module is connected to the input end of the fifth feature extraction module; the output end of the fifth feature extraction module is connected to the input end of the eleventh feature processing module; the output end of the eleventh feature processing module is configured to output the model output of the target signal recognition model.

3. The signal recognition method according to claim 2, characterized in that: The first feature dimensionality reduction module includes a first convolution unit, a second convolution unit, a first normalization unit, a second normalization unit, a first activation unit, a second activation unit and a first fusion unit; wherein, The input end of the first convolution unit and the input end of the first fusion unit are configured to receive the module input of the first feature dimensionality reduction module; the output end of the first convolution unit is connected to the input end of the first normalization unit; the output end of the first normalization unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second normalization unit; the output end of the second normalization unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is connected to the input end of the second activation unit; and the output end of the second activation unit is configured to output the module output of the first feature dimensionality reduction module.

4. The signal recognition method according to claim 2, characterized in that: The first feature extraction module includes a first pooling unit, a second pooling unit, a third pooling unit, a fourth pooling unit, a fifth pooling unit, a sixth pooling unit, a multi-layer perceptron, a second fusion unit, a third fusion unit and a fourth fusion unit; wherein, The input end of the first pooling unit, the input end of the second pooling unit and the input end of the third fusion unit are configured to receive the first module input of the first feature extraction module; the output end of the first pooling unit and the output end of the second pooling unit are both connected to the input end of the multi-layer perceptron; the output end of the multi-layer perceptron is respectively connected to the input end of the third pooling unit and the input end of the fourth pooling unit; the output end of the third pooling unit and the output end of the fourth pooling unit are both connected to the input end of the second fusion unit; the output end of the second fusion unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is respectively connected to the input end of the fifth pooling unit and the input end of the fourth fusion unit; the output end of the fifth pooling unit is connected to the input end of the sixth pooling unit; the output end of the sixth pooling unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is configured to output the first module output of the first feature extraction module.

5. The signal recognition method according to claim 2, characterized in that: The first feature analysis module includes a third convolution unit, a fourth convolution unit, a fifth convolution unit, a sixth convolution unit, a third normalization unit, a fourth normalization unit, a fifth normalization unit, a sixth normalization unit, a third activation unit, a fourth activation unit, a fifth activation unit and a fifth fusion unit; wherein, The input end of the third convolution unit and the input end of the sixth convolution unit are both configured to receive the second module input of the first feature analysis module; the output end of the third convolution unit is connected to the input end of the third normalization unit; the output end of the third normalization unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the fourth convolution unit; the output end of the fourth convolution unit is connected to the input end of the fourth normalization unit; the output end of the fourth normalization unit is connected to the input end of the fourth activation unit; the output end of the fourth activation unit is connected to the input end of the fifth convolution unit; the output end of the fifth convolution unit is connected to the input end of the fifth normalization unit; the output end of the fifth normalization unit is connected to the input end of the fifth fusion unit; the output end of the sixth convolution unit is connected to the input end of the sixth normalization unit; the output end of the sixth normalization unit is connected to the input end of the fifth fusion unit; the output end of the fifth fusion unit is connected to the input end of the fifth activation unit; and the output end of the fifth activation unit is configured to output the second module output of the first feature analysis module.

6. The signal recognition method according to claim 2, characterized in that: The first feature processing module includes a seventh convolution unit, an eighth convolution unit, a ninth convolution unit, a seventh normalization unit, an eighth normalization unit, a ninth normalization unit, a sixth activation unit, a seventh activation unit, an eighth activation unit and a sixth fusion unit; wherein, The input end of the seventh convolution unit and the input end of the sixth fusion unit are configured to receive the third module input of the first feature processing module; the output end of the seventh convolution unit is connected to the input end of the seventh normalization unit; the output end of the seventh normalization unit is connected to the input end of the sixth activation unit; the output end of the sixth activation unit is connected to the input end of the eighth convolution unit; the output end of the eighth convolution unit is connected to the input end of the eighth normalization unit; the output end of the eighth normalization unit is connected to the input end of the seventh activation unit; the output end of the seventh activation unit is connected to the input end of the ninth convolution unit; the output end of the ninth convolution unit is connected to the input end of the ninth normalization unit; the output end of the ninth normalization unit is connected to the input end of the sixth fusion unit; the output end of the sixth fusion unit is connected to the input end of the eighth activation unit; the output end of the eighth activation unit is configured to output the third module output of the first feature processing module.

7. A signal recognition device, characterized in that: The device comprises: An acquisition module, used to acquire information of a signal to be identified; A first processing module is used to pre-process the signal information to be identified to obtain target processed signal information; A second processing module is used to perform recognition processing on the target processing signal information to obtain target signal recognition result information; The step of performing identification processing on the target processing signal information to obtain target signal identification result information includes: performing a first signal feature extraction on the target processed signal information to obtain first extracted signal feature information; performing a second signal feature extraction on the target processed signal information to obtain second extracted signal feature information; Using a target signal recognition model to perform recognition processing on the first extracted signal feature information and the second extracted signal feature information to obtain target signal recognition result information; The process of pre-processing the signal information to be identified to obtain target processed signal information includes: Performing noise reduction processing on the signal information to be identified to obtain first processed signal information; performing time domain waveform feature marking processing on the first processed signal information to obtain second processed signal information; Performing a fast Fourier transform on the second processed signal information to obtain frequency domain signal information; Perform frequency domain feature frame marking processing on the frequency domain signal information to obtain target processed signal information; The first signal feature extraction is performed on the target processed signal information to obtain first extracted signal feature information, including: Perform wavelet packet decomposition on the target processing signal information to obtain signal coefficient information; Performing coefficient threshold processing on the signal coefficient information to obtain signal threshold information; Performing wavelet packet reconstruction processing on the signal coefficient information and the signal threshold information to obtain first extracted signal feature information; Among them, the second signal feature extraction is to calculate the high-order spectrum of the signal and mark it according to its characteristics. The high-order spectrum signal feature extraction used in the second signal feature extraction is high-order cumulative amount and high-order spectrum feature; Among them, the target signal recognition model includes a first convolution module, and the first convolution module can be constructed based on a deep convolutional network of CBAM.

8. A signal recognition device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the signal recognition method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the signal recognition method according to any one of claims 1 to 6.

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