Structural signal matching method, device and equipment, medium and computer product

Abstract

The invention discloses a structure signal matching method, a structure signal matching device, structure signal matching equipment, a storage medium and a computer product, and relates to the technical field of signal identification. Adaptive adjustment is carried out on the template and the signals to ensure the recognition effect of the same type of signals under different signal-to-noise ratios; in addition, one of the target signal and the template signal, which has a higher signal-to-noise ratio, is subjected to modified logarithmic transformation in a time-Fourier domain so as to weaken the influence of the signal-to-noise ratio factor on signal identification, and then is mapped into a modified time-Fourier matrix through the steps of denoising, standardization and the like; further identifying and matching the structure signal by measuring the similarity of the corrected time-Fourier matrixes of the target signal and the template signal; the method does not depend on a large number of labeled samples, and is low in cost and easy to deploy; meanwhile, the recognition principle is not a black box system, strict mathematical guarantee is achieved, and interpretability is higher.

Description

Technical Field

[0001] This application relates to the technical field of signal recognition, and particularly to a structural signal matching method, apparatus, device, storage medium, and computer product. Background Art

[0002] The detection and recognition of communication signals have important practical significance in the fields of military reconnaissance, electronic countermeasure, information network security, etc. Existing signal recognition algorithms are mainly divided into two categories:

[0003] The first category is traditional signal recognition algorithms, which usually use the parameters of the signal itself or related codewords as the judgment basis. When the signal-to-noise ratio of the signal is poor and difficult to demodulate or a new signal appears, its recognition ability and rapid iterative update ability will encounter challenges, and corresponding adaptation changes need to be made to the signal recognition algorithm.

[0004] The second category is recognition algorithms based on deep neural networks. This type of algorithm has excellent recognition effects under the closed-set conditions of sufficient signal samples and stable distributions. However, in practical applications, it is difficult to meet the independent and identically distributed assumption, and the electromagnetic environment is becoming increasingly complex; in addition, the model depends on a large number of labeled samples, and the black box system cannot provide strict mathematical guarantees.

[0005] Therefore, it is necessary to provide a structural signal matching method based on a template-signal adaptive adjustment mechanism to solve the technical problems existing in the prior art, such as relying on a large number of labeled samples, being unable to provide strict mathematical guarantees, and poor algorithm adaptation. Summary of the Invention

[0006] The main purpose of this application is to provide a structural signal matching method, apparatus, device, storage medium, and computer product. By simply adding a suitable matching template for different structural signals without modifying the overall architecture of the algorithm, the signal data collected by the hardware can be directly recognized and the type to which the signal belongs can be output.

[0007] To achieve the above object, this application proposes a structural signal matching method, which includes:

[0008] Obtain the signal data of the signal to be recognized and the corresponding signal structure composite template;

[0009] Wherein, the signal structure composite template includes the signal data of the template signal, the time-Fourier matrix pre-mapped by the template signal in the matching section, and the time offset of the matching section moment;

[0010] Determine the position of the signal start moment according to the signal to be recognized, and obtain the corresponding signal start timestamp;

[0011] Select the matching section of the signal to be recognized according to the signal start timestamp and the time offset of the matching section moment;

[0012] According to the matching section of the signal to be recognized, match the time-Fourier matrix pre-mapped by the corresponding template signal, and obtain the time resolution and frequency resolution corresponding to the matrix;

[0013] According to the time resolution and frequency resolution, map the matching section of the signal to be recognized onto the time-Fourier domain;

[0014] Perform corresponding logarithmic mapping on the time-Fourier matrix of the signal to be recognized;

[0015] Compare the signal-to-noise ratios of the template signal and the signal to be recognized, and calculate the logarithmic matching coefficient for the one with the higher signal-to-noise ratio;

[0016] Perform adaptive adjustment according to the logarithmic matching coefficient;

[0017] Based on the similarity, perform matching determination to obtain the corresponding matching determination result.

[0018] In one embodiment, when the signal-to-noise ratio of the signal to be recognized is high, there is the following relational expression:

[0019] max(log 10 F T -log 10 N T )=max(log k F S -log k N S );

[0020] Wherein, F T is the time-Fourier matrix of the template signal, N T is the noise level of the template signal, and F S is the time-Fourier matrix of the signal to be recognized, N S is the noise level of the signal to be recognized, and k is the logarithm base.

[0021] In one embodiment, the logarithm base k is obtained according to the relational expression and output as the logarithmic matching coefficient; wherein,

[0022] In one embodiment, the adaptive adjustment according to the logarithmic matching coefficient includes:

[0023] Diminution alignment step: According to the logarithmic matching coefficient, perform adaptive adjustment on the one with the higher signal-to-noise ratio of the template signal and the signal to be recognized, so that the difference between the peak value of the time-Fourier matrix of the adjusted signal after adjustment and the noise is approximately equal.

[0024] In one embodiment, the matching determination based on the similarity includes:

[0025] Steps for calculating structural similarity: Calculate the similarity between the time-Fourier matrix of the template signal and the signal to be recognized after impairment alignment to obtain the similarity difference r;

[0026] Steps for threshold similarity determination: Determine whether the similarity difference is greater than the threshold α; if it is greater, they are not similar, otherwise they are similar; finally, output the determination result according to the standard interface.

[0027] In one embodiment, the similarity difference r is calculated by the following formula:

[0028]

[0029] where is the average amplitude of the template signal, is the average amplitude of the signal to be recognized, and i and j are matrix row and column variables respectively.

[0030] In a second aspect, to achieve the above object, the present application further provides a structural signal matching device, and the device includes:

[0031] An acquisition module that acquires the signal data of the signal to be recognized and the corresponding signal structure composite template;

[0032] Determine the position of the signal start time according to the signal to be recognized to obtain the corresponding signal start timestamp;

[0033] Select the matching section of the signal to be recognized according to the signal start timestamp and the time offset of the matching section;

[0034] A mapping module that matches the time-Fourier matrix pre-mapped by the corresponding template signal according to the matching section of the signal to be recognized, and obtains the corresponding time resolution and frequency resolution of the matrix;

[0035] Map the matching section of the signal to be recognized to the time-Fourier domain according to the time resolution and frequency resolution;

[0036] Perform corresponding logarithmic mapping on the time-Fourier matrix of the signal to be recognized;

[0037] A matching module that compares the signal-to-noise ratios of the template signal and the signal to be recognized, and calculates the logarithmic matching coefficient for the one with the higher signal-to-noise ratio;

[0038] An adjustment module that performs adaptive adjustment according to the logarithmic matching coefficient;

[0039] A determination module that performs matching determination based on similarity to obtain the corresponding matching determination result.

[0040] In a third aspect, to achieve the above object, the present application further provides a structural signal matching device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the above structural signal matching method.

[0041] In a fourth aspect, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the above structural signal matching method are implemented.

[0042] In a fifth aspect, to achieve the above object, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the above structural signal matching method are implemented.

[0043] One or more technical solutions proposed by the present application have at least the following technical effects:

[0044] Compared with the existing method based on signal statistical features, the calculation method proposed by the present invention has stronger compatibility and can be recognized based on any model of hardware detection device. For different hardware devices, only the template signal data needs to be updated, and no changes need to be made to other parts of the algorithm; it also has stronger robustness in the recognition of structural signals and can well recognize similar signals under both low and high signal-to-noise ratio conditions; at the same time, it has flexible scalability. For the recognition requirements of new structural signals, corresponding templates can be added at any time without changing other parts of the algorithm. Compared with the existing method based on deep learning, the calculation method proposed by the present invention does not rely on a large number of labeled samples, has low cost and is easy to deploy; at the same time, the recognition principle has strict mathematical guarantees and is not a black box system, so it has stronger interpretability. Description of the Drawings

[0045] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0046] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a schematic flowchart of the structural signal matching method in an embodiment of the present application.

[0048] Figure 2It is a schematic flowchart of the structure signal matching method in a specific embodiment of this application.

[0049] Figure 3 It is a schematic structural diagram of the structure signal matching device of this application.

[0050] Figure 4 It is a schematic structural diagram of the structure signal matching device of this application;

[0051] Figure 5 It is a schematic pseudocode diagram of the structure signal matching algorithm provided by this application.

[0052] The realization of the purpose, functional features and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0054] In order to better understand the technical solutions of this application, the following will be described in detail with reference to the accompanying drawings of the specification and specific embodiments.

[0055] In the related art, the detection and recognition of communication signals have important practical significance in the fields of military reconnaissance, electronic countermeasures, information network security, etc. The existing signal recognition algorithms are mainly divided into two categories: First, the traditional signal recognition algorithms, which usually use the parameters of the signal itself or related codewords as the judgment basis. When the signal-to-noise ratio of the signal is poor and difficult to demodulate or a new signal appears, its recognition ability and rapid iterative update ability will encounter challenges; Second, the recognition algorithms based on deep neural networks, which have excellent recognition effects under the closed-set conditions of sufficient signal samples and stable distributions, but in actual applications, the independent and identically distributed assumption is difficult to meet, and the electromagnetic environment is becoming increasingly complex, which places high requirements on the generalization ability of the model.

[0056] In fact, due to its protocol requirements, communication signals usually have recognizable features in terms of structure, and these features have physical meanings in terms of time and frequency. Therefore, considering the matching and recognition of signals from the time-Fourier domain can make good use of these features, and at the same time, the template library can be continuously enriched during the recognition process to achieve an increasingly stable recognition effect.

[0057] Based on this, the embodiments of this application provide a structure signal matching method, referring to Figure 1 , Figure 1 It is a schematic flowchart of the first embodiment of the structure signal matching method of this application.

[0058] It should be noted that the execution entity of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a structure signal matching device, etc. that can implement the above functions. Hereinafter, taking the structure signal matching device as an example, this embodiment and the following embodiments will be described.

[0059] In this embodiment, the structure signal matching method includes the steps of:

[0060] Obtain the signal data of the signal to be recognized, and select a representative signal structure composite template;

[0061] Wherein, the signal structure composite template includes the signal data of the template signal, the time-Fourier matrix pre-mapped by the template signal in the matching section, and the time offset of the matching section;

[0062] According to the signal to be recognized, determine the position of the signal start time, and obtain the corresponding signal start timestamp;

[0063] According to the signal start timestamp and the time offset of the matching section, select the matching section of the signal to be recognized;

[0064] According to the matching section of the signal to be recognized, match the time-Fourier matrix pre-mapped by the corresponding template signal, and obtain the time resolution and frequency resolution corresponding to the matrix;

[0065] According to the time resolution and frequency resolution, map the matching section of the signal to be recognized to the time-Fourier domain;

[0066] Perform a corresponding logarithmic mapping on the time-Fourier matrix of the signal to be recognized;

[0067] Compare the signal-to-noise ratios of the template signal and the signal to be recognized, and calculate the logarithmic matching coefficient for the one with the higher signal-to-noise ratio;

[0068] Perform adaptive adjustment according to the logarithmic matching coefficient;

[0069] Based on the similarity, perform a matching determination to obtain the corresponding matching determination result.

[0070] It should be noted that in this embodiment, the signal type matching is performed by comparing the most distinguishable part of the signal in the time-Fourier domain, and the template and the signal can be adaptively adjusted to ensure the recognition effect of the same type of signal under different signal-to-noise ratios.

[0071] Specifically, first obtain the signal structure composite template from the structure signal template library. This template will include the pre-calculated template time-Fourier matrix, as well as parameters such as the time resolution, frequency resolution, matrix dynamic range, and time offset of the matching section corresponding to this matrix.

[0072] Then enter the formal algorithm process. The data input port acquires signal data and obtains the starting timestamp of the signal. Then, offset by the same offset as the time offset of the template matching section to obtain the matching section of the signal to be measured.

[0073] Before performing adaptive adjustment, first perform a time-Fourier transform on the matching section of the target signal according to the time resolution and spectral resolution of the template signal. Then, perform a logarithmic mapping on the time-Fourier matrix of the target signal and perform a logarithmic match with the template signal.

[0074] Logarithmic matching mainly calculates the logarithmic matching coefficient for the side with a higher signal-to-noise ratio between the template signal and the signal to be recognized, so that the dynamic ranges of the time-Fourier matrix of the target signal and the time-Fourier matrix of the template signal are approximately the same. Assuming the target signal is stronger, the following equation holds:

[0075] max(log 10 F T -log 10 N T )=max(log k F S -log k N S )

[0076] Where, F T is the time-Fourier matrix of the template signal, N T is the noise level of the template signal, and F S is the time-Fourier matrix of the signal to be recognized, N S is the noise level of the signal to be recognized, and k is the logarithm base.

[0077] To find the logarithm base k to make the above equation hold, it is equivalent to:

[0078]

[0079] Then:

[0080]

[0081] When the above adjustments are completed, the structural similarity calculation step can be performed. Then, judge whether the similarity difference r is greater than the threshold α through the threshold similarity judgment step. If it is greater, it is dissimilar; otherwise, it is similar. Finally, output the judgment result according to the standard interface.

[0082] In this specific embodiment, as Figure 5 shown, it is the implementation details of the structural signal matching algorithm based on the template-signal adaptive adjustment mechanism of the present invention. It should be noted that during in-domain logarithmic matching, due to:

[0083]

[0084] Therefore, in practical applications, logarithmic matching is equivalent to multiplying the original logarithmic value by a constant, which can be calculated together in the <template-signal> adaptive adjustment process. Then, the similarity difference between the two is measured by the following formula:

[0085]

[0086] Wherein, is the average amplitude of the template signal, is the average amplitude of the signal to be recognized, and i and j are the matrix row and column variables respectively; the denominator measures the difference of the time-Fourier matrix after logarithmic matching within the domain, and the numerator is the cross-term of the average amplitudes of the two, which plays the role of a normalization constant. When the difference r between the two is less than the threshold, it can be determined that the two belong to the same type of signal, otherwise they do not belong to the same type of signal.

[0087] In summary, the structural signal matching method provided by this application has at least the following beneficial effects:

[0088] 1. This method does not rely on a large number of labeled samples, has low cost, and is easy to deploy.

[0089] 2. Strong interpretability: The recognition principle has strict mathematical guarantees and is not a black-box system.

[0090] 3. Strong robustness: It can well recognize the same type of signals under both low signal-to-noise ratio and high signal-to-noise ratio conditions.

[0091] 4. Strong compatibility: It can be recognized based on any model of hardware detection device. For different hardware devices, only the template signal data needs to be updated, and no other parts of the algorithm need to be changed;

[0092] 5. Strong scalability: For the recognition requirements of new structural signals, corresponding templates can be added at any time without changing other parts of the algorithm.

[0093] This application also provides a structural signal matching device. Please refer to Figure 3 The structural signal matching device includes:

[0094] An acquisition module, which acquires the signal data of the signal to be recognized and the corresponding signal structure composite template;

[0095] Determine the starting time position of the signal according to the signal to be recognized to obtain the corresponding signal start timestamp;

[0096] Select the matching section of the signal to be recognized according to the signal start timestamp and the time offset of the matching section;

[0097] A mapping module, which matches the time-Fourier matrix pre-mapped by the corresponding template signal according to the matching section of the signal to be recognized, and obtains the corresponding time resolution and frequency resolution of the matrix;

[0098] According to the time resolution and frequency resolution, map the matching section of the signal to be recognized onto the time-Fourier domain;

[0099] Perform corresponding logarithmic mapping on the time-Fourier matrix of the signal to be recognized;

[0100] A matching module, which compares the signal-to-noise ratios of the template signal and the signal to be recognized, and calculates the logarithmic matching coefficient for the one with the higher signal-to-noise ratio;

[0101] An adjustment module, which performs adaptive adjustment according to the logarithmic matching coefficient;

[0102] A determination module, which performs matching determination based on the similarity and obtains the corresponding matching determination result.

[0103] The structure signal matching device provided by this application adopts the structure signal matching method in the above embodiment, and can solve the technical problem of low signal type recognition efficiency. Compared with the related technology, the beneficial effects of the structure signal matching device provided by this application are the same as those of the structure signal matching method provided by the above embodiment, and other technical features in the structure signal matching device are the same as the features disclosed in the method of the above embodiment, and will not be elaborated here.

[0104] This application provides a structure signal matching device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the structure signal matching method in the above embodiment.

[0105] Next, refer to Figure 4 , which shows a schematic structural diagram of a structure signal matching device suitable for implementing the embodiments of this application. The structure signal matching device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The structure signal matching device shown is only an example, and should not bring any limitation to the functions and usage scopes of the embodiments of this application.

[0106] As Figure 4 shown, the structure signal matching device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the structure signal matching device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the structure signal matching device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a structure signal matching device having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0107] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0108] The structure signal matching device provided in the present application adopts the structure signal matching method in the above embodiments, and can solve the technical problem of low signal type recognition efficiency. Compared with the related art, the beneficial effects of the structure signal matching device provided in the present application are the same as those of the structure signal matching method provided in the above embodiments, and other technical features in the structure signal matching device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0109] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0110] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0111] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the structure signal matching method in the above embodiments.

[0112] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0113] The above computer-readable storage medium can be included in the structure signal matching device; it can also exist separately and not be assembled into the structure signal matching device.

[0114] The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the structure signal matching device, the structure signal matching device is enabled to implement the above structure signal matching method.

[0115] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0117] The modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0118] The readable storage medium provided in this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned structural signal matching method, and can solve the technical problem of low signal type recognition efficiency. Compared with the related art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the structural signal matching method provided in the above embodiments, and will not be elaborated here.

[0119] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the structural signal matching method as described above.

[0120] The computer program product provided by the present application can solve the technical problem of low signal type recognition efficiency. Compared with the related art, the beneficial effects of the computer program product provided by the present application are the same as those of the structural signal matching method provided by the above embodiments, and will not be elaborated here.

[0121] The above are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A structure signal matching method, characterized in that: The method includes: Acquire signal data of a signal to be identified and a corresponding signal structure composite template; Wherein, the signal structure composite template includes signal data of the template signal, a time-Fourier matrix of the template signal pre-map of the matching section, and a matching section time offset; According to the signal to be identified, determine the signal start time position and obtain the corresponding signal start timestamp; Select the matching segment of the signal to be identified according to the signal start timestamp and the matching segment time offset; According to the matching section of the signal to be identified, the time-Fourier matrix pre-mapped by the corresponding template signal is matched, and the time resolution and frequency resolution corresponding to the matrix are obtained; According to the time resolution and the frequency resolution, the matching segment of the signal to be identified is mapped to the time-Fourier domain; Make corresponding logarithmic mapping on the time-Fourier matrix of the signal to be identified; Compare the signal-to-noise ratios of the template signal and the signal to be identified, and calculate the logarithmic matching coefficient for the one with a higher signal-to-noise ratio; Adaptive adjustment is performed based on the logarithmic matching coefficient; A matching judgment is performed based on the similarity to obtain a corresponding matching judgment result.

2. The method according to claim 1, characterized in that When the signal-to-noise ratio of the signal to be identified is high, the following relationship exists: max(log 10 F T -log 10 N T )=max(log k F S -log k N S ); Among them, F T is the time-Fourier matrix of the template signal, N T is the noise level of the template signal, and F S is the time-Fourier matrix of the signal to be identified, N S is the noise level of the signal to be identified, and k is the logarithmic base.

3. The method according to claim 2, characterized in that The logarithmic base k is obtained according to the relationship and output as the logarithmic matching coefficient; where, 4. The method according to claim 1, characterized in that Adaptive adjustment based on logarithmic matching coefficients includes: Impairment alignment step: Adaptively adjust the template signal and the signal to be identified with a higher signal-to-noise ratio according to the logarithmic matching coefficient, so that the difference between the peak value of the adjusted time-Fourier matrix of the adjusted signal minus the noise is approximately equal.

5. The method according to claim 4, characterized in that Matching decisions based on similarity include: Structural similarity calculation steps: perform similarity calculation on the time-Fourier matrix of the template signal after the impairment alignment and the signal to be identified to obtain the similarity difference r; Threshold similarity determination step: determine whether the similarity difference is greater than the threshold α; if greater, then they are not similar, otherwise similar; finally, output the determination result according to the standard interface.

6. The method according to claim 5, characterized in that The similarity difference is calculated by the following formula: in, is the average amplitude of the template signal, is the average amplitude of the signal to be identified, i and j are the matrix row and column variables respectively.

7. A structural signal matching device, characterized in that: The device comprises: An acquisition module, which acquires signal data of a signal to be identified and a corresponding signal structure composite template; According to the signal to be identified, determine the signal start time position and obtain the corresponding signal start timestamp; Select the matching segment of the signal to be identified according to the signal start timestamp and the matching segment time offset; A mapping module matches the pre-mapped time-Fourier matrix of the corresponding template signal according to the matching segment of the signal to be identified, and obtains the time resolution and frequency resolution corresponding to the matrix; According to the time resolution and the frequency resolution, the matching segment of the signal to be identified is mapped to the time-Fourier domain; Make corresponding logarithmic mapping on the time-Fourier matrix of the signal to be identified; The matching module compares the signal-to-noise ratio of the template signal and the signal to be identified, and calculates the logarithmic matching coefficient for the one with a higher signal-to-noise ratio; An adjustment module performs adaptive adjustment based on the logarithmic matching coefficient; The determination module performs matching determination based on the similarity and obtains the corresponding matching determination result.

8. A structural signal matching device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the structure signal matching method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the structure signal matching method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the structure signal matching method according to any one of claims 1 to 6 are implemented.

Images