A method, device and medium for signal detection and recognition based on template matching

Abstract

This application discloses a signal detection and identification method, device, and medium based on template matching, relating to the field of telecommunications technology. The method includes: determining a signal sequence template based on a pre-set reconnaissance target and signal frame format; performing parallel processing on broadband data to obtain multiple narrowband signals; performing time-domain detection on the narrowband signals using a pre-set dual sliding window to determine the start and end positions of the narrowband signals and the corresponding signal-to-noise ratio (SNR); performing time-frequency domain two-dimensional correlation detection on the narrowband signals using a pre-set sequence signal template to determine the frequency offset and signal type of the narrowband signals; statistically analyzing the detection results based on the frequency offset and signal type; and outputting a broadband reconnaissance result list based on the detection results. This application obtains narrowband signals through parallel processing of broadband data, locates signals and obtains SNR using a dual sliding window, detects frequency offset and type using time-frequency domain two-dimensional correlation, and statistically lists the results; the process is efficient, accurate, and comprehensive.

Description

Technical Field

[0001] This application relates to the field of telecommunications technology, and in particular to a signal detection and identification method, device and medium based on template matching. Background Technology

[0002] With the increasing frequency of electronic warfare, the types of new signal systems are becoming more diverse, and the electromagnetic situation is exhibiting characteristics of dynamism, complexity, and high density. Broadband data detection and identification, as a key means of electronic warfare, electronic reconnaissance, and intelligence gathering, can obtain crucial information such as the target's location, trajectory, platform, system, and communication capabilities by detecting, extracting, separating, identifying, and interpreting intercepted broadband data. This allows for rapid determination of the opponent's intentions and the formulation of corresponding countermeasures. Therefore, accurately detecting target signals in broadband data is of great significance. However, in real-world environments, the amount of data received by front-end sensor devices is enormous, with wide bandwidth. Furthermore, the actual signal spectrum is congested, with signals from various frequency bands, systems, and platform types interfering and overlapping, greatly increasing the difficulty of signal detection and separation. Therefore, effectively separating target signals from complex broadband data has become a crucial problem that urgently needs to be solved. Summary of the Invention

[0003] To address the aforementioned issues, this application proposes a signal detection and recognition method based on template matching. The method comprises: determining a signal sequence template according to a pre-set reconnaissance target and signal frame format; performing parallel processing on broadband data to obtain multiple narrowband signals; performing time-domain detection on the narrowband signals using a pre-set dual sliding window to determine the start and end positions of the narrowband signals and the corresponding signal-to-noise ratio; performing time-frequency domain two-dimensional correlation detection on the narrowband signals using a pre-set sequence signal template to determine the frequency offset and signal type of the narrowband signals; and statistically analyzing the detection results based on the frequency offset and signal type to output a broadband reconnaissance result list based on the detection results.

[0004] In one example, parallel processing of the broadband data specifically includes: downconverting the broadband data and performing overlap-preserving processing on the downconverted broadband data to filter the broadband data; determining the segment length and overlap length of the broadband data, and determining a decimation factor based on the segment length and overlap length to determine multiple narrowband signals based on the decimation factor.

[0005] In one example, determining multiple narrowband signals based on the decimation factor specifically includes: segmenting the broadband data according to the decimation factor to determine multiple signal segments; performing a first scaling transformation on the multiple signal segments to convert them to the frequency domain; balancing the multiple signal segments after the first scaling transformation and extracting frequency components from the balanced multiple signal segments to filter them; performing a second scaling transformation on the filtered multiple signal segments to convert them back to the time domain; and correcting the multiple signal segments according to a preset output phase to determine multiple narrowband signals.

[0006] In one example, the narrowband signal is subjected to time-domain detection using a pre-set dual sliding window. Specifically, this includes: determining two adjacent sliding windows of equal length, with a window length of not less than 128; determining the energy value within the two sliding windows, the signal-to-noise ratio, and the ratio of the energy between the two windows by sliding them point by point; if the energy within the window is stable, the signal-to-noise ratio is less than 6dB, and the ratio is between [0.7-1.5], then the signal is determined to be in a noise state based on the energy value.

[0007] In one example, the method further includes: if the energy within the window gradually increases and the ratio is greater than 3, then the signal is determined to be in a start state based on the energy value and the ratio; if the energy within the window gradually decreases and the ratio is less than -3, then the signal is determined to be in an end state based on the energy value and the ratio.

[0008] In one example, time-frequency domain two-dimensional correlation detection is performed on the narrowband signal using a pre-set sequence signal template. Specifically, this includes: determining a mutual ambiguity function based on the sequence signal template; and determining the arrival time difference and frequency offset of the narrowband signal based on the mutual ambiguity function. The calculation formula for the mutual ambiguity function is as follows:

[0009]

[0010] in, The mutual ambiguity coefficients of the signals. , It is a sequence signal. For frequency offset, Let T be the time difference and T be the period.

[0011] In one example, the method further includes: determining a spectral slice corresponding to the time difference of arrival, determining a maximum value based on the spectral slice, determining a sum of the time differences of arrival, and determining a maximum correlation value based on the maximum value and the sum.

[0012] In one example, determining the signal type of the broadband data specifically includes: determining the corresponding peak value based on the maximum correlation value, and determining an effective index based on the peak value, so as to determine the signal type based on the effective index.

[0013] On the other hand, this application also proposes a template matching-based signal detection and recognition device, comprising: 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, the instructions being executed by the at least one processor to enable the template matching-based signal detection and recognition device to perform: the method described in any of the examples above.

[0014] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to be the method described in any of the examples above.

[0015] This application employs parallel processing of broadband data, significantly improving data processing efficiency through down-conversion, overlap-preservation processing, and the determination of multiple narrowband signals based on extraction factors. It can quickly extract key narrowband signals from broadband data. A dual sliding window approach is used for time-domain detection. By determining the energy and noise state within the window, the start and end positions of the signal can be accurately identified, along with the signal-to-noise ratio, providing a reliable basis for subsequent analysis. A sequence signal template is used for two-dimensional correlation detection in the time and frequency domain. The mutual fuzzy function accurately determines the signal's arrival time difference and frequency shift. Further operations such as spectral slicing determine the maximum correlation value, comprehensively grasping the signal characteristics. Based on the maximum correlation value, peak values ​​and effective indicators are determined, thereby accurately identifying the signal type, effectively solving the signal identification problem and improving the accuracy and reliability of the reconnaissance results. This application features clear steps and rigorous logic. From signal template determination to the final output of the reconnaissance result list, each step is closely linked and progressively advanced, forming a complete and scientific signal detection and identification system that is easy to operate and promote in practice. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a flowchart illustrating a template-matching-based signal detection and recognition method in an embodiment of this application.

[0018] Figure 2 This is a flowchart illustrating the principle of shortwave full-band frequency domain DDC in the embodiments of this application;

[0019] Figure 3 This is a schematic diagram of the mutual fuzziness function in the embodiments of this application;

[0020] Figure 4 This is a time-frequency diagram of the received signal in an embodiment of this application;

[0021] Figure 5 This is a schematic diagram illustrating the maximum correlation value of each DTO slice in the embodiments of this application;

[0022] Figure 6 This is a schematic diagram illustrating the kernel function thread allocation when multiplying DDC data with a template in an embodiment of this application;

[0023] Figure 7 Calculating the correlation coefficient in the embodiments of this application A diagram illustrating kernel function thread allocation during the process;

[0024] Figure 8 Calculating the final correlation coefficient in the embodiments of this application A diagram illustrating kernel function thread allocation during the process;

[0025] Figure 9 This is a schematic diagram illustrating the comparison of detection accuracy in the embodiments of this application;

[0026] Figure 10 This is a schematic diagram of a template-matching-based signal detection and recognition device in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, in order to solve the above problems, this application provides a signal detection and recognition method based on template matching, the method comprising:

[0030] S101. Based on the pre-set reconnaissance target and signal frame format, determine the signal sequence template and perform parallel processing on the broadband data to obtain multiple narrowband signals.

[0031] Full-band signal processing is adopted to reduce the false alarm rate; template matching method is used to solve the problems of high false alarm rate and high false alarm rate under low signal-to-noise ratio conditions; at the same time, the robustness and effectiveness of the actual processing are improved to address the problem of drastic energy fluctuations caused by frequency sweeping and strong noise interference.

[0032] When a large number of narrowband digital downconversion (DDC) channels are required, conventional CPU-based DDC solutions struggle to achieve fast parallel processing, resulting in low processing efficiency. Since GPUs, as parallel processors, are better suited for parallel processing of multiple data streams, therefore… Figure 2 As shown, a high-efficiency parallel DDC technique based on frequency domain filtering and GPU acceleration is designed. The input signal x(n) first enters the overlap buffer module for preprocessing, and then undergoes FFT transformation to convert the signal to the frequency domain. Next, I / Q balancing is performed, and then the signal is split into multiple paths, each of which undergoes frequency component extraction. The extracted frequency components are then filtered in the frequency domain, and then IFFT transformation is performed to convert the signal back to the time domain. Finally, phase correction is performed, and the multi-processed signal is output. to This process enables multi-channel parallel processing and analysis of the input signal. Frequency-domain DDC technology utilizes FFT operations to perform down-conversion, filtering, and decimation of the input broadband data, thereby acquiring various narrowband signal data. In practical applications, since the data can only be processed in segments, it is necessary to ensure the phase continuity of each segment of the output narrowband signal data when using this technology. This is achieved in two ways: first, by using an overlap-preserving method to filter the broadband data, thus ensuring the phase continuity of the filtered broadband data; second, by setting the broadband data segment length and overlap length to integer multiples of the decimation factor, thereby ensuring the phase continuity of the output narrowband signal data. Based on the above ideas, the method for parallel multi-channel frequency-domain filtering of broadband data sequentially covers signal overlap buffering, large-scale FFT transformation, I / Q balancing, frequency component extraction, frequency-domain filtering, small-scale IFFT transformation, and output phase correction. Each step is accelerated in parallel by programming on the GPU using a unified computing device architecture based on GPU programming methods.

[0033] S102. Perform time-domain detection on the narrowband signal through a pre-set dual sliding window to determine the start and end positions of the narrowband signal and the corresponding signal-to-noise ratio of the narrowband signal.

[0034] The dual sliding window signal detection algorithm is a time-domain signal detection algorithm with advantages such as low computational cost, high speed, and small detection error. This algorithm uses two windows of equal and adjacent length. and The two windows slide point by point while remaining relatively stationary during the sliding process. Specifically, the windows... and Slide point by point from left to right through the calculation window. and Accumulated energy within and When the window is filled with noise, the signal inside the window is stable and the energy value is low; when a signal enters the window, the energy inside the window gradually increases, until the condition is met. When the condition is met, it is determined as the start of a signal. When the window is entirely filled with signals, the energy is constant and changes little. As the signal disappears, the window... The energy value within the window will decrease, and the signal is considered to end when the corresponding condition is met again. In the dual sliding window detection algorithm, the value of m reflects the signal-to-noise ratio (SNR) of the signal within the window, so the threshold can be determined based on the SNR. In practical applications, the SNR threshold should be no less than 3dB, and the window length should be no less than 128.

[0035] Two adjacent sliding windows of equal length, with a minimum length of 128, are selected. The energy within each window, the signal-to-noise ratio (SNR), and the ratio of the energy between the two windows are determined by sliding the windows point by point. If the energy within a window is stable, the SNR is below 6 dB, and the ratio is between 0.7 and 1.5, the signal is considered to be in a noisy state based on the energy value. If the energy within a window gradually increases and the ratio is greater than 3, the signal is considered to be in a starting state based on the energy value and the ratio. If the energy within a window gradually decreases and the ratio is less than -3, the signal is considered to be in a ending state based on the energy value and the ratio.

[0036] S103. Perform time-frequency domain two-dimensional correlation detection on the narrowband signal using a pre-set sequence signal template to determine the frequency offset and signal type of the narrowband signal; statistically analyze the detection results of each channel based on the frequency offset and the signal type, and output a broadband reconnaissance result list based on the detection results.

[0037] Template matching correlation detection algorithms primarily utilize a local specific sequence signal template and the DDC output signal to perform two-dimensional correlation operations in the time-frequency domain, thereby achieving the detection of a specific signal. The core theory of this algorithm is based on the mutual ambiguity function of the two signals. This is to simultaneously estimate the two signals... and The DTO and DFO are typically calculated using the Cross Ambiguity Function (CAF) of these two signals, and the formula is as follows:

[0038]

[0039] in, The mutual ambiguity coefficients of the signals. , It is a sequence signal. For frequency offset, Let T be the time difference and T be the period.

[0040] As can be seen from the definition of the fuzzy function, it possesses the ability to completely suppress the influence of independent noise. In fact, the fuzzy function is a time-frequency representation based on the generalized cross-correlation of signals, and each dimension of its operation can be considered a correlation operation. Therefore, the correlation of the signal directly affects the calculation result of the mutual fuzzy function. A schematic diagram of the mutual fuzzy function is shown below. Figure 3 As shown in the figure, the relationship between the number of sliding samples, DFO, and correlation amplitude is illustrated. The X-axis represents the number of sliding samples, ranging from 0 to 300; the Y-axis represents the DFO, ranging from -5000Hz to 5000Hz; and the Z-axis represents the correlation amplitude, ranging from 0 to 0.12. The figure shows that the correlation amplitude is low and relatively evenly distributed in most areas, while a significant high-amplitude peak appears near the DFO, indicating that the signal correlation is significantly enhanced at this specific frequency offset. This type of chart is often used to analyze the characteristics of signals in the time-frequency domain, helping to identify specific frequency components in the signal and their variations.

[0041] like Figure 4 As shown, taking data link 22 as an example, this demonstrates processing burst waveforms containing 8 Link22 connections. Figure 5 As shown, after the preamble sequence template of the local Link22 is correlated with the signal, the maximum value of the spectrum slice corresponding to each DTO is calculated and divided by the sum of the slices. The result is used as the maximum correlation value of the DTO slice. It can be clearly seen from the figure that there are 8 obvious correlation peaks after the received signal is correlated with the local template.

[0042] In one embodiment, by comprehensively utilizing full-band channelization technology, dual sliding window detection method, and related peak information analysis for signal detection and identification, not only is the signal detection probability effectively improved and the signal type accurately identified, but the false alarm rate is also significantly reduced, making the detection results more accurate and reliable.

[0043] The pre-set test conditions were: data signal bandwidth 28.5MHz, sampling rate 40MHz, and data duration 30 seconds. There were a total of 2000 signals, and the specific signal types and distribution are shown in Table 1 below.

[0044] Table 1 Broadband Data Signal Distribution Information

[0045]

[0046] Based on the above 10 signal types, 10 local templates were generated. Combined with a signal bandwidth of 28.5MHz, and following a standard of 3kHz bandwidth and 1.5kHz overlap, a total of 19,000 channels were created. Each channel has 128 data points, and each channel undergoes 10 correlation operations. The entire processing flow first performs full-band frequency domain DDC processing on the broadband data; then, dual sliding window detection is performed on the channelized data; finally, kernel function thread allocation is performed when multiplying the DDC data with the local templates. Figure 6 As shown, this kernel function uses a two-dimensional grid and one-dimensional thread blocks. The grid size is M*N, and each row of thread blocks is responsible for calculating the multiplication of one channel's data with all local templates; the thread block size is 1*L, and each thread block is responsible for calculating the multiplication of one channel's data with a set of local templates.

[0047] In one embodiment, such as Figure 7 As shown, the correlation coefficient is calculated. When allocating threads for the kernel function, the kernel function uses a one-dimensional grid and one-dimensional thread blocks. The grid size is 1*MN, and every M consecutive thread blocks are responsible for calculating all the data for one channel. , i=1,2,…,M; the size of the thread block is 1*L, and each thread block is responsible for calculating a certain value of a channel. .like Figure 8 As shown, the final correlation coefficient is calculated. The kernel function is allocated threads at specific times. The kernel function uses a one-dimensional grid and a one-dimensional thread block. The grid size is 1*N, responsible for calculating all channels. The size of a thread block is 1*M, and each thread block is responsible for calculating a certain value of a channel. .

[0048] The results of the above tests, compared with those of traditional energy-based signal detection, are as follows: Figure 9 As shown, at high signal-to-noise ratios (SNR), the accuracy of the two detection methods is basically the same, and both increase with the increase of SNR. When the SNR is below 8dB, the existing detection accuracy decreases significantly, while the detection accuracy in this embodiment is not affected, and the current detection accuracy can still reach 90% even at 6dB.

[0049] like Figure 10 As shown in the illustration, this application also provides a signal detection and recognition device based on template matching, comprising:

[0050] At least one processor; and,

[0051] A memory that is communicatively connected to at least one processor; wherein,

[0052] The memory stores instructions that can be executed by at least one processor to enable a template-matching-based signal detection and recognition device to perform the method as described in any of the embodiments above.

[0053] This application also provides a non-volatile computer storage medium storing computer-executable instructions, which are configured as described in any of the above embodiments.

[0054] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0055] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0056] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0057] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0058] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0059] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0060] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0065] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0066] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0067] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0068] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A template matching based signal detection identification method, characterized in that, The method comprises the following steps: determining a signal sequence template according to a preset reconnaissance target and a signal frame format, and performing parallel processing on the wideband data through the signal sequence template to obtain multiple narrowband signals; performing time domain detection on the narrowband signals through a preset double sliding window to determine the starting position and ending position of the narrowband signals and determine the signal-to-noise ratio corresponding to the narrowband signals; performing two-dimensional correlation detection on the narrowband signals in the time and frequency domains through a preset sequence signal template to determine the frequency offset and signal type of the narrowband signals, and statistically analyzing the detection results according to the frequency offset and the signal type to output a wideband reconnaissance result list according to the detection results; performing parallel processing on the wideband data, specifically including: performing down-conversion processing on the wideband data, and performing overlap-save processing on the down-converted wideband data to filter the wideband data; determining the segmentation length and overlap length of the wideband data, determining the decimation factor according to the segmentation length and the overlap length, and determining multiple narrowband signals according to the decimation factor; determining multiple narrowband signals according to the decimation factor, specifically including: segmenting the wideband data according to the decimation factor to determine multiple segments of signals, performing first scale transformation on the multiple segments of signals to convert the multiple segments of signals to the frequency domain; performing balancing processing on the multiple segments of signals after the first scale transformation, and performing frequency component extraction on the balanced multiple segments of signals to filter the multiple segments of signals; performing second scale transformation on the filtered multiple segments of signals to convert the multiple segments of signals back to the time domain, and correcting the multiple segments of signals according to a preset output phase to determine multiple narrowband signals.

2. The method of claim 1, wherein, performing time domain detection on the narrowband signals through a preset double sliding window, specifically including: determining two adjacent sliding windows with equal length, and the window length is not less than 128, and determining the energy value, signal-to-noise ratio and energy ratio between the two windows in the sliding windows through point-by-point sliding; if the energy in the window is stable, the signal-to-noise ratio is lower than 6dB, and the ratio is between 0.7-1.5, then according to the energy value, it is judged that the signal is in noise state.

3. The method of claim 2, wherein, The method further comprises: if the energy in the window gradually increases and the ratio is greater than 3dB, then according to the energy value and the ratio, it is judged that the signal is in the starting state; if the energy in the window gradually decreases and the ratio is less than -3dB, then according to the energy value and the ratio, it is judged that the signal is in the ending state.

4. The method of claim 1, wherein, performing two-dimensional correlation detection on the narrowband signals in the time and frequency domains through a preset sequence signal template, specifically including: determining a cross ambiguity function according to the sequence signal template, determining the time difference of arrival and the frequency offset of the narrowband signals according to the cross ambiguity function, and the calculation formula of the cross ambiguity function is: wherein is the cross ambiguity function of the signals, , is the sequence signal, is the frequency offset, is the time difference, T is the period.

5. The method of claim 4, wherein, The method further comprises: determining the frequency spectrum slice corresponding to the time difference of arrival, determining the maximum value according to the frequency spectrum slice, determining the sum of the time difference of arrival, and determining the maximum correlation value according to the maximum value and the sum.

6. The method of claim 5, wherein, determining the signal type of the wideband data, specifically including: According to the maximum correlation value, a corresponding peak value is determined, and according to the peak value, an effective index is determined, so as to determine the signal type according to the effective index.

7. A signal detection and recognition device based on template matching, characterized in that Comprise: At least one processor; And, The memory is in communication connection with 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 to enable the signal detection and identification device based on template matching to perform the method as claimed in any one of claims 1-6.

8. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer executable instructions are configured to perform the method as claimed in any one of claims 1-6.

Images