Fast wideband signal detection method and device based on time-frequency diagram, equipment and medium

Abstract

The application relates to a fast wideband signal detection method, device and equipment based on a time-frequency graph and a medium. The method determines the starting and ending frequencies of a wideband signal by performing Gaussian blurring, time domain averaging, gradient estimation and gradient matching on a time-frequency graph of an electromagnetic signal; verifies and scores the starting and ending frequency positions of each row on the time-frequency graph, and if the score passes, the row contains a wideband signal, otherwise, the row does not contain a wideband signal; determines whether the wideband signal is a continuous signal, and if it is not, calculates the starting and ending times of a wideband signal pulse in the time-frequency graph according to the number of rows containing the wideband signal; and obtains the characteristics of the wideband signal in the time-frequency graph according to the starting and ending frequencies of the wideband signal and the starting and ending times of the wideband signal pulse. The method proposes different feature extraction processes and detection decision mechanisms, guarantees the timeliness of detection, and provides theoretical support for fast wideband signal detection in complex environments.

Description

Technical Field

[0001] The present application relates to the technical field of broadband signal detection, and in particular to a fast broadband signal detection method, apparatus, device and medium based on a time-frequency diagram. Background Art

[0002] With the rapid development of modern communications and electronic warfare systems, efficient detection of broadband signals in complex electromagnetic environments has become a key technical challenge. Traditional detection methods (such as energy detection and matched filtering) significantly degrade in performance when the signal-to-noise ratio is low and interference is strong, and they struggle to cope with the non-stationary nature of broadband signals. Time-frequency analysis, by combining time and frequency domain information, effectively captures the transient characteristics of signals and has become an important tool for addressing these issues. However, existing time-frequency methods still face bottlenecks such as high computational complexity and insufficient real-time performance. This is particularly true when processing high-dimensional time-frequency plots, where feature extraction and classification efficiency struggle to meet practical requirements.

[0003] In recent years, with advancements in technology, most detection algorithms have significantly improved their performance. However, these algorithms are also subject to significant resource investment and long preparation times, limiting their application in real-time detection scenarios. Furthermore, noise, multipath effects, and interference signals in complex electromagnetic environments can disrupt the structural characteristics of time-frequency graphs, increasing the false detection rate of traditional algorithms. Summary of the Invention

[0004] Based on this, it is necessary to provide a fast broadband signal detection method, device, equipment and medium based on time-frequency diagram to address the above technical problems.

[0005] A fast broadband signal detection method based on a time-frequency diagram, the method comprising:

[0006] Obtain the time-frequency diagram of the electromagnetic signal and perform Gaussian blur on the time-frequency diagram.

[0007] The time-frequency graph after Gaussian blurring is averaged along the time domain to obtain the time-domain average data.

[0008] The gradient is estimated based on the time domain average data to obtain the gradient estimation result.

[0009] Gradient matching is performed based on the gradient estimation result and the preset matching principle to determine the starting and cutoff frequencies of the broadband signal.

[0010] The starting and ending frequency positions of each row in the time-frequency diagram are verified and scored. If the score passes, there is a broadband signal in the row; otherwise, there is no broadband signal in the row.

[0011] Determine whether the broadband signal is a continuous signal. If it is a discontinuous signal, calculate the start time and end time of the broadband signal pulse in the time-frequency diagram based on the number of rows where the broadband signal exists or not.

[0012] According to the starting and cutting-off frequencies of the broadband signal, the starting time and cutting-off time of the broadband signal pulse, the broadband signal characteristics existing in the time-frequency diagram are obtained.

[0013] In one embodiment, performing gradient estimation based on the time-domain average data to obtain a gradient estimation result includes:

[0014] The time domain average data is subjected to sliding average and difference to obtain the gradient sequence.

[0015] The gradient sequence is binarized according to a preset gradient threshold to obtain a binarized sequence; the binarized sequence includes: a rising edge sequence and a falling edge sequence.

[0016] The frequencies of the rising edge and the falling edge of each broadband signal in the time-frequency diagram are determined according to the rising edge sequence and the falling edge sequence.

[0017] The midpoint value of each continuous bulge in the binarized sequence is used as the frequency point estimation value of the broadband signal, and the corresponding gradient value in the gradient sequence is recorded to obtain the gradient estimation result.

[0018] In one embodiment, the time domain average data is subjected to sliding average and difference, and the gradient sequence is obtained as follows:

[0019] ;

[0020] in, is the gradient sequence, is the average number, i is an integer, , , is the length of the time domain average data, is the time domain average data.

[0021] In one embodiment, the gradient sequence is binarized according to a preset gradient threshold to obtain a binarized sequence; the binarized sequence includes a rising edge sequence and a falling edge sequence, including:

[0022] A preset gradient threshold is set, where the preset gradient threshold is greater than 0.

[0023] The positive numbers in the gradient sequence are compared with a preset gradient threshold, and the elements greater than the preset gradient threshold form a rising edge sequence.

[0024] The negative numbers in the gradient sequence are compared with the inverse number of the preset gradient threshold, and the elements whose numbers are smaller than the inverse number of the preset gradient threshold form a falling edge sequence.

[0025] In one embodiment, the preset matching principles include: Preset matching principle 1: each rising edge needs to be gradient compared with the falling edge to the right of its position; the gradient comparison method is: the absolute value of the corresponding gradient in the rising edge and the falling edge, the maximum value is less than the minimum value by less than 2, limiting the difference between the two gradient values ​​to be too large.

[0026] Preset matching principle 2: Each pair of rising and falling edges must be verified and scored. If a rising edge corresponds to multiple falling edges, the pair with the highest score is selected. If a falling edge corresponds to multiple rising edges, the pair with the highest score is selected. If all rising and falling edges are not matched, the match is considered a failure and no subsequent operations are performed.

[0027] In one embodiment, the specific steps of verifying and scoring include: for a pair of rising edge and falling edge, marking a first position and a second position at a preset distance to the left of the rising edge position and to the right of the falling edge position.

[0028] Based on the time domain average data, the first position, and the second position, the signal-to-noise ratio and square wave confidence of the signal are calculated; the signal-to-noise ratio and square wave confidence are respectively:

[0029] ;

[0030] ;

[0031] in, is the signal-to-noise ratio, Prob is the square wave confidence, , for arrive The average value of for arrive The average value of , , , is the time domain average data, and are the rising edge and falling edge positions respectively, and The first and second positions respectively.

[0032] The scores of the signal-to-noise ratio and the square wave confidence are calculated according to the signal-to-noise ratio, the square wave confidence, and the maximum and minimum values ​​of the preset thresholds.

[0033] The final score is obtained by taking a weighted average of the scores of signal-to-noise ratio and square wave confidence.

[0034] In one embodiment, the signal-to-noise ratio and square wave confidence score are:

[0035] ;

[0036] ;

[0037] in, and are the signal-to-noise ratio and square wave confidence scores, respectively. and They are the maximum and minimum values ​​of the preset threshold respectively.

[0038] A fast broadband signal detection device based on a time-frequency diagram, the device comprising:

[0039] The Gaussian blur processing module is used to obtain the time-frequency diagram of the electromagnetic signal and perform Gaussian blur on the time-frequency diagram.

[0040] The time domain averaging module is used to take the average of the time-frequency graph after Gaussian blur processing along the time domain direction to obtain the time domain average data.

[0041] The gradient estimation module is used to perform gradient estimation based on the time domain average data to obtain a gradient estimation result.

[0042] The gradient matching module is used to perform gradient matching based on the gradient estimation result and the preset matching principle to determine the starting and ending frequencies of the broadband signal.

[0043] The broadband signal detection module is used to verify and score the starting and ending frequency positions of each row on the time-frequency diagram. If the score passes, there is a broadband signal in the row, otherwise, there is no broadband signal in the row; it determines whether the broadband signal is a continuous signal. If it is a discontinuous signal, the starting time and ending time of the broadband signal pulse in the time-frequency diagram are calculated according to the number of rows where the broadband signal exists or not; based on the starting and ending frequencies of the broadband signal and the starting time and ending time of the broadband signal pulse, the broadband signal characteristics existing in the time-frequency diagram are obtained.

[0044] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of any of the above-mentioned fast broadband signal detection methods based on time-frequency diagrams.

[0045] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned fast broadband signal detection methods based on time-frequency diagrams.

[0046] The above-mentioned fast broadband signal detection method, device, equipment, and medium based on time-frequency graphs determine the starting and ending frequencies of broadband signals by performing Gaussian blurring, time-domain averaging, gradient estimation, and gradient matching on the time-frequency graph of electromagnetic signals. The starting and ending frequency positions of each row in the time-frequency graph are verified and scored. If the score passes, a broadband signal exists in that row; otherwise, no broadband signal exists in that row. A determination is made as to whether the broadband signal is continuous. If it is discontinuous, the starting and ending times of the broadband signal pulses in the time-frequency graph are calculated based on the number of rows where the broadband signal exists or does not exist. Based on the starting and ending frequencies of the broadband signal and the starting and ending times of the broadband signal pulses, the broadband signal characteristics present in the time-frequency graph are obtained. This method proposes different feature extraction processes and detection decision mechanisms to ensure the timeliness of detection and provide theoretical support for the rapid detection of broadband signals in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 1 is a flow chart of a fast broadband signal detection method based on a time-frequency diagram in one embodiment;

[0048] Figure 2 is a time-frequency diagram of an electromagnetic signal in another embodiment;

[0049] Figure 3 is a time-frequency diagram processed by Gaussian blur in another embodiment;

[0050] Figure 4 A schematic diagram of time domain average data in another embodiment;

[0051] Figure 5 A schematic diagram of a gradient sequence in another embodiment;

[0052] Figure 6 A schematic diagram of gradient sequence binarization in another embodiment;

[0053] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0055] This application proposes a fast broadband signal detection method based on time-frequency graphs. This method performs a series of operations on the time-frequency graph, including Gaussian blurring, time-domain averaging, gradient estimation, gradient matching, and feature extraction. This method can quickly obtain accurate frequency band information and pulse start and end time information for broadband signals present in the graph (hereinafter, a two-dimensional time-frequency graph with frequency units in MHz and time units in ms is used as an example). By integrating physical features in the time and frequency domains, this method achieves low-complexity, high-real-time signal detection.

[0056] In one embodiment, Figure 1 As shown, a fast broadband signal detection method based on a time-frequency diagram is provided, the method comprising the following steps:

[0057] Step 100: Obtain a time-frequency diagram of the electromagnetic signal and perform Gaussian blurring on the time-frequency diagram.

[0058] Specifically, a two-dimensional Gaussian blur is performed on the time-frequency diagram of the electromagnetic signal. The purpose of this operation is to smooth out the discontinuity of some broadband signals in the time domain (such as Figure 2 As shown in ), the estimation error is reduced for subsequent gradient estimation. The time-frequency diagram after Gaussian blurring is shown in Figure 3 As shown. Gaussian blur is a basic technology for image processing, and its principle formula is:

[0059] ;

[0060] in, is the core weight coefficient of Gaussian blur, which determines the contribution of each pixel. is the pixel coordinate offset relative to the blur center, The standard deviation is used to control the blur strength and blur range.

[0061] Step 102: averaging the time-frequency graph after Gaussian blurring along the time domain direction to obtain time-domain average data.

[0062] Specifically, after obtaining the time-frequency graph processed by Gaussian blur, it is averaged along the time domain direction to obtain the data after time domain averaging, that is, the time domain average data. The time domain average data is a one-dimensional sequence ,like Figure 4 The purpose of this operation is to reduce the impact of short and strong broadband pulses near the broadband signal on the frequency band estimation, and also to provide data preprocessing for subsequent gradient estimation.

[0063] Step 104: Perform gradient estimation based on the time-domain average data to obtain a gradient estimation result.

[0064] Specifically, a sliding average is performed on the time domain average data to obtain a gradient sequence, each element of the gradient sequence is binarized to obtain a binarized sequence, and gradient estimation is performed based on the binarized sequence.

[0065] Step 106: Perform gradient matching according to the gradient estimation result and a preset matching principle to determine the start and end frequencies of the broadband signal.

[0066] Step 108: Verify and score the start and end frequency positions of each row on the time-frequency diagram. If the score passes, there is a broadband signal in the row; otherwise, there is no broadband signal in the row.

[0067] Step 110: Determine whether the broadband signal is a continuous signal. If it is a discontinuous signal, calculate the start time and end time of the broadband signal pulse in the time-frequency diagram according to the number of rows where the broadband signal exists or not.

[0068] Step 112: Obtain the broadband signal features in the time-frequency diagram according to the starting and ending frequencies of the broadband signal and the starting time and ending time of the broadband signal pulse.

[0069] In the above-mentioned fast broadband signal detection method based on time-frequency graphs, the method determines the starting and ending frequencies of the broadband signal by performing Gaussian blurring, time-domain averaging, gradient estimation, and gradient matching on the time-frequency graph of the electromagnetic signal. The starting and ending frequency positions of each row in the time-frequency graph are verified and scored. If the score passes, a broadband signal exists in that row; otherwise, no broadband signal exists in that row. A determination is made as to whether the broadband signal is continuous. If it is discontinuous, the starting and ending times of the broadband signal pulses in the time-frequency graph are calculated based on the number of rows where the broadband signal exists or does not exist. Based on the starting and ending frequencies of the broadband signal and the starting and ending times of the broadband signal pulses, the broadband signal features present in the time-frequency graph are obtained. This method proposes different feature extraction processes and detection decision mechanisms to ensure the timeliness of detection and provide theoretical support for the rapid detection of broadband signals in complex environments.

[0070] In one embodiment, step 104 includes: performing a sliding average and a difference on the time-domain average data to obtain a gradient sequence; performing binarization processing on the gradient sequence according to a preset gradient threshold to obtain a binarized sequence; the binarized sequence includes a rising edge sequence and a falling edge sequence; determining the frequencies of the rising edge and the falling edge of each broadband signal in the time-frequency graph based on the rising edge sequence and the falling edge sequence; using the midpoint value of each continuous bulge in the binarized sequence as a frequency point estimate value of the broadband signal, and recording the corresponding gradient value in the gradient sequence to obtain a gradient estimation result.

[0071] In one embodiment, a sliding average is performed on the time domain average data and a difference is made to obtain a gradient sequence; the gradient sequence expression is:

[0072] ;

[0073] in, is the gradient sequence, is the average number, i is an integer, , , is the length of the time domain average data, is the time domain average data.

[0074] In one embodiment, a gradient sequence is binarized according to a preset gradient threshold to obtain a binarized sequence; the binarized sequence includes a rising edge sequence and a falling edge sequence, including: setting a preset gradient threshold, wherein the preset gradient threshold is greater than 0; comparing positive numbers in the gradient sequence with the preset gradient threshold, and forming a rising edge sequence from elements greater than the preset gradient threshold; and comparing negative numbers in the gradient sequence with the opposite of the preset gradient threshold, and forming a falling edge sequence from elements less than the opposite of the preset gradient threshold.

[0075] In one embodiment, the preset matching principles include: preset matching principle 1: each rising edge needs to be gradient compared with the falling edge to the right of its position; the gradient comparison method is: the absolute value of the corresponding gradient in the rising edge and the falling edge, the maximum value is less than the minimum value by 2, and the difference between the two gradient values ​​is limited to be too large; preset matching principle 2: each pair of rising and falling edges needs to be verified and scored; if one rising edge corresponds to multiple falling edges, the pair with the highest score is taken; if one falling edge corresponds to multiple rising edges, the pair with the highest score is taken; if all rising edges and falling edges are not matched, it is considered that the match failed, and no subsequent operations are performed.

[0076] In one embodiment, the specific steps of verifying and scoring include: for a pair of rising edges and falling edges, marking a first position and a second position at a preset distance to the left of the rising edge position and to the right of the falling edge position; calculating the signal-to-noise ratio and square wave confidence of the signal based on the time domain average data, the first position, and the second position; the expressions of the signal-to-noise ratio and the square wave confidence are respectively:

[0077] ;

[0078] ;

[0079] in, is the signal-to-noise ratio, Prob is the square wave confidence, , for arrive The average value of for arrive The average value of , , , is the time domain average data, and are the rising edge and falling edge positions respectively, and The first and second positions respectively.

[0080] The scores of the signal-to-noise ratio and the square wave confidence are calculated according to the maximum and minimum values ​​of the signal-to-noise ratio, the square wave confidence, and the preset thresholds; and the scores of the signal-to-noise ratio and the square wave confidence are weighted averaged to obtain a final score.

[0081] In one embodiment, the fractional expressions of the signal-to-noise ratio and the square wave confidence are:

[0082] ;

[0083] ;

[0084] in, and are the signal-to-noise ratio and square wave confidence scores, respectively. and They are the maximum and minimum values ​​of the preset thresholds, respectively. Taking confidence as an example, maxVal=0.8, minVal=0.4, that is, when the calculated Prob is less than 0.4, it is 0 points, and when it is greater than 0.8, it is always 1 point. If it is between 0.4 and 0.8, the score is between 0 and 1.

[0085] Specifically, the time domain average data is a one-dimensional sequence (like Figure 4 As shown), the gradient sequence is obtained by performing sliding average on this set of one-dimensional data and making a difference. (like Figure 5 In order to control the accuracy of detection and the false alarm rate of detection, the gradient threshold T is set ( ) to binarize the gradient value. The smaller T is, the more advantageous it is in detecting low signal-to-noise ratio signals. Since the gradient value is obtained by difference, there are negative numbers. Therefore, it is necessary to binarize Threshold judgment is performed on both positive and negative directions (i.e. and T and -T respectively) to obtain a binary sequence and .Depend on The obtained sequence It is called the rising edge sequence, and vice versa. The resulting falling edge sequence (in , the reverse operation is for better understanding). The gradient sequence diagram is shown in Figure 5.

[0086] The falling edge sequence B2 obtained by comparison is composed entirely of 0s and 1s. B2 is inverted to better express that the falling edge is in the downward direction. Figure 6 As stated, Figure 6 The upward convex part of the middle red line indicates the rising edge, and the downward convex part indicates the falling edge.

[0087] Through the rising edge sequence and falling edge sequence Basically, the frequency of the rising and falling edges of each broadband signal in the time-frequency diagram can be obtained. The midpoint value of each continuous convexity in the binary sequence is the final estimated frequency point value. When obtaining the corresponding frequency point position, it is also necessary to record the gradient sequence The corresponding gradient value in prepares for the next step of gradient matching.

[0088] After gradient estimation is completed, gradient matching is performed, that is, matching the rising edge and the falling edge. The matching principles are as follows:

[0089] Principle 1: Each rising edge needs to be compared with the falling edge to the right of it;

[0090] Principle 2: Each pair of rising and falling edges must be verified and scored. If a rising edge corresponds to multiple falling edges, the pair with the highest score is selected. If a falling edge corresponds to multiple rising edges, the pair with the highest score is selected. If all rising and falling edges do not match, it will be considered a match failure and no subsequent operations will be performed.

[0091] Gradient comparison method in Principle 1: The absolute value of the corresponding gradient on the rising edge and the falling edge should be less than 2 compared to the minimum value, limiting the difference between the two gradient values ​​from being too large.

[0092] Verification scoring method in principle 2: After obtaining the rising edge and falling edge positions, record them as and , mark the location and ,in , In the sequence Based on the expression of signal-to-noise ratio and square wave confidence, the signal is analyzed for signal-to-noise ratio. and square wave confidence Calculate the signal-to-noise ratio and square wave confidence scores based on their respective expressions. Finally, take the weighted average of the signal-to-noise ratio and square wave confidence scores to get the final score. If the final score is greater than 0.5, the test passes.

[0093] After gradient matching, the positions of the rising and falling edges on the pair can be determined as the starting and cutoff frequencies of the broadband signal. Finally, based on the fractional expression of the signal-to-noise ratio and the square wave confidence, the initial time-frequency diagram is used to calculate the peaks of each row. and The position is verified and scored. If the score passes, there is a signal in that row, otherwise there is no signal. This can also determine whether the broadband signal is continuous in the time-frequency diagram. If it is a discontinuous signal, the start and end time of the broadband signal pulse in the time-frequency diagram can be calculated based on the number of rows with or without signals (relative to the start time of the time-frequency diagram).

[0094] Finally, by integrating the information obtained from the previous calculations, we can obtain the broadband signal characteristics (frequency, bandwidth, pulse time, etc.) in the time-frequency diagram.

[0095] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0096] In one embodiment, a fast broadband signal detection device based on a time-frequency diagram is provided, comprising: a Gaussian blur processing module, a time-domain averaging module, a gradient estimation module, a gradient matching module, and a broadband signal detection module, wherein:

[0097] The Gaussian blur processing module is used to obtain the time-frequency diagram of the electromagnetic signal and perform Gaussian blur on the time-frequency diagram.

[0098] The time domain averaging module is used to take the average of the time-frequency graph after Gaussian blur processing along the time domain direction to obtain the time domain average data.

[0099] A gradient estimation module is used to perform gradient estimation based on the time domain average data to obtain a gradient estimation result;

[0100] The gradient matching module is used to perform gradient matching based on the gradient estimation result and the preset matching principle to determine the starting and ending frequencies of the broadband signal.

[0101] The broadband signal detection module is used to verify and score the starting and ending frequency positions of each row on the time-frequency diagram. If the score passes, there is a broadband signal in the row, otherwise, there is no broadband signal in the row; it determines whether the broadband signal is a continuous signal. If it is a discontinuous signal, the starting time and ending time of the broadband signal pulse in the time-frequency diagram are calculated according to the number of rows where the broadband signal exists or not; based on the starting and ending frequencies of the broadband signal and the starting time and ending time of the broadband signal pulse, the broadband signal characteristics existing in the time-frequency diagram are obtained.

[0102] In one embodiment, the gradient estimation module is further configured to perform sliding averaging and subtraction on the time-domain average data to obtain a gradient sequence; binarize the gradient sequence according to a preset gradient threshold to obtain a binarized sequence; the binarized sequence includes a rising edge sequence and a falling edge sequence; the frequencies of the rising edge and falling edge of each broadband signal in the time-frequency diagram are determined based on the rising edge sequence and the falling edge sequence; the midpoint value of each continuous bulge in the binarized sequence is used as a frequency point estimate value of the broadband signal, and the corresponding gradient value in the gradient sequence is recorded to obtain a gradient estimation result.

[0103] In one embodiment, the gradient estimation module is further configured to perform sliding average and difference on the time domain average data to obtain a gradient sequence as described in the above gradient sequence expression.

[0104] In one embodiment, the gradient estimation module is further configured to set a preset gradient threshold, where the preset gradient threshold is greater than 0; compare positive numbers in the gradient sequence with the preset gradient threshold, and form a rising edge sequence with elements greater than the preset gradient threshold; and compare negative numbers in the gradient sequence with the opposite of the preset gradient threshold, and form a falling edge sequence with elements less than the opposite of the preset gradient threshold.

[0105] In one embodiment, the preset matching principles in the gradient matching module include: Preset matching principle 1: each rising edge needs to be gradient compared with the falling edge to the right of its position; the gradient comparison method is: the absolute value of the corresponding gradient of the rising edge and the falling edge, the maximum value is less than the minimum value by less than 2, and the difference between the two gradient values ​​is limited to be too large; Preset matching principle 2: each pair of rising and falling edges needs to be verified and scored; if one rising edge corresponds to multiple falling edges, the pair with the highest score is taken; if one falling edge corresponds to multiple rising edges, the pair with the highest score is taken; if all rising edges and falling edges are not matched, it is considered that the match failed, and no subsequent operations are performed.

[0106] In one embodiment, the specific steps of verifying and scoring in the broadband signal detection module include: for a pair of rising edges and falling edges, marking a first position and a second position at a preset distance to the left of the rising edge position and to the right of the falling edge position; calculating the signal-to-noise ratio and square wave confidence of the signal using the above-mentioned signal-to-noise ratio and square wave confidence expressions based on the time domain average data, the first position and the second position; calculating the scores of the signal-to-noise ratio and square wave confidence based on the maximum and minimum values ​​of the signal-to-noise ratio, square wave confidence and the preset thresholds; and performing weighted averaging on the scores of the signal-to-noise ratio and square wave confidence to obtain the final score.

[0107] In one embodiment, the broadband signal detection module uses the above-mentioned fractional expressions of the signal-to-noise ratio and the square wave confidence to calculate the scores of the signal-to-noise ratio and the square wave confidence.

[0108] For the specific limitations of the fast broadband signal detection device based on the time-frequency graph, please refer to the limitations of the fast broadband signal detection method based on the time-frequency graph above, which will not be repeated here. The various modules in the above-mentioned fast broadband signal detection device based on the time-frequency graph can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0109] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a fast broadband signal detection method based on a time-frequency diagram is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0110] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0111] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiment when executing the computer program.

[0112] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0113] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A fast broadband signal detection method based on time-frequency diagram, characterized in that: The method comprises: Obtaining a time-frequency diagram of the electromagnetic signal, and performing Gaussian blurring on the time-frequency diagram; The time-frequency graph after Gaussian blurring is averaged along the time domain direction to obtain the time-domain average data; Performing gradient estimation based on the time-domain average data to obtain a gradient estimation result; Performing gradient matching based on the gradient estimation result and a preset matching principle to determine the starting and ending frequencies of the broadband signal; Verify and score the start and end frequency positions of each row on the time-frequency diagram. If the score passes, there is a broadband signal in the row; otherwise, there is no broadband signal in the row. Determine whether the broadband signal is a continuous signal; if it is a discontinuous signal, calculate the start time and end time of the broadband signal pulse in the time-frequency graph according to the number of rows where the broadband signal exists; According to the starting and cutting-off frequencies of the broadband signal, the starting time and cutting-off time of the broadband signal pulse, the broadband signal characteristics existing in the time-frequency diagram are obtained.

2. The fast broadband signal detection method based on time-frequency diagram according to claim 1, characterized in that: Performing gradient estimation based on the time-domain average data to obtain a gradient estimation result includes: Performing sliding average and difference on the time domain average data to obtain a gradient sequence; Binarization is performed on the gradient sequence according to a preset gradient threshold to obtain a binary sequence; the binary sequence includes: a rising edge sequence and a falling edge sequence; Determining the frequencies of the rising edge and the falling edge of each broadband signal in the time-frequency diagram according to the rising edge sequence and the falling edge sequence; The midpoint position of each continuous bulge in the binary sequence is used as the rising edge or falling edge position of the broadband signal, and the gradient value corresponding to the rising edge or falling edge position in the gradient sequence is recorded to obtain a gradient estimation result.

3. The fast broadband signal detection method based on time-frequency diagram according to claim 1, characterized in that: The time domain average data is subjected to sliding average and difference, and the gradient sequence is obtained as follows: in, is the gradient sequence, is the time domain average data, is the average number, i is an integer, , , is the length of the time domain average data.

4. The fast broadband signal detection method based on time-frequency diagram according to claim 2, characterized in that: Binarizing the gradient sequence according to a preset gradient threshold to obtain a binary sequence; The binary sequence includes: a rising edge sequence and a falling edge sequence, including: Setting a preset gradient threshold, wherein the preset gradient threshold is greater than 0; Comparing the positive numbers in the gradient sequence with the preset gradient threshold, and forming a rising edge sequence from elements greater than the preset gradient threshold; The negative numbers in the gradient sequence are compared with the inverse of the preset gradient threshold, and the elements whose values ​​are smaller than the inverse of the preset gradient threshold form a falling edge sequence.

5. The fast broadband signal detection method based on time-frequency diagram according to claim 1, characterized in that: The preset matching principles include: Preset matching principle 1: Each rising edge needs to be compared with the falling edge to its right. The gradient comparison method is: the absolute value of the corresponding gradient of the rising edge and the falling edge, the ratio of the maximum to the minimum value is less than 2, and the difference between the two gradient values ​​is limited. Preset matching principle 2: Each pair of rising and falling edges must be verified and scored. If a rising edge corresponds to multiple falling edges, the pair with the highest score is selected. If a falling edge corresponds to multiple rising edges, the pair with the highest score is selected. If all rising and falling edges are not matched, the match is considered a failure and no subsequent operations are performed.

6. The fast broadband signal detection method based on time-frequency diagram according to claim 1 or 5, characterized in that: The specific steps for verifying the scoring include: For a pair of rising edge and falling edge, mark a first position and a second position at a preset distance to the left of the rising edge position and to the right of the falling edge position; Calculate the signal-to-noise ratio and square wave confidence of the signal based on the time-domain average data, the first position, and the second position; the signal-to-noise ratio and square wave confidence are respectively: in, is the signal-to-noise ratio, Prob is the square wave confidence, , for arrive The average value of for arrive The average value of , , , is the time domain average data, and are the rising edge and falling edge positions respectively, and The first and second positions respectively; Calculating scores of the signal-to-noise ratio and the square wave confidence according to the signal-to-noise ratio, the square wave confidence, and the maximum and minimum values ​​of the preset thresholds; The final score is obtained by taking a weighted average of the scores of signal-to-noise ratio and square wave confidence.

7. The fast broadband signal detection method based on time-frequency diagram according to claim 6, characterized in that: The signal-to-noise ratio and square wave confidence score are: in, and are the signal-to-noise ratio and square wave confidence scores, respectively. and They are the maximum and minimum values ​​of the preset threshold respectively.

8. A fast broadband signal detection device based on time-frequency diagram, characterized in that: The device comprises: A Gaussian blur processing module, used for obtaining a time-frequency diagram of the electromagnetic signal and performing Gaussian blur on the time-frequency diagram; The time domain averaging module is used to average the time-frequency graph after Gaussian blur processing along the time domain direction to obtain the time domain average data; A gradient estimation module, configured to perform gradient estimation based on the time-domain average data to obtain a gradient estimation result; A gradient matching module, configured to perform gradient matching based on the gradient estimation result and a preset matching principle to determine the starting and ending frequencies of the broadband signal; The broadband signal detection module is used to verify and score the starting and cutting-off frequency positions of each row on the time-frequency diagram. If the score is passed, there is a broadband signal in the row; otherwise, there is no broadband signal in the row; determine whether the broadband signal is a continuous signal; if it is a discontinuous signal, calculate the starting time and cutting-off time of the broadband signal pulse in the time-frequency diagram according to the number of rows where the broadband signal exists; obtain the broadband signal characteristics in the time-frequency diagram according to the starting and cutting-off frequencies of the broadband signal and the starting time and cutting-off time of the broadband signal pulse.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the fast broadband signal detection method based on time-frequency diagram according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fast broadband signal detection method based on time-frequency diagram according to any one of claims 1 to 7 are implemented.

Images