Time-frequency diagram-based image transmission signal detection method, terminal device, and storage medium

Abstract

The application discloses a kind of based on time-frequency chart's graph signal detection method, terminal equipment and storage medium, the method includes the following steps: obtaining signal time-frequency chart, Fourier transform is carried out after filtering and binarization to it in time direction, the maximum value position of each column is counted and clustered, and the class with bandwidth is too narrow and the column discontinuous in class is removed;The corresponding column of each class is summed in frequency direction to obtain pulse envelope, and then the start and end column of the pulse spectrum envelope corresponding to each class is obtained, which is compared with the start and end column of each class after clustering, to find the class with the smallest error;Iterate until the error of current calculation result and previous calculation result tends to be stable, then determine the frequency band of stable result, that is, whether the signal is the target signal can be judged.The application is applied to communication technology and electronic countermeasure field, not only less calculation, and high-precision detection can be carried out on graph signal under low signal-to-noise ratio condition, but also can filter out part of interference signal, false alarm rate is lower.

Description

Technical Field

[0001] This invention relates to the fields of communication technology and electronic countermeasures technology, specifically a method for detecting image transmission signals based on time-frequency diagrams, a terminal device, and a storage medium. Background Technology

[0002] In the current social context, drone technology has been increasingly widely used in both military and civilian applications; therefore, counter-drone systems have emerged, and the detection of drone signals is the foundation of counter-drone systems. Accurately detecting drone signals is an important and challenging task.

[0003] Drone signals include image transmission and numerical control signals. Image transmission signals are sent by the drone and received by the remote controller, primarily transmitting images captured by the drone's onboard camera. Because image transmission signals are sent by the drone, their detection is fundamental for locating the target drone. Since image transmission signals transmit relatively large amounts of data, they have wide bandwidth and pulse width, and are characterized by continuous transmission. They are typically modulated using OFDM (Orthogonal Frequency Division Multiplexing) technology.

[0004] Currently, the main methods for recognizing UAV image transmission signals are edge detection after binarization and image convolution. Edge detection methods are prone to failure at low signal-to-noise ratios and are very sensitive to interference signals, while image convolution algorithms have a very high computational load and are prone to misidentifying non-target signals, requiring other information for auxiliary recognition. Summary of the Invention

[0005] To address the shortcomings of the existing technology, this invention provides a method, terminal device, and storage medium for detecting image transmission signals based on time-frequency maps. This method is not only computationally simple but also enables effective detection of image transmission signals even under low signal-to-noise ratio conditions. Furthermore, it can filter out some interference signals.

[0006] To achieve the above objectives, the present invention provides a method for detecting image transmission signals based on time-frequency diagrams, comprising the following steps:

[0007] Step 1: The received signal is sampled by ADC, windowed, and subjected to short-time Fourier transform to obtain the signal time-frequency graph, and the signal time-frequency graph is binarized by adaptive threshold.

[0008] Step 2: After performing a Fourier transform on the time-frequency graph of the binarized signal in the time direction, count the position of the maximum value in each column and cluster them according to the position of the maximum value, and remove the classes with too narrow bandwidth and the discontinuous columns in the classes.

[0009] Step 3: Sum the corresponding column delay frequency directions of each category in the signal time-frequency graph to obtain the pulse envelope of each category, and then perform binarization on each pulse envelope;

[0010] Step 4: Calculate the start and end lines and pulse width of the corresponding pulses in each class, and accumulate the corresponding line delay time directions of the pulses in the signal time-frequency diagram of a single class to obtain the pulse spectrum envelope of each class.

[0011] Step 5: After binarizing the pulse spectrum envelope, calculate the start and end columns of the pulse spectrum envelope corresponding to each class, compare them with the start and end columns of each class after clustering in Step 2, find the class with the smallest error, and take the smallest error as the calculation result.

[0012] Step 6: Repeat steps 3-5 until the error between the current calculation result and the previous calculation result tends to stabilize. Then, make the corresponding judgment on the frequency band of the stable result to determine whether the signal is the target signal.

[0013] In one embodiment, step 1, the process of performing mean filtering and adaptive threshold binarization on the signal time-frequency graph, is as follows:

[0014] Step 1.1: Expand the signal time-frequency graph by mirroring the mean filter window size. For a signal time-frequency graph with a window size of m×n, expand the upper, lower, left, and right boundaries of the signal time-frequency graph by ceil((m+1) / 2) rows and ceil((n+1) / 2) columns in reverse row and column order. The values ​​on the diagonal are also filled by the values ​​on the diagonal in mirror order. Here, ceil means rounding down.

[0015] Step 1.2: Calculate the integral image based on the extended signal time-frequency diagram, and obtain the mean-filtered signal time-frequency diagram based on the integral image;

[0016] Step 1.3: Calculate the adaptive threshold based on the time-frequency graph of the mean-filtered signal, as follows:

[0017] Threshold(x,y)=Average(x,y)+D

[0018] In the formula, Threshold(x, y) is the adaptive threshold, Average(x, y) is the time-frequency image of the signal after mean filtering, and D is a settable parameter;

[0019] Step 1.4: Binarize the original signal time-frequency diagram based on the adaptive threshold.

[0020] In one embodiment, step 2, which involves calculating the position of the maximum value in each column and performing clustering based on the position of the maximum value, specifically includes:

[0021] Find the position of the maximum value in each column after the Fourier transform, and group the columns with the same maximum value position into one category, denoted as K. n The position of its corresponding column is denoted as col. n The starting column is denoted as Terminating column is denoted as

[0022] In one embodiment, step 2, the process of removing classes with excessively narrow bandwidth, is as follows:

[0023]

[0024] In the formula, Delete means deleting the corresponding class, c n For class K n The number of columns included, C is the number of columns in the signal time-frequency diagram, and P is a configurable parameter less than 1.

[0025] In one embodiment, step 2, the process of removing discontinuous columns from the class, is as follows:

[0026] Find the positions of the points where the corresponding column is not continuous, as follows:

[0027] T = col n (i+1)-col n (i)

[0028]

[0029] In the formula, T represents the first intermediate parameter, and col n (i+1)-col n (i) represents the (i+1)th column of the nth class minus the ith column, i.e., the subtraction with offset, and Point(i) represents the point in the column;

[0030] Find the positions of consecutive columns with small bandwidth:

[0031] T′=Point(i+1)-Point(i)

[0032] P start (n)=(T′=-1)

[0033] P stop (n)=(T′=1)

[0034] In the formula, T′ represents the second intermediate parameter, and P start (n), P stop (n) represents all the starting and ending positions of the consecutive column, respectively;

[0035] Let col′(n) = P start (n): P stop(n), col″(n) = (len(col′(n)) < (C × P)), and the corresponding column with too narrow bandwidth in this class is set to 1, that is, Point(col″) = 1, where col′(n) represents the set of columns corresponding to the bandwidth of the nth suspicious signal, col″(n) represents the set of columns with a number less than the specified number of columns, len(col′) represents the length of col′(n), C is the number of columns in the signal time-frequency diagram, P is a configurable parameter, and Point(col″(n)) represents the column with too narrow bandwidth;

[0036] Delete the column corresponding to Point 1 in the class.

[0037] In one embodiment, in step 3, the pulse envelope is:

[0038]

[0039] In the formula, E nt (r) represents class K n The pulse envelope, col n For class K n The corresponding column position, I(r, c), represents the r-th row and c-th column in the original signal time-frequency diagram.

[0040] In one embodiment, in step 4, the start and end points of the corresponding pulses in each class are calculated based on the gradient, where the gradient is a 1-bit start position and the gradient is a -1-bit end position.

[0041] In one embodiment, in step 5, the calculation result is:

[0042]

[0043]

[0044] In the formula, The corresponding class K of the calculation results n The minimum error of the starting column and the minimum error of the ending column, f start f stop These are the start and end columns of the pulse spectrum envelope, i.e., the start and end frequencies of the pulse spectrum envelope; Class K n The starting column and the ending column.

[0045] To achieve the above objectives, the present invention also provides a terminal device, comprising:

[0046] Memory, used to store programs;

[0047] A processor is configured to execute the program stored in the memory. When the program is executed, the processor is configured to perform some or all of the steps of the image transmission signal detection method based on the time-frequency diagram described above.

[0048] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing computer-executable instructions; when executed by a processor, the computer-executable instructions are used to implement some or all of the steps of the image transmission signal detection method based on time-frequency diagrams as described above.

[0049] Compared with the prior art, the present invention has the following beneficial technical effects:

[0050] 1. Compared with edge detection methods, the present invention can tolerate a lower signal-to-noise ratio and has higher detection accuracy;

[0051] 2. Compared with traditional image convolution methods, this invention requires less computation;

[0052] 3. Compared with edge detection methods and image convolution methods, the present invention can filter out some interference signals and has a lower false alarm rate. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0054] Figure 1 This is a flowchart of the image transmission signal detection method in an embodiment of the present invention;

[0055] Figure 2 This is a time-frequency diagram of the UAV image transmission signal in an embodiment of the present invention;

[0056] Figure 3 This is an example diagram of image extension in an embodiment of the present invention;

[0057] Figure 4 This is the binarization result of the signal time-frequency diagram in the embodiment of the present invention;

[0058] Figure 5 This is the amplitude distribution map of the binarized spectrum after Fourier transform in an embodiment of the present invention;

[0059] Figure 6 This is a schematic diagram of the spectral envelope after frequency delay accumulation in an embodiment of the present invention;

[0060] Figure 7This is a schematic diagram of the binarized spectrum envelope result after frequency delay accumulation in an embodiment of the present invention;

[0061] Figure 8 This is a schematic diagram of the signal envelope after time delay accumulation in an embodiment of the present invention;

[0062] Figure 9 This is a schematic diagram of the signal envelope binarization result after delay accumulation in an embodiment of the present invention;

[0063] Figure 10 This is a time-frequency diagram of the image transmission signal of the detection results in an embodiment of the present invention;

[0064] Figure 11 This is a structural block diagram of the terminal device in an embodiment of the present invention.

[0065] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0067] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0068] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0069] like Figure 1 The image transmission signal detection method based on time-frequency diagram disclosed in this embodiment is shown. Taking the image transmission signal of a UAV as an example, the image transmission signal detection method specifically includes the following steps 1-6.

[0070] Step 1: The received signal is sampled by ADC, windowed, and subjected to short-time Fourier transform to obtain the signal time-frequency graph. The signal time-frequency graph is then subjected to mean filtering and adaptive threshold binarization.

[0071] Windowing and short-time Fourier transform are achieved by the following formula:

[0072]

[0073] In the formula, S(f, k) is the signal time-frequency diagram, s(n) is the discrete-time data after AD sampling, W(nk) is the window function coefficient, j is the imaginary unit, and f is the sampling frequency. In this embodiment, the signal time-frequency diagram after short-time Fourier transform is as follows: Figure 2 As shown, Figure 2 In the graph, the horizontal axis represents the corresponding frequency in Hertz, the left vertical axis represents time in seconds, and the right vertical axis represents amplitude. The specific implementation method for binarizing the signal time-frequency graph using mean filtering and adaptive thresholding is as follows:

[0074] Step 1.1: Expand the signal time-frequency diagram by mirroring the mean filter window size. For a signal time-frequency diagram with a window size of m×n, expand the upper, lower, left, and right boundaries of the signal time-frequency diagram by ceil((m+1) / 2) rows and ceil((n+1) / 2) columns in reverse row and column order. Figure 3 Taking an image with a window size of i×j as an example, it needs to be expanded by 6 rows and 6 columns. The top boundary expands upwards by 3 rows (rows 1, 2, and 3), and the bottom boundary expands by 3 rows (row i, (i-1), and (i-2). Similarly, the column expansion is done: the left boundary expands to the left by 3 columns (columns 1, 2, and 3), and the right boundary expands by 3 columns (columns j, (j-1), and (j-2). The values ​​on the diagonal are also filled using the diagonal values ​​in mirror order, where ceil represents rounding down.

[0075] Step 1.2: Calculate the integral image based on the extended signal time-frequency diagram, as follows:

[0076]

[0077] In the formula, Integral(r, c) is the integral image, I(i, j) is the expanded time-frequency graph of the signal, and R and C are the number of rows and columns of the expanded time-frequency graph, respectively. The filtered time-frequency graph of the signal is calculated from the integral image as follows:

[0078]

[0079] In the formula, Average(x, y) is the time-frequency diagram of the signal after mean filtering;

[0080] Step 1.3: Calculate the adaptive threshold based on the time-frequency graph of the mean-filtered signal, as follows:

[0081] Threshold(x,y)=Average(x,y)+D

[0082] In the formula, Threshold(x, y) is the adaptive threshold, and D is a settable parameter;

[0083] Step 1.4: Binarize the signal time-frequency graph based on the adaptive threshold, as follows:

[0084]

[0085] In the formula, I(x, y) is the original time-frequency diagram of the signal, I bin (x, y) is the time-frequency diagram of the signal after binarization, i.e. Figure 4 As shown.

[0086] Step 2: After performing a Fourier transform on the time-delay direction of the binarized signal time-frequency graph, the positions of the maximum values ​​in each column are counted, and clusters are formed based on these positions. Clusters with excessively narrow bandwidth and discontinuous columns within a cluster are removed. Specifically, performing a Fourier transform on the time-delay direction of the binarized signal time-frequency graph involves performing a Fourier transform on each column in ascending order of row number. In this embodiment, the amplitude distribution after performing a Fourier transform on the time-delay direction of the binarized signal time-frequency graph is... Figure 5 As shown, from Figure 5 As can be seen, the columns corresponding to drone signals have obvious peaks, and the peak positions are concentrated.

[0087] In the specific implementation process, the method of calculating the position of the maximum value in each column and performing clustering based on the position of the maximum value is as follows: find the position of the maximum value in each column after Fourier transform, and group the columns with the same maximum value position into one class, denoted as K. n The position of its corresponding column is denoted as col. n The starting column is denoted as Terminating column is denoted as

[0088] In this embodiment, the process of removing classes with excessively narrow bandwidth is as follows:

[0089]

[0090] In the formula, Delete means deleting the corresponding class, c n For class K n The number of columns included, C is the number of columns in the signal time-frequency diagram, and P is a configurable parameter. In this embodiment, the configurable parameter P is less than 1.

[0091] In this embodiment, the process of removing discontinuous columns from a class is as follows:

[0092] First, locate the points where the corresponding columns are not consecutive:

[0093] T = col n (i+1)-col n (i)

[0094]

[0095] In the formula, T represents the first intermediate parameter, Point(i) represents the point in the column, and col... n col is the set of all column positions of a certain class. n (i+1)-col n (i) indicates a shifted subtraction, that is, subtracting the i-th column from the (i+1)-th column in the n-th class, for example, col. n =Columns 1, 2, 3, 5. The result of subtracting the columns with shifted order is 2-1=1, 3-2=1, 5-3=2. If the columns are consecutive, the result is 1. If the columns are not consecutive, the result will always be greater than 1.

[0096] Secondly, find the positions of the corresponding columns that are consecutive but have small bandwidth:

[0097] T′=Point(i+1)-Point(i)

[0098] P start (n)=(T′=-1)

[0099] P stop (n)=(T′=1)

[0100] In the formula, T′ represents the second intermediate parameter, and P start P stop These represent all start and end positions of a continuous column; and are related to the aforementioned col. n Similarly, Point is the Point value calculated earlier. If it is a consecutive point, Point(i) is 1, otherwise it is 0. This step is also a subtraction with shifted positions. Point(i) is the value of the i-th point in the aforementioned Point, and Point(i+1) is the value of the (i+1)-th point in the aforementioned Point.

[0101] Then, let col′(n) = P start (n): P stop(n), col″(n) = (len(col′(n)) < (C×P)), and the corresponding column with narrow bandwidth in this class is set to 1, that is, Point(col″) = 1, where col′(n) represents the set of columns corresponding to the bandwidth of the nth suspicious signal, len(col′(n)) represents the length of col′(n), for example, A∶B represents a continuous sequence from A to B, such as 1∶5 represents 1, 2, 3, 4, 5, and the corresponding len(col′(n)) is 5; col″(n) represents the set of columns with a number less than the specified number of columns, for example, C×P is 5, col′(n) has col′(1) = 1, 2, 3, 4, 5, 6, 7, 8, col′(2) = 12, 13 and col′(3) = 17, 18, 19, then col″(n) = col′(2) and col′(3); Point(col″(n)) represents the column with narrow bandwidth;

[0102] Finally, delete the column corresponding to Point 1 in the class, thus removing the non-contiguous columns in the class.

[0103] Step 3: Sum the corresponding column delay frequency directions of each category in the signal time-frequency graph to obtain the pulse envelope of each category, i.e. Figure 6 As shown, and the pulse envelopes are analyzed. Figure 7 The binarization process shown is as follows, where the pulse envelope is:

[0104]

[0105] In the formula, E nt (r) represents class K n The pulse envelope, I(r, c) represents the r-th row and c-th column of the original signal time-frequency plot, col n (1) is the starting column col n (end) is the terminator column. The above formula represents the expression for col in the time-frequency diagram of the original signal. n Accumulate in the direction of column delay frequency.

[0106] In the specific implementation process, the implementation method of binarizing the pulse envelope in step 3 is the same as that in step 1. The difference is that step 1 is the binarization of two-dimensional data (image), while step 3 is the binarization of one-dimensional data (pulse envelope).

[0107] Step 4: Calculate the start and end lines and pulse width of the corresponding pulses in each class, and accumulate the corresponding row delay times of the pulses in the signal time-frequency diagram for each class to obtain the pulse spectrum envelope for each class. Figure 8As shown. The calculation method of the pulse spectrum envelope is the same as that of the pulse envelope in step 3. The only difference is that the pulse envelope in step 3 is based on the row-by-row accumulation of the extended signal time-frequency diagram, while the pulse spectrum envelope in step 4 is based on the row-by-row accumulation of the original signal time-frequency diagram before expansion.

[0108] In this embodiment, the start and end points of the corresponding pulses in each class are calculated based on the gradient. The specific implementation method for calculating the start and end points of the corresponding pulses in each class is basically the same as that in step 2. The only difference is that when the gradient is 1, it is recorded as the starting position, and when the gradient is -1, it is recorded as the ending position.

[0109] Step 5: Perform spectral envelope analysis on each pulse. Figure 9 After binarization, the start and end columns of the pulse spectrum envelope corresponding to each class are calculated and compared with the start and end columns of each class after clustering in step 2. The class with the smallest error is found, and this smallest error is used as the calculation result. Specifically:

[0110]

[0111]

[0112] In the formula, The corresponding class K of the calculation results n The minimum error of the starting column and the minimum error of the ending column, f start f stop These are the start and end columns of the pulse spectrum envelope, i.e., the start and end frequencies of the pulse spectrum envelope; Class K n The starting column and the ending column.

[0113] In the specific implementation process, the specific implementation method of binarizing each pulse spectrum envelope and calculating the start and end columns of each corresponding pulse spectrum envelope in step 5 is the same as that in steps 3-4, so it will not be described again in this embodiment.

[0114] Step 6: Repeat steps 3-5 until the error between the current calculation result and the previous calculation result tends to stabilize. Figure 10 By making corresponding determinations based on the frequency band of the stable results shown, it can be determined whether the signal is a drone signal. As for the specific implementation process of determining whether a signal is a drone image transmission signal based on the frequency band of the stable results, it is a conventional technical means in the field of electronic warfare, so it will not be described in detail in this embodiment.

[0115] In the specific implementation process, the method for determining when the error between the current calculation result and the previous calculation result has stabilized is as follows:

[0116] The minimum error of the starting column in the current calculation result is equal to the minimum error of the starting column in the previous calculation result, and the minimum error of the ending column in the current calculation result is equal to the minimum error of the ending column in the previous calculation result; or

[0117] The difference between the minimum error of the starting column in the current calculation result and the minimum error of the starting column in the previous calculation result is less than the accuracy threshold, and the difference between the minimum error of the ending column in the current calculation result and the minimum error of the ending column in the previous calculation result is less than the accuracy threshold.

[0118] refer to Figure 11 This embodiment also provides a terminal device including a transmitter, a receiver, a memory, and a processor. The transmitter is used to send instructions and data, the receiver is used to receive instructions and data, the memory is used to store computer-executed instructions, and the processor is used to execute the computer-executed instructions stored in the memory to implement the various steps performed by the image transmission signal detection method in the above embodiment. Its specific implementation process is the same as the aforementioned image transmission signal detection method.

[0119] It should be noted that the aforementioned memory can be either standalone or integrated with the processor. When the memory is set up independently, the terminal device also includes a bus for connecting the memory and the processor.

[0120] This embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the processor executes the computer-executable instructions, the image transmission signal detection method executed by the terminal device described above is implemented.

[0121] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A time-frequency map based image signal detection method, characterized in that, Includes the following steps: Step 1: The received signal is sampled by an ADC, windowed, and subjected to a short-time Fourier transform to obtain a signal time-frequency graph. The signal time-frequency graph is then binarized using an adaptive threshold. The process of performing mean filtering and adaptive threshold binarization on the signal time-frequency graph is as follows: Step 1.1: Expand the signal time-frequency diagram by mirror image according to the mean filter window size. For a window size of... The signal time-frequency diagram is expanded by reversing the row and column order of the upper, lower, left, and right boundaries. OK, The values ​​on the diagonal of the column are also filled with the values ​​on the diagonal in a mirror order, where... Indicates rounding down; Step 1.2: Calculate the integral image based on the extended signal time-frequency diagram, and obtain the mean-filtered signal time-frequency diagram based on the integral image; Step 1.3: Calculate the adaptive threshold based on the time-frequency graph of the mean-filtered signal, as follows: In the formula, is an adaptive threshold, is a mean filtered signal time-frequency image, is a settable parameter; Step 1.4: Binarize the original signal time-frequency diagram based on the adaptive threshold; Step 2: After performing a Fourier transform on the time-frequency graph of the binarized signal in the time direction, count the position of the maximum value in each column and cluster them according to the position of the maximum value, and remove the classes with too narrow bandwidth and the discontinuous columns in the classes. The process of removing non-contiguous columns from a class is as follows: Find the positions of the points where the corresponding column is not continuous, as follows: wherein represents a first intermediate parameter, represents a first intermediate parameter, represents a first intermediate parameter, represents a first intermediate parameter, represents a first intermediate parameter, represents a point in the column; Find the positions of consecutive columns with small bandwidth: wherein denotes a second intermediate parameter, , are all start positions, all end positions, respectively, of a consecutive column; make , And in this class, the corresponding column with excessively narrow bandwidth is set to 1, that is... ,in, Indicates the first The set of columns corresponding to the bandwidth of each suspicious signal. This indicates that the number of columns is less than the specified set of columns. express Length, This represents the column number of the signal time-frequency diagram. These are configurable parameters. Columns indicating narrow bandwidth; Delete the corresponding column for which the value is 1. Delete the corresponding column for which the value is 1. Step 3: Sum the corresponding column delay frequency directions of each category in the signal time-frequency graph to obtain the pulse envelope of each category, and then perform binarization on each pulse envelope; Step 4: Calculate the start and end lines and pulse width of the corresponding pulses in each class, and accumulate the corresponding line delay time directions of the pulses in the signal time-frequency diagram of a single class to obtain the pulse spectrum envelope of each class. Step 5: After binarizing the pulse spectrum envelope, calculate the start and end columns of the pulse spectrum envelope corresponding to each class, compare them with the start and end columns of each class after clustering in Step 2, find the class with the smallest error, and take the smallest error as the calculation result. Step 6: Repeat steps 3-5 until the error between the current calculation result and the previous calculation result tends to stabilize. Then, make a corresponding judgment on the frequency band of the stable result to determine whether the signal is the target signal. The judgment method after the error between the current calculation result and the previous calculation result tends to stabilize is as follows: the minimum error of the starting column in the current calculation result is equal to the minimum error of the starting column in the previous calculation result, and the minimum error of the ending column in the current calculation result is equal to the minimum error of the ending column in the previous calculation result; or the difference between the minimum error of the starting column in the current calculation result and the minimum error of the starting column in the previous calculation result is less than the accuracy threshold, and the difference between the minimum error of the ending column in the current calculation result and the minimum error of the ending column in the previous calculation result is less than the accuracy threshold.

2. The time-frequency map based image signal detection method of claim 1, wherein, In step 2, the step of calculating the position of the maximum value in each column and performing clustering based on the position of the maximum value specifically involves: Find the position of the maximum value of each column after Fourier transform, and classify the columns with the same maximum value position into one class, denoted as , the position of the corresponding column is denoted as , the starting column is denoted as , and the ending column is denoted as .

3. The time-frequency map based map signal detection method of claim 2, wherein, In step 2, the process of removing classes with insufficient bandwidth is as follows: wherein, denotes the deletion of the corresponding class, is the class number of columns contained in the class, is the number of columns of the signal time-frequency map, is a configurable parameter less than 1.

4. The time-frequency map based map signal detection method of claim 1, wherein, In step 3, the pulse envelope is: wherein is a class of pulse envelopes, is a class of corresponding column positions, denotes the original signal time-frequency plot at row and column .

5. The time-frequency map based graph signal detection method of claim 1, wherein, In step 4, the start and end points of the corresponding pulses in each class are calculated based on the gradient, where the gradient is the 1-bit start position and the gradient is the -1-bit end position.

6. The time-frequency map based map signal detection method of claim 1, wherein, In step 5, the calculation result is: In the formula, , The corresponding classes of the calculation results Minimum error of the starting column and minimum error of the ending column. , These are the start and end columns of the pulse spectrum envelope, i.e., the start and end frequencies of the pulse spectrum envelope; , Classes The starting column and the ending column.

7. A terminal device, characterized by comprising: include: Memory, used to store programs; A processor is configured to execute the program stored in the memory, wherein when the program is executed, the processor is configured to perform some or all of the steps of the image transmission signal detection method based on time-frequency diagrams as described in any one of claims 1 to 6.

8. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions; when the computer execution instructions are executed by the processor, the computer execution instructions are used to implement part or all steps of the time-frequency map based image signal detection method according to any one of claims 1 to 6.

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