A method, device, storage medium and computing device for detecting OTFS modulation signals based on super-threshold method and visual graph

Abstract

The present invention provides an OTFS modulation signal detection method, device, storage medium and computing equipment based on super-threshold method and visual graph. The method firstly takes the modulus square of the observed signal; then samples the observed signal after modulus square based on the super-threshold method; then calculates the corresponding power spectrum sequence and performs visual graph conversion on the new sequence obtained after sampling, thereby obtaining the corresponding visual graph signal, and performs a graphic Fourier transform on the obtained visual graph signal, and finally uses the smoothness of the graphic signal as the detection statistic, sets the decision threshold, and judges whether the observed signal is an OTFS modulation signal or an ordinary noise signal. The detection performance of the method of the present invention is better than that of the method of directly performing visual graph conversion on the time domain signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to an OTFS modulation signal detection method, device, storage medium and computing equipment based on a super-threshold method and a visible graph. Background Art

[0002] In recent years, with the improvement of information and communication technology in various countries, multi-carrier modulated signals have become a new topic in the current signal development field. Radar communication integrated signals, as a type of multi-carrier modulated signal, are also an important field of contemporary electronic warfare, which has led to more and more research and development investment in the field of radar communications in various countries. Nowadays, the electromagnetic environment is becoming more and more complex, and traditional single electronic countermeasure equipment can no longer adapt to the current trend of electronic warfare. With the development of radar communication integrated systems, in future electronic warfare, electronic countermeasure equipment may need to have communication functions, radar functions, and signal processing functions at the same time. With the continuous development and advancement of 5G and 6G technologies, various multi-carrier modulated signals, such as orthogonal time-frequency space signals (OTFS), have begun to enter the research scope of various countries. However, the detection problem of OTFS modulated signals has always been a research difficulty, which is specifically manifested as follows:

[0003] (1) Various noise or background interference and other issues. As the electromagnetic environment becomes more and more complex, signals are easily interfered by various noises.

[0004] (2) There are few relevant detection algorithms. Since multi-carrier modulation signals emerged in the context of 5G and 6G, there is currently a lack of effective detection algorithms.

[0005] (3) The detection effect of traditional detection methods is poor. The waveform of some multi-carrier modulation signals is similar to the noise waveform, and traditional detection methods cannot effectively detect them.

[0006] There are currently three types of research directions: one is the research on signal detection algorithms based on traditional regular domains; the second is the research on signal detection algorithms based on non-regular domains, such as the correlation detection algorithm based on graph domain signal processing; the third is the research on signal detection algorithms based on machine learning and deep learning.

[0007] Since the waveform of the multi-carrier modulation signal represented by the OTFS modulation signal is similar to the noise waveform, the detection effect of the signal detection method based on the traditional regular domain is not very ideal. Summary of the invention

[0008] Aiming at the problem of poor detection performance of OTFS modulation signal at present, the present invention proposes an OTFS modulation signal detection method, device, storage medium and computing equipment based on super-threshold method and visible graph, converts OTFS modulation signal into visible graph signal, and detects signal and noise by taking the smoothness of visible graph signal as detection feature.

[0009] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0010] In a first aspect, the present invention provides an OTFS modulation signal detection method based on a super-threshold method and a visual graph, comprising:

[0011] Acquire an observation signal, and modulo-square the observation signal to obtain an observation signal sequence after modulo-square;

[0012] The observed signal sequence after modulo square is sampled by using a super-threshold method to obtain a sampled signal sequence;

[0013] Calculating the power spectrum of the sampling signal sequence and converting the power spectrum into a visual image;

[0014] Performing Fourier transform on the power spectrum as a visual image signal, and calculating the smoothness of the visual image signal;

[0015] A detection is performed based on the smoothness to determine whether there is an OTFS modulation signal in the observed signal.

[0016] Preferably, the observed signal sequence after modulo squaring is sampled using a super-threshold method to obtain a sampled signal sequence, including:

[0017] Normalizing the observed signal sequence after modulo squaring to obtain a normalized observed signal sequence;

[0018] Set a threshold value, return the element values ​​in the normalized observation signal sequence that are less than the threshold value to 0, and obtain the index of the observation signal that has not been returned to 0;

[0019] According to the observed signal index that has not been returned to zero, the corresponding element is taken from the observed signal sequence after modulo squaring to obtain a sampled signal sequence.

[0020] Preferably, calculating the power spectrum of the sampling signal sequence and converting the power spectrum into a visual graph comprises:

[0021] The discrete Fourier transform is calculated for the sampled signal sequence to obtain:

[0022] ;

[0023] in, , , is the sampling signal The value after discrete Fourier transformation, is the number of sampled signals;

[0024] The power spectrum is calculated based on the discrete Fourier transformed value as follows:

[0025] ;

[0026] in, The sampling signal The power spectrum of

[0027] The power spectrum is converted into a visibility graph G(V,E), which consists of a node set V and an edge set E.

[0028] Preferably, performing a Fourier transform of the power spectrum as a visual image signal comprises:

[0029] The power spectrum Treated as a visual signal ,Right now:

[0030] ;

[0031] Calculate the adjacency matrix of the visibility graph Sum degree matrix ;

[0032] Based on the adjacency matrix of the visibility graph Sum degree matrix Compute the Laplacian matrix :

[0033] ;

[0034] Calculate the Laplacian matrix The eigenvalues ​​and eigenvectors of get the characteristic matrix ;

[0035] Based on the feature matrix The visual signal Perform Fourier transform on the graph and get:

[0036] ;

[0037] in, Visual signal The Fourier transform result of the graph is: is the feature matrix The transpose of .

[0038] Preferably, calculating the smoothness of the visual image signal includes:

[0039] ,

[0040] in, For smoothness, is the Laplace matrix No. feature values.

[0041] Preferably, performing detection based on the smoothness to determine whether there is an OTFS modulation signal in the observed signal includes:

[0042] Setting Threshold Value , if the smoothness is Greater than the threshold , it is determined that the observed signal contains an OTFS modulation signal; otherwise, there is only a noise signal in the observed signal.

[0043] In a second aspect, the present invention provides an OTFS modulation signal detection device based on a super-threshold method and a visible graph, which is used to implement the above-mentioned OTFS modulation signal detection method based on a super-threshold method and a visible graph, and the device includes:

[0044] A signal processing module, used for acquiring an observation signal, performing modulus square operation on the observation signal, and obtaining a sequence of observation signals after modulus square operation;

[0045] A signal sampling module, used for sampling the observed signal sequence after modulus square by using a super-threshold method to obtain a sampled signal sequence;

[0046] A graph conversion module, used for calculating the power spectrum of the sampling signal sequence and converting the power spectrum into a visible graph;

[0047] A calculation module, used for performing Fourier transform on the power spectrum as a visual image signal, and calculating the smoothness of the visual image signal;

[0048] The detection module is used to perform detection based on the smoothness to determine whether there is an OTFS modulation signal in the observed signal.

[0049] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0050] In a fourth aspect, the present invention provides a computing device, comprising:

[0051] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described above.

[0052] The beneficial effects of the present invention are:

[0053] The present invention provides an OTFS modulation signal detection method based on POT and visual graph, adopts the graph domain signal detection method in the irregular domain, converts the OTFS modulation signal into a visual graph signal, and detects the signal and noise by using the smoothness of the visual graph signal as a detection feature. The method utilizes the more intuitive feature information of the graph, so it can better mine the potential features of the graph. From the input signal after the graph conversion, the power spectrum sequences of the noise signal H0 and the OTFS modulation signal H1 have no obvious differences except for the large difference in amplitude, but after conversion to the visual graph, the difference between the two is large. Therefore, it is beneficial to improve the detection effect of the signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of an OTFS modulation signal detection method based on POT and visual graph provided by an embodiment of the present invention;

[0055] Figure 2 (a) is the unsampled power spectrum sequence of the noise signal H0;

[0056] Figure 2 (b) is the unsampled power spectrum sequence of the OTFS modulated signal H1;

[0057] Figure 3 (a) is the visual diagram of the unsampled power spectrum sequence transformation of the noise signal H0;

[0058] Figure 3 (b) is a visual diagram of the unsampled power spectrum sequence conversion of the OTFS modulated signal H1;

[0059] Figure 4 (a) is the power spectrum sequence of the noise signal H0 after the POT sampling threshold is set to 0.05;

[0060] Figure 4 (b) is the power spectrum sequence of the OTFS modulated signal H1 after the POT sampling threshold is set to 0.05;

[0061] Figure 5 (a) is the visual diagram of the power spectrum sequence corresponding to the transformation of the noise signal H0 after the POT sampling threshold is set to 0.05;

[0062] Figure 5(b) is the visual diagram of the power spectrum sequence corresponding to the conversion of the OTFS modulated signal H1 after setting the POT sampling threshold to 0.05;

[0063] Figure 6 (a) is the power spectrum sequence of the noise signal H0 after the POT sampling threshold is set to 0.1;

[0064] Figure 6 (b) is the power spectrum sequence of the OTFS modulated signal H1 after the POT sampling threshold is set to 0.1;

[0065] Figure 7 (a) is the visual diagram of the power spectrum sequence corresponding to the transformation of the noise signal H0 after setting the POT sampling threshold to 0.1;

[0066] Figure 7 (b) is the visual diagram of the power spectrum sequence corresponding to the conversion of the OTFS modulated signal H1 after setting the POT sampling threshold to 0.1;

[0067] Figure 8 It is a schematic diagram for comparing working curves when different POT thresholds are set in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The specific implementation of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The embodiments described below with reference to the accompanying drawings are illustrative and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0069] It should be noted that the term "one embodiment" or "embodiment" of the present invention refers to a specific feature, structure or characteristic that can be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0070] The specific embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0071] Example 1

[0072] This embodiment 1 provides an OTFS modulation signal detection method based on a super-threshold method and a visual graph, see Figure 1 ,include:

[0073] Step 1: Get the observed signal and square the observed signal modulo;

[0074] Step 2: The observed signal after modulus square is sampled using the Peaks Over Threshold (POT) method;

[0075] Step 3: Calculate the power spectrum of the sampled signal, and perform visual graph conversion on the power spectrum of the sampled signal to obtain a corresponding visual graph signal;

[0076] Step 4: Perform Fourier transform on the visual image signal corresponding to the power spectrum and calculate the smoothness;

[0077] Step 5: Perform detection based on smoothness to determine whether there is an OTFS modulation signal in the observed signal.

[0078] In this embodiment, the observed signal is an OTFS signal, and the MATLAB simulation modulation code of OTFS is run to obtain the observed signal. The OTFS modulation signal is different from the traditional BPSK and QPSK modulation signals. The OTFS modulation signal is a modulation technology based on the two-dimensional orthogonality of time and frequency. It converts the data signal from the time domain and frequency domain to the time and frequency domain, and uses the orthogonality of the time and frequency domain to achieve efficient data transmission. OTFS modulation uses discrete Fourier transform (DFT) to convert the time domain signal to the frequency domain signal, and then multiplies the frequency domain signal with the orthogonal time domain sequence, and finally performs an inverse discrete Fourier transform (IDFT) to convert the signal back to the time domain.

[0079] In this embodiment, the question of whether there is a target signal in the received signal sequence, i.e., the observed signal, is described as a binary hypothesis test. Define H0: There is no target signal or only a noise signal in the observed signal; H1: The observed signal consists of a target signal and a noise signal; N is the signal dimension. Therefore, the received signal of the radar is:

[0080] ,

[0081] in represents the signal sequence received by the radar, is an additive white Gaussian noise sequence, is a multi-carrier modulation signal sequence.

[0082] For the observed signal Take the modulus square in the time domain, that is:

[0083] , .

[0084] In this embodiment, the observed signal after modulus square is sampled using the POT method, and the specific implementation process is as follows:

[0085] S21, the observed signal after modulus square Perform normalization, that is:

[0086] ,

[0087] for The normalized sequence is express , , the minimum value among express , , the maximum value among.

[0088] S22. Use the POT method to sample as follows:

[0089] After normalization, the sequence , Setting Threshold Values , return the element values ​​less than the threshold value to 0, thus obtaining the index of the row that is not returned to 0 ,

[0090] ,

[0091] According to the index of the unreturned zero row , from the sequence after modulo square , Take out the corresponding non-zero row from the output to get the sampling signal, that is, all From the original sequence Extract the constituent sampling signal , is the number of rows that are not returned to zero, that is, the number of sampled signals.

[0092] In this embodiment, the power spectrum of the sampled signal is calculated and converted into a visual graph. The specific implementation process is as follows:

[0093] S31, sampling signal Calculate the discrete Fourier transform to get , and then calculate the corresponding power spectrum sequence of the obtained discrete Fourier transform sequence ,Right now:

[0094] ,

[0095] .

[0096] S32, the power spectrum obtained Converted into a corresponding visual graph G(V,E),

[0097] The visible graph G(V,E) consists of a node set V and an edge set E. The nodes are the dimensions of the observed signal. Given.

[0098] In one embodiment, the power spectrum sequence of the observed signal and the visual graph are compared, such as Figure 2 (a) and Figure 2 (b) is the unsampled power spectrum sequence of the noise signal H0 and the OTFS modulated signal H1; Figure 3 (a) and Figure 3 (b) is a visual diagram of the transformation of the unsampled power spectrum sequence of H0 and H1; Figure 4 (a) and Figure 4 (b) is the power spectrum sequence after H0 and H1 set the POT sampling threshold to 0.05; Figure 5 (a) and Figure 5 (b) is the visual graph of the corresponding transformation of the power spectrum sequence after H0 and H1 set the POT sampling threshold to 0.05; Figure 6 (a) and Figure 6 (b) is the power spectrum sequence after H0 and H1 set the POT sampling threshold to 0.1; Figure 7 (a) and Figure 7 (b) is the visual graph of the power spectrum sequence of H0 and H1 after setting the POT sampling threshold to 0.1. By observing the power spectrum sequences of H0 and H1 and their corresponding visual graphs, it can be found that the difference between the ordinary power spectrum sequences of the two is not large, but after converting the power spectrum sequences into the corresponding visual graphs, the difference between the two is very obvious.

[0099] Therefore, in the embodiment of the present invention, the POT method is used for sampling, the power spectrum of the sampled signal is calculated, and the power spectrum of the sampled signal is converted into a visible graph to obtain a corresponding visible graph, and the power spectrum is used as a visible graph signal for subsequent detection.

[0100] In this embodiment, Fourier transform is performed on the visible image signal, and the smoothness is calculated. The smoothness of the visible image signal is used as a detection feature to detect the observation signal. The specific implementation process is as follows:

[0101] Defining visual signals , since the visual image transform is a one-to-one mapping, the power spectrum Treated as a visual signal ,Right now:

[0102] ,

[0103] The adjacency matrix is ​​used to describe the connection relationship matrix between nodes in a graph, that is, in an undirected graph, if there is a connection between node a and node b, the value of the element in the ath row and bth column and the bth row and ath column in the adjacency matrix is ​​1, otherwise it is 0. If the graph is a weighted graph, the elements of the adjacency matrix can be expressed as the strength of the connection, usually the weight value of the connection.

[0104] The visible graph is an undirected and unweighted graph, so the power spectrum after sampling is defined as The visible adjacency matrix of ,for:

[0105] ,

[0106] is the adjacency matrix The element at row a and column b in .

[0107] The degree matrix is ​​a diagonal matrix whose diagonal elements represent the degree of each node, that is, the number of edges connected to the node. Therefore, in the visual graph, the degree of a node is the number of edges connected to it.

[0108] Defining the degree matrix ,Right now:

[0109] ,

[0110] For Node The degree.

[0111] S31: Adjacency matrix based on the visible graph Sum degree matrix Compute the Laplacian matrix ,Right now:

[0112] ,

[0113] S32: Calculate the Laplacian matrix The eigenvalue of and the eigenvectors consisting of the eigenvalues , and calculate the feature matrix , based on the feature matrix for the visual signal Perform Fourier transform on the graph to get , expressed as:

[0114] ,

[0115] ,

[0116] ,

[0117] in for Unit matrix, characteristic matrix From all the eigenvectors composition, is the feature matrix The transpose of .

[0118] S33: Define the smoothness of the visual signal as ,

[0119] Calculate the smoothness of the visual signal based on the Fourier transform and eigenvalue of the graph ,as follows:

[0120] ,

[0121] in is the Laplace matrix No. feature values.

[0122] Setting Threshold Value , determine whether the observed signal is H0 or H1,

[0123] Specifically, if Greater than threshold , then it is determined that the observed signal contains an OTFS modulation signal, otherwise, the observed signal is a noise signal.

[0124] In one embodiment, different detection methods are simulated and compared, including calculating the visual smoothness of the unsampled power spectrum sequence for detection, setting the POT sampling threshold to 0.1 and calculating the visual smoothness of the power spectrum sequence for detection, and setting the POT sampling threshold to 0.05 and calculating the visual smoothness of the power spectrum sequence for detection. In order to make full use of the data and avoid experimental contingency, the simulation conditions are: 1000 simulations are performed and the signal-to-noise ratio SNR is set to -10dB. Figure 8 The algorithm time t and the working curve (ROC, Receiver Operating Characteristic Curve) area auc of different detection methods and different POT thresholds are shown respectively. It can be seen that the area under the ROC curve of this method is larger than that of other methods, and after POT sampling, the algorithm performance is almost the same, but the algorithm operation time is greatly reduced. Therefore, the method improved in this embodiment can better detect OTFS modulated signals, and the detection accuracy is greatly improved.

[0125] Example 2

[0126] Based on the above inventive concept, this embodiment 2 provides an OTFS modulation signal detection device based on a super-threshold method and a visible graph, which is used to implement the method of the above embodiment, and the device includes:

[0127] A signal processing module, used for acquiring an observation signal, performing modulus square operation on the observation signal, and obtaining a sequence of observation signals after modulus square operation;

[0128] A signal sampling module, used for sampling the observed signal sequence after modulus square by using a super-threshold method to obtain a sampled signal sequence;

[0129] A graph conversion module, used for calculating the power spectrum of the sampling signal sequence and converting the power spectrum into a visible graph;

[0130] A calculation module, used for performing Fourier transform on the power spectrum as a visual image signal, and calculating the smoothness of the visual image signal;

[0131] The detection module is used to perform detection based on the smoothness to determine whether there is an OTFS modulation signal in the observed signal.

[0132] It is worth pointing out that the device embodiment corresponds to the above-mentioned method embodiment, and the implementation methods of the above-mentioned method embodiments are all applicable to the device embodiment and can achieve the same or similar technical effects, so they will not be repeated here.

[0133] Example 3

[0134] Based on the above inventive concept, this embodiment 3 provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes the method according to the above embodiment 1.

[0135] Example 4

[0136] Based on the above-mentioned inventive concept, this embodiment 4 provides a computing device, including one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method of the above-mentioned embodiment 1.

[0137] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting OTFS modulation signals based on super-threshold method and visual graph, characterized in that: include: Acquire an observation signal, and modulo-square the observation signal to obtain an observation signal sequence after modulo-square; The observed signal sequence after modulus square is sampled by using a superthreshold method to obtain a sampled signal sequence, including: normalizing the observed signal sequence after modulus square to obtain a normalized observed signal sequence; setting a threshold value, returning element values ​​in the normalized observed signal sequence that are less than the threshold value to 0, and obtaining an observed signal index that is not returned to 0; according to the observed signal index that is not returned to 0, taking out the corresponding element from the observed signal sequence after modulus square to obtain a sampled signal sequence; Calculating the power spectrum of the sampled signal sequence and converting the power spectrum into a visual graph comprises: The discrete Fourier transform is calculated for the sampled signal sequence to obtain: Wherein, F(l), 1≤l≤C, is the value after discrete Fourier transformation of the sampling signal M(v), v=1, 2, ..., C, and C is the number of sampling signals; The power spectrum is calculated based on the discrete Fourier transformed value as follows: Wherein, P(l) is the power spectrum of the sampled signal M(v); Convert the power spectrum into a visible graph G(V, E), where the visible graph G(V, E) consists of a node set V and an edge set E; Performing a Fourier transform of a graph using the power spectrum as a visual graph signal includes: The power spectrum P(l) is regarded as a visual image signal T, that is: T=P(l). Calculating an adjacency matrix A and a degree matrix D of the visibility graph; The Laplacian matrix L is calculated based on the adjacency matrix A and degree matrix D of the visibility graph: L = DA; Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix L to obtain a characteristic matrix U; Performing a Fourier transform on the visual image signal T based on the feature matrix U, the following is obtained: in, is the Fourier transform result of the visual signal T, U T is the transpose of the feature matrix U; Calculating the smoothness of the visual image signal includes: Among them, B is the smoothness, λ m is the mth eigenvalue of the Laplace matrix L; Detection is performed based on the smoothness to determine whether there is an OTFS modulation signal in the observation signal, including: setting a threshold value K, if the smoothness B is greater than the threshold value K, then determining that the observation signal contains the OTFS modulation signal; otherwise, only a noise signal exists in the observation signal.

2. An OTFS modulation signal detection device based on super-threshold method and visual graph, characterized in that: The device for implementing the OTFS modulation signal detection method based on the super-threshold method and the visible graph according to claim 1 comprises: A signal processing module, used for acquiring an observation signal, performing modulus square operation on the observation signal, and obtaining a sequence of observation signals after modulus square operation; A signal sampling module, used for sampling the observed signal sequence after modulus square by using a super-threshold method to obtain a sampled signal sequence; A graph conversion module, used for calculating the power spectrum of the sampling signal sequence and converting the power spectrum into a visible graph; A calculation module, used for performing Fourier transform on the power spectrum as a visual image signal, and calculating the smoothness of the visual image signal; The detection module is used to perform detection based on the smoothness to determine whether there is an OTFS modulation signal in the observed signal.

3. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of claim 1 .

4. A computing device, characterized in that include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method according to claim 1.

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