Modulation period estimation method and system

Abstract

The invention provides a modulation period estimation method and system, and relates to the technical field of communication and radar signal analysis. The method comprises the following steps: receiving communication and radar signals to obtain a first feature map matrix; inputting the first feature map matrix into a first estimation classification network model to obtain a first classification result, and outputting a first discrimination result; continuously circulating to obtain a preliminary estimation period; obtaining a modulation period spectrum based on the communication and radar signals; performing calculation based on the modulation period spectrum in the initial estimation period to obtain a corresponding differential signal, and further obtaining a second feature map matrix; and inputting the second feature map matrix into a second estimation classification network model to obtain a second classification result, and outputting a second discrimination result to obtain a modulation period. The method solves the problem of how to directly carry out modulation period estimation on communication and radar signals of various different typical signal modulation systems under the condition of not knowing the signal modulation system and not additionally increasing modulation system identification steps.

Description

Technical Field

[0001] The present invention relates to the technical field of communication and radar signal analysis, and in particular to a modulation period estimation method and system. Background Art

[0002] To facilitate information transmission, in communication and radar systems, signals are modulated at certain time intervals, which is the symbol period of communication signals or the pulse repetition period of radar signals, collectively referred to as the modulation period. For monitoring and receiving communication and radar signals, the key to extracting information under non-cooperative conditions is to estimate their modulation parameters, and one of the most critical modulation parameters is the modulation period of the signal. Mastering the modulation period can provide favorable conditions for estimating other parameters and information under non-cooperative conditions.

[0003] For communication and radar signals, there are currently many methods to analyze and estimate the modulation period. However, communication and radar signals have many different modulation systems, such as multi-level quadrature amplitude modulation (mQAM), multi-level digital phase modulation (mPSK), multi-level frequency shift keying (mFSK), pulse Doppler, pseudo-code phase modulation coincidence pulse, etc. Different modulation systems use different analysis methods for system signals. Therefore, modulation identification needs to be performed before signal analysis and processing, which makes the entire signal processing process complicated, increases the difficulty of hardware implementation and the difficulty of estimating the modulation period. Summary of the invention

[0004] Based on this, it is necessary to provide a modulation period estimation method and system to address the above-mentioned technical problems, so as to solve the problem of how to directly estimate the modulation period for communication and radar signals of various different typical signal modulation systems without knowing the signal modulation system and without adding additional modulation system identification steps.

[0005] The present invention provides a modulation period estimation method, the method comprising: S100: receiving communication and radar signals to obtain a first feature map matrix; inputting the first feature map matrix into a constructed first estimation classification network model to obtain a first classification result; discriminating the first classification result and outputting a first discrimination result; S200: Continuously loop S100 until the preset conditions are met Then, the index position of the last jump point in the first discrimination result is searched within the preliminary search interval to obtain a preliminary estimated period; S300: Based on the communication and radar signals, a modulation period spectrum is obtained; within the preliminary estimation period, a corresponding differential signal is calculated based on the modulation period spectrum; and a covariance matrix is ​​constructed according to the differential signal to obtain a second feature map matrix; S400: inputting the second feature map matrix into the constructed second estimation classification network model to obtain a second classification result; performing discrimination on the second classification result and outputting a second discrimination result; S500: Obtain a modulation period according to the maximum value of the second discrimination result.

[0006] Furthermore, obtaining the first feature map matrix includes: S101: Order ,set up Value and value; Indicates the length of continuous detection The number of segments of the point signal, Indicates the number of maximum singular values ​​selected; S102: The front of the communication and radar signal points are discarded to obtain a first signal, based on the previous The point value constructs the dimension The Hankel matrix of S103: Perform singular value decomposition on the Hankel matrix to obtain a corresponding left singular matrix, a singular value diagonal matrix, and a right singular matrix; select the first singular value matrix in the singular value diagonal matrix. maximum singular values, and reconstruct the Hankel matrix using the corresponding left singular matrix and the right singular matrix to obtain a first covariance matrix; S104: If , then let , and keep looping S102~S103 until , the corresponding The first covariance matrices are superimposed and averaged to obtain Hankel cumulative covariance matrix in dimensions; S105: Standardize the Hankel cumulative covariance matrix to obtain the first feature map matrix.

[0007] Furthermore, the first estimation classification network model The build includes: S111: Establishing a signal sample library; S112: Order ; Select the first Signal ;in, The label is , the modulation period is ; Redefine the received signal: , , ; S113: Select Value; if , then in the interval A random integer value is selected as Value; if , , then in the interval A random integer value is selected as Value; if , then in the interval A random integer value is selected as value; S114: Based on the received signal Get the first feature map matrix , and label it , added to the first feature map sample library, and let ; S115: Continuously looping S112 to S114 until all first feature map matrices corresponding to the signals in the signal sample library are obtained, thereby completing the establishment of the first feature map sample library; S116: Obtaining the first estimation classification network model based on the first feature map sample library training .

[0008] Furthermore, the first classification result is judged, and outputting the first judgment result includes: Determine the first classification result Is it less than the judgment standard? , the first discrimination result is output as: ;in, is a step function; when When The first discrimination result outputted is 0, otherwise the first discrimination result outputted is 1.

[0009] Furthermore, the preliminary estimation period The calculation method is: ; in, , Representing the number of reference points of the preliminary search interval; Indicates the index position of the last jump point in the first discrimination result; Indicates the judgment criteria.

[0010] Furthermore, obtaining the modulation period spectrum includes: S301: Setting the row values ​​of the dynamic matrix and column values ,in, ; S302: Take the front Signal points , arrange the point values ​​in column order, and get OK Dynamic matrix of columns; S303: Divide the dynamic matrix into two parts evenly to obtain the first part and Part II ; and based on the first part and the second part Correspondingly, two covariance matrices are obtained; S304: respectively calculating the sum of the modulus values ​​of each element in the two covariance matrices, and taking the larger value of the two; S305: If , then let , and keep looping S302~S304 until , and obtain the modulation periodic spectrum.

[0011] Furthermore, obtaining the second feature map matrix includes: Based on the multiple differential signals, multiple maximum points of the corresponding differential signals are obtained, and the indexes of the multiple maximum points are arranged in the order of the values ​​in the differential signals to obtain an index set. ; Select the index collection The element value in ,use OK Dynamic matrix construction of columns dimensional covariance matrix, and standardize the covariance matrix to obtain the second feature map matrix.

[0012] Furthermore, the second estimation classification network model The build includes: S401: Establishing a signal sample library; S402: Order ; Select the first Signal ;in, The label is , the modulation period is ; Redefine the received signal: , , ; S403: Select Value; if , then let ,in, is a rational fraction less than 1; if , then let ,in, is a rational fraction greater than 1 and less than 5; if , then let ; , then let ,in, for A random integer value within ; if , then let ,in, is any other random integer value except the four cases in S403; S404: Take the front Signal points , arrange the point values ​​in column order, and get OK A dynamic matrix of columns; based on the dynamic matrix, a covariance matrix is ​​obtained, and the covariance matrix is ​​standardized to obtain a second feature map matrix ; and label it , added to the second feature map sample library, and let ; S405: Continuously looping S403 to S404 until all second feature map matrices corresponding to the signals in the signal sample library are obtained, thereby completing the establishment of the second feature map sample library; S406: Obtaining the second estimation classification network model based on the second feature map sample library training .

[0013] Furthermore, the modulation period The calculation method is: ; in, represents the second classification result; is a step function; Indicates the number of multiple maximum value points of the corresponding differential signal; Indicates the maximum value point of the differential signal; Indicates the maximum value of the second classification result.

[0014] On the other hand, the present invention further provides a modulation period estimation system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the above methods.

[0015] In general, the present invention provides a modulation period estimation method and system, which can achieve the following beneficial effects compared with the prior art: (1) The present invention can directly estimate the modulation period for communication and radar signals of various typical signal modulation systems without knowing the signal modulation system and without adding additional modulation system identification steps. In addition, in the method proposed by the present invention, the training of the first estimation classification network model for rough estimation and the training of the second estimation classification network model for precise estimation only need to be performed once. No matter how many signals are subsequently subjected to modulation period estimation, only the trained estimation classification network model needs to be used, and there is no need to repeat the training process. The entire estimation process is simpler, more convenient, and faster.

[0016] (2) On the one hand, the present invention utilizes the structural characteristics of the dynamic matrix under the modulation period dimension, and obtains the modulation period spectrum by summing the module values ​​of all elements of the covariance matrix after segmentation to highlight this special structural feature; on the other hand, the Hankel cumulative covariance matrix feature map constructed by the present invention has a dimension of When taking different values ​​(that is, when being greater than different multiples of the modulation period), it has different structural characteristics; therefore, a rough estimate of the modulation period of the signal can be made, and then these structural characteristics can be used to roughly determine a suitable preliminary estimation period, so as to avoid the search interval of the selected modulation period being too small or too large without any prior information, thereby increasing the estimation efficiency and accuracy.

[0017] (3) Based on the feature graph and its special structural features, this paper proposes to use convolutional neural network (ResNet101) for training under large sample conditions, and realizes the The characteristic graph under the value condition is classified and identified, making the estimation of the modulation period faster and more accurate.

[0018] (4) The dynamic covariance matrix feature map constructed by the present invention has a dimension When taking different values ​​(integer or fractional multiples of the modulation period), it also has different structural characteristics, especially When taking different integer multiples of the modulation period, the discrimination is greater, based on which the modulation period under different integer multiples can be distinguished. The values ​​are classified and identified in order to accurately find the real modulation period.

[0019] (5) Since the modulation period spectrum may obtain local maxima at integer multiples and fractional multiples of the modulation period, it is impossible to directly estimate the modulation period by searching for the maximum value point, and there will be a problem of fuzzy period estimation. Therefore, the present invention combines the modulation period spectrum with the structural characteristics of the dynamic covariance matrix, finds multiple local maximum points as candidate values ​​of the true modulation period through the modulation period spectrum, and then classifies and identifies the dynamic covariance matrix feature graphs of the dimensions corresponding to the multiple candidate values ​​to find the true modulation period, thereby eliminating the fractional and integer multiples of the modulation period in the multiple candidate values, solving the problem of fuzzy period estimation and obtaining a better estimation effect.

[0020] (6) Although the candidate values ​​of the modulation period can be obtained by searching the modulation period spectrum to find multiple local maximum points, it is difficult to determine the range of the search without any prior information. Therefore, the present invention utilizes the structural characteristics of the Hankel cumulative covariance matrix, classifies and identifies the characteristic graph of the Hankel cumulative covariance matrix, finds the value of M greater than a certain integer multiple of the modulation period, and thus determines that a search interval includes at least an integer multiple of a modulation period in [N, 10N], thereby greatly improving the efficiency of modulation period estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a schematic diagram of a modulation period estimation method and system provided by the present invention; Figure 2 It is a schematic diagram of a method for obtaining a first characteristic graph matrix of a modulation period estimation method and system provided by the present invention; Figure 3 It is a schematic diagram of a method for establishing a first estimation classification network model of a modulation period estimation method and system provided by the present invention; Figure 4 It is a schematic diagram of a method for obtaining a modulation period spectrum of a modulation period estimation method and system provided by the present invention; Figure 5 It is a schematic diagram of a method for establishing a second estimation classification network model of a modulation period estimation method and system provided by the present invention; Figure 6 It is a dynamic matrix partitioning schematic diagram of a modulation period estimation method and system provided by the present invention; Figure 7 It is a schematic diagram of a modulation period spectrum of a modulation period estimation method and system provided by the present invention; Figure 8 It is a schematic diagram of a characteristic diagram of the Hankel cumulative covariance matrix generated by signals of different modulation systems under different M values ​​provided by a modulation period estimation method and system of the present invention; Fig. 9 It is a schematic diagram of dynamic covariance matrix characteristics generated by different modulation system signals under different M values ​​of a modulation period estimation method and system provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings and embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0024] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method, step or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such method, step or system. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the method, step or system including the element.

[0025] like Figure 1 As shown, the present invention provides a modulation period estimation method and system, the method comprising: S100: receiving communication and radar signals to obtain a first feature map matrix; inputting the first feature map matrix into a constructed first estimation classification network model to obtain a first classification result; discriminating the first classification result and outputting a first discrimination result.

[0026] Specifically, if Figure 2 As shown, the first feature map matrix The gains can include: S101: Order ,set up Value and value; Indicates the length of continuous detection The number of segments of the point signal, Indicates the number of maximum singular values ​​selected.

[0027] S102: Forward communication and radar signals points are discarded to obtain the first signal. The point value constructs the dimension The Hankel matrix of ; where , .

[0028] As an example, the dimension is The Hankel matrix of is: .

[0029] S103: Perform singular value decomposition on the Hankel matrix, that is, , and then get the corresponding left singular matrix , singular value diagonal matrix , right singular matrix ; Select the singular value diagonal matrix Center front The largest singular values, and using the corresponding left singular matrix and the right singular matrix Reconstruct the Hankel matrix and obtain the first covariance matrix.

[0030] It should be noted that the reconstructed Hankel matrix is: ; The first covariance matrix is: .

[0031] S104: If , then let , and keep looping S102~S103 until , the corresponding The first covariance matrices are superimposed and averaged to obtain The Hankel cumulative covariance matrix in dimensionality.

[0032] It should be noted that the Hankel cumulative covariance matrix is: .

[0033] S105: Hankel cumulative covariance matrix Perform standardization to obtain the first feature map matrix .

[0034] It should be noted that standardization processing includes: ; ; .

[0035] make ,Will Input to the trained first estimation classification network model In the classification results .

[0036] like Figure 3 As shown, the first estimated classification network model The build includes: S111: Establish a signal sample library.

[0037] As a specific embodiment, the method for establishing a signal sample library includes: S1111: Order ; S1112: Set the category to ,make ; S1113: Set the signal-to-noise ratio to ,make .

[0038] S1114: respectively from A type of signal system is randomly selected from each type of signal system, and corresponding sample signal processing is performed respectively.

[0039] Since the characteristic signals extracted from different signal systems are different, in order to increase the diversity and comprehensiveness of the signal sample library, the present invention randomly selects one type of signal system from each type of signal system, performs corresponding sample signal processing respectively, and then establishes a signal sample library.

[0040] More specifically, one of the first signal systems is randomly selected and sample signal processing is performed; one of the second signal systems is randomly selected and sample signal processing is performed; and one of the third signal systems is randomly selected and sample signal processing is performed.

[0041] Among them, the first type of signal system includes: linear frequency modulation, linear frequency modulation pseudo code phase modulation composite, rectangular pulse shaping 2~8PSK, 2~8FSK, rectangular pulse shaping 8~32QAM, etc.; the second type of signal system includes: pulse Doppler, linear frequency modulation pulse train, sinusoidal frequency modulation pulse train, etc.; the third type of signal system includes: raised cosine pulse shaping 2~8PSK, raised cosine pulse shaping 8~32QAM, etc.

[0042] The sample signal processing includes: after selecting the signal system, randomly generating the modulation parameter value corresponding to the modulation period (including the modulation period N), and then generating the corresponding discrete time signal according to the modulation system and modulation parameters. , randomly insert multiple points of 0 value at the front end of the signal (the number of 0 value points is a random integer between 1 and N), and according to the signal-to-noise ratio Add noise to the sample signal collected; use the signal after adding noise to Indicates that Label it " " and then added to the signal sample library.

[0043] S1115: If ,make , go to S1116, otherwise go to S1114; for example, .

[0044] S1116: If ,make , go to S1117, otherwise go to S1113; for example, .

[0045] S1117: If , go to S1118, otherwise go to S1112; for example, .

[0046] S1118: This completes the establishment of the signal sample library. signal samples.

[0047] S112: Order ; Select the first Signal ;in, The label is , the modulation period is ; Redefine the receiving signal: , , .

[0048] For example, the signal sample library has signal samples, and take the first sample from the 51840 samples in the signal sample library. Signal .

[0049] S113: Select Value; if , then in the interval A random integer value is selected as Value; if , , then in the interval A random integer value is selected as Value; if , then in the interval A random integer value is selected as value; S114: Based on the received signal Get the first feature map matrix , and label it , added to the first feature map sample library, and let .

[0050] It should be noted that the first feature map matrix The method of obtaining is the same as above and will not be repeated here.

[0051] S115: Continuously loop S112 to S114 until all first feature map matrices corresponding to the signals in the signal sample library are obtained, thereby completing the establishment of the first feature map sample library.

[0052] That is to judge Is it greater than the number of signals in the signal sample library? More specifically, if , to S112, and then continue to cycle S112~S114; until , complete the construction of the first feature map sample library, the library contains samples.

[0053] S116: Obtaining a first estimated classification network model based on the first feature map sample library training .

[0054] For example, in the first feature map sample library generated in the above steps, each of the labels "0" to "11" contains 4320 samples. From the samples contained in each label, 2440 samples are randomly selected for training and 600 samples are used for verification, thereby constructing a training sample library with 2440*12=28800 samples and a test sample library with 600*12=7200 samples. Then, the 28800 samples in the training sample library are sent to the ResNet-101 convolutional neural network for training. The trained network is used It means that the network is able to classify the feature map samples fed into it, that is, output a value between "0" and "11".

[0055] As an embodiment, the first classification result is judged and the first judgment result is output, which includes: judging the first classification result Is it less than the judgment standard? , the first discrimination result is output as: ; in, is a step function; when When The first discrimination result outputted is 0, otherwise the first discrimination result outputted is 1.

[0056] S200: Continuously looping S100 until a preset condition is met, then searching for the index position of the last jump point in the first discrimination result in the preliminary search interval to obtain a preliminary estimated period.

[0057] It should be noted that if the preset conditions are met, it can be , that is, in step S100, let , set the initial search interval value. That is, exist The value is taken between , and the number of points taken each time is , Indicates the number of reference points in the initial search interval. For example, when Take 10, When the value is 11, the entire coverage range is 10 to 10240.

[0058] if , then let , and keep looping S100 until Only after the preset conditions are met will the index position of the last jump point in the first judgment result be searched within the preliminary search interval.

[0059] As an embodiment, obtaining the index position of the last transition point includes: Find the first judgment result The index positions of all the transition points from 0 to 1 in the , and put them all into a set in order middle: ; and find out The position of the last point in is : ; So we get the first judgment result The index position of the last 0 to 1 transition point in .

[0060] Calculate the corresponding value , get the initial estimated cycle : ; in, , Indicates the number of reference points in the initial search interval; Indicates the index position of the last jump point in the first judgment result; Indicates the judgment criteria.

[0061] S300: Based on the communication and radar signals, a modulation period spectrum is obtained; within the preliminary estimation period, a corresponding differential signal is calculated based on the modulation period spectrum; and a covariance matrix is ​​constructed according to the differential signal to obtain a second feature map matrix.

[0062] It should be noted that if Figure 4 As shown, the modulation period spectrum is obtained by: S301: Setting the row values ​​of the dynamic matrix and column values ,in, .

[0063] S302: Take the front Signal points , arrange the point values ​​in column order, and get OK A dynamic matrix of columns.

[0064] in, OK The dynamic matrix of columns is: .

[0065] At the same time, its covariance matrix can also be calculated: .

[0066] S303: Divide the dynamic matrix into two parts evenly to obtain the first part and Part II ; and based on the first part and Part II Correspondingly, two covariance matrices are obtained.

[0067] That is, a first covariance matrix is ​​obtained based on the first part, and a second covariance matrix is ​​obtained based on the second part.

[0068] Specifically, according to Value calculation and , and the matrix It is divided into two parts: and ,in, Depend on Before Row composition, Depend on After Then and Multiply them with their conjugate transposes, and get two corresponding covariance matrices, which are: ; .

[0069] S304: Calculate the sum of the modulus values ​​of each element in the two covariance matrices respectively, and take the larger value of the two.

[0070] That is, take the modulus of each element of the two covariance matrices to obtain a matrix composed of the modulus of each element and , find the sum of all elements of these two matrices and take the larger value of the two : .

[0071] S305: If , then let , and continue to cycle S302~S304 until , and the modulation period spectrum is obtained .

[0072] By cycling through the above steps, we get the modulated periodic spectrum. ,exist Within the range, calculate The differential signal , and find its maximum point : ; in, .

[0073] It should be noted that the second feature map matrix is ​​obtained by: Based on the multiple differential signals, multiple maximum points of the corresponding differential signals are obtained, and the indexes of the multiple maximum points are arranged in the order of the values ​​in the differential signals to obtain an index set. ; Among them, the index collection The results include: finding There are multiple large value points in the middle, and their indexes are pressed The values ​​in the are sorted into the collection in order of size middle: ; .

[0074] make ; Select the index collection The element value in ,use OK Dynamic matrix construction of columns The covariance matrix of dimension , and the covariance matrix is ​​standardized to obtain the second feature map matrix.

[0075] The standardization process includes: ; ; ; .

[0076] S400: The second feature map matrix Input to the constructed second estimation classification network model The second classification result is obtained ; Judge the second classification result and output the second judgment result.

[0077] As an example, Figure 5 As shown, the second estimated classification network model The build includes: S401: Establishing a signal sample library; The signal sample library can be established in the same manner as the signal sample library in establishing the first estimation and classification network model.

[0078] As a specific embodiment, the method for establishing a signal sample library includes: S4111: Order ; S4112: Set the category to ,make ; S4113: Set the signal-to-noise ratio to ,make .

[0079] S4114: respectively from A type of signal system is randomly selected from each type of signal system, and corresponding sample signal processing is performed respectively.

[0080] It should be noted that the signal system category and the corresponding sample signal processing are the same as the signal sample library in the establishment of the first estimation classification network model, and will not be repeated here.

[0081] S4115: If ,make , go to S2116, otherwise go to S2114; for example, .

[0082] S4116: If ,make , go to S2117, otherwise go to S2113; for example, .

[0083] S4117: If , go to S2118, otherwise go to S2112; for example, .

[0084] S4118: This completes the establishment of the signal sample library. signal samples.

[0085] S402: Order ; Select the first Signal ;in, The label is , the modulation period is ; Redefine the receiving signal: , , .

[0086] For example, the signal sample library has signal samples, and take the first sample from the 127008 samples in the signal sample library. Signal

[0087] S403: Select value; like , then let ,in, is a rational fraction less than 1; like , then let ,in, is a rational fraction greater than 1 and less than 5; like , then let ; like , then let ,in, for A random integer value within ; like , then let ,in, is a random integer value other than the four cases in S403.

[0088] S404: Take the front Signal points , arrange the point values ​​in column order, and get OK The dynamic matrix of the columns; based on the dynamic matrix, the covariance matrix is ​​obtained, and the covariance matrix is ​​standardized to obtain the second feature map matrix ; and label it , added to the second feature map sample library, and let .

[0089] in, OK Dynamic matrix of columns Can be: ; Covariance matrix Can be: ; Standardization can be: ; ; ; .

[0090] S405: Continuously looping S403 to S404 until all second feature map matrices corresponding to the signals in the signal sample library are obtained, thereby completing the establishment of the second feature map sample library.

[0091] More specifically, if , to S402, and then continue to cycle from S402 to S404; until , complete the construction of the second feature map sample library, the library contains samples.

[0092] S406: Obtaining a second estimated classification network model based on the second feature map sample library training .

[0093] For example, in the second feature map sample library generated by the above steps, each of the labels "-1" to "12" contains 9072 samples. From the samples contained in each label, 4800 samples are randomly selected for training and 1200 samples are randomly selected for verification, thereby constructing a training sample library with 4800*14=67200 samples and a test sample library with 1200*14=16800 samples. Then, the 67200 samples in the training sample library are sent to the ResNet-101 convolutional neural network for training. The trained network is used It means that the network is able to classify the feature map samples fed into it, that is, output a value between "-1" and "12".

[0094] S500: Obtain a modulation period according to the maximum value in the second discrimination result.

[0095] It should be noted that the second discrimination result may be: ; if , then let , reselect the index set The element value in , and keep looping to get the second judgment result; if , then directly calculate the second discrimination result The maximum value of: ; Then calculate the modulation period .

[0096] Furthermore, the modulation period The calculation method is: ; in, Indicates the second classification result; is a step function; Indicates the number of multiple maximum value points of the corresponding differential signal; Indicates the maximum value point of the differential signal; Indicates the maximum value of the second classification result.

[0097] In the process of obtaining the modulation period spectrum, when the modulation period value is obtained, the dynamic matrix obtained is After being divided into two parts by row, at least one of the two divided matrices has column data coming from the same modulation period.

[0098] like Figure 6 As shown, at this time, the sum of the modulus values ​​of all elements of the covariance matrix of the segmentation matrix will be a large value, that is, the obtained modulation period spectrum R[M] is A local maximum value will be obtained at the point, which can be used to preliminarily distinguish and Therefore, the matrix can play a role in preliminary screening of modulation cycles. It should be noted that Figure 7 As shown, in addition to the local maximum at the modulation period, R[M] may also take local maximum at integer and fractional multiples of the modulation period.

[0099] like Figure 8 As shown, for a periodically modulated signal, the constructed Hankel cumulative covariance matrix characteristic diagram There are two special structures. Figure 8(a) shows the Hankel cumulative covariance matrix feature map generated by 8FSK. Each sub-map corresponds to a different range of M values. Within this range, the shape of the feature map is roughly the same. It can be found that the larger the M, the thinner the bars on the main diagonal of the feature map. In theory, the thickness of the bars can be used to make a rough estimate and find the appropriate M value, for example, find an M value that is approximately equal to 4 times the modulation period. Figure 8 (a) Signals with characteristic pattern shapes also include: mfsk signal, rectangular pulse shaped mpsk signal, rectangular pulse shaped mqam signal, raised cosine pulse shaped mpsk signal, raised cosine pulse shaped mqam signal, etc. Figure 8 (b) shows the Hankel cumulative covariance matrix feature map generated by a linear frequency modulated continuous wave signal. Each sub-map corresponds to a different range of M values. It can be found that the larger the M, the more straight lines there are in the diagonal direction of the feature map. In theory, a rough estimate can be made through the number of straight lines to find the appropriate M value. Figure 8 (b) Signals with characteristic pattern shapes also include: linear frequency modulation pulse train signals, sinusoidal frequency modulation pulse train signals, pulse Doppler signals, etc.

[0100] Depend on Figure 8 It can be seen that based on the special structural characteristics of the Hankel cumulative covariance matrix feature map, a deep neural network can be introduced to learn the Hankel cumulative covariance matrix feature map under a large number of different M values, adaptively generate the optimal classifier, and judge whether the M value corresponding to the current analysis dimension is greater than a certain integer multiple of the modulation period to determine a containing A search interval that is an integer multiple of at least one modulation period is used to improve the efficiency of accurate estimation of subsequent modulation periods.

[0101] like Fig. 9 As shown, the dynamic covariance matrix characteristic diagram of different modulation system signals when M takes different values . When M=pN (p is an integer greater than or equal to 1), there must be multiple independent blocks (square or cone) on the diagonal in its feature map, and as the value of p increases, the number of blocks also increases; in addition, if M=pN (where p is a rational fraction), when p>1, as p increases, the shape of the signal feature map of various systems will gradually become a stick on the main diagonal, and the larger p is, the thinner the stick is. In each system, the feature map when p<1 or p>1 also has a certain degree of distinction.

[0102] In short, when M takes different integer multiples of the actual modulation period, the feature map has a great degree of differentiation, and the most obvious is that the number of blocks in the obtained image is inconsistent. In addition to the local maximum value appearing at the modulation period, the R[M] proposed above may also take local maximum values ​​at integer and fractional multiples of the modulation period. Therefore, this feature can be combined with R[M], and multiple local extreme points can be found through the R[M] differential waveform. Then, the feature maps of the M value dimension corresponding to these local extreme points are classified to find the one corresponding to the actual modulation period (i.e., M=N), so as to distinguish the modulation period from its integer (or fractional) multiples and find the modulation period N. In order to complete effective classification, a deep neural network can be introduced to adaptively generate the optimal classifier by learning a large number of feature map sample data with different M value dimensions under different signal-to-noise ratio conditions.

[0103] On the other hand, the present invention also provides a modulation period estimation system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods. The technical solution of the system is consistent with the technical solution of the above method and will not be repeated here.

[0104] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0105] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the above-described embodiments are only schematic, and the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0110] Those skilled in the art will appreciate that all or part of the various circuits in the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0111] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0112] The technical features of the above embodiments may 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.

[0113] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A modulation period estimation method, characterized in that: The method comprises: S100: receiving communication and radar signals to obtain a first feature map matrix; inputting the first feature map matrix into a constructed first estimation classification network model to obtain a first classification result; discriminating the first classification result and outputting a first discrimination result; S200: Continuously looping S100 until a preset condition is met, then searching for the index position of the last jump point in the first discrimination result in the preliminary search interval to obtain a preliminary estimated period; S300: Based on the communication and radar signals, a modulation period spectrum is obtained; within the preliminary estimation period, a corresponding differential signal is calculated based on the modulation period spectrum; and a covariance matrix is ​​constructed according to the differential signal to obtain a second feature map matrix; S400: inputting the second feature map matrix into the constructed second estimation classification network model to obtain a second classification result; performing discrimination on the second classification result and outputting a second discrimination result; S500: Obtain a modulation period according to the maximum value of the second discrimination result.

2. A modulation period estimation method according to claim 1, characterized in that: The obtaining of the first feature map matrix includes: S101: Order ,set up Value and value; Indicates the length of continuous detection The number of segments of the point signal, Indicates the number of maximum singular values ​​selected; S102: The communication and radar signal points are discarded to obtain a first signal, based on the previous The point value constructs the dimension The Hankel matrix of S103: Perform singular value decomposition on the Hankel matrix to obtain a corresponding left singular matrix, a singular value diagonal matrix, and a right singular matrix; select the first singular value matrix in the singular value diagonal matrix. maximum singular values, and reconstruct the Hankel matrix using the corresponding left singular matrix and the right singular matrix to obtain a first covariance matrix; S104: If , then let , and keep looping S102~S103 until , the corresponding The first covariance matrices are superimposed and averaged to obtain Hankel cumulative covariance matrix in dimensions; S105: Standardize the Hankel cumulative covariance matrix to obtain the first feature map matrix.

3. A modulation period estimation method according to claim 1, characterized in that: The first estimated classification network model The build includes: S111: Establishing a signal sample library; S112: Order ; Select the first Signal ;in, The label is , the modulation period is ; Redefine the received signal: , , ; S113: Select Value; if , then in the interval A random integer value is selected as Value; if , , then in the interval A random integer value is selected as Value; if , then in the interval A random integer value is selected as value; S114: Based on the received signal Get the first feature map matrix , and label it , added to the first feature map sample library, and let ; S115: Continuously looping S112 to S114 until all first feature map matrices corresponding to the signals in the signal sample library are obtained, thereby completing the establishment of the first feature map sample library; S116: Obtaining the first estimation classification network model based on the first feature map sample library training .

4. A modulation period estimation method according to claim 1, characterized in that: The first classification result is judged and the first judgment result is outputted, comprising: Determine the first classification result Is it less than the judgment standard? , the first discrimination result is output as: ;in, is a step function; when When The first discrimination result outputted is 0, otherwise the first discrimination result outputted is 1.

5. A modulation period estimation method according to claim 1, characterized in that: The initial estimation period The calculation method is: ； in, , Representing the number of reference points of the preliminary search interval; Indicates the index position of the last jump point in the first discrimination result; Indicates the judgment criteria.

6. A modulation period estimation method according to claim 1, characterized in that: The obtaining of the modulation period spectrum comprises: S301: Setting the row values ​​of the dynamic matrix and column values ,in, ; S302: Take the front Signal points , arrange the point values ​​in column order, and get OK Dynamic matrix of columns; S303: Divide the dynamic matrix into two parts evenly to obtain the first part and Part II ; and based on the first part and the second part Correspondingly, two covariance matrices are obtained; S304: respectively calculating the sum of the modulus values ​​of each element in the two covariance matrices, and taking the larger value of the two; S305: If , then let , and keep looping S302~S304 until , and obtain the modulation periodic spectrum.

7. A modulation period estimation method according to claim 1, characterized in that: The second feature map matrix is ​​obtained by: Based on the multiple differential signals, multiple maximum points of the corresponding differential signals are obtained, and the indexes of the multiple maximum points are arranged in the order of the values ​​in the differential signals to obtain an index set. ; Select the index collection The element value in ,use OK Dynamic matrix construction of columns dimensional covariance matrix, and standardize the covariance matrix to obtain the second feature map matrix.

8. A modulation period estimation method according to claim 1, characterized in that: The second estimated classification network model The build includes: S401: Establishing a signal sample library; S402: Order ; Select the first Signal ;in, The label is , the modulation period is ; Redefine the received signal: , , ; S403: Select Value; if , then let ,in, is a rational fraction less than 1; if , then let ,in, is a rational fraction greater than 1 and less than 5; if , then let ;like , then let ,in, for A random integer value within ; if , then let ,in, is any other random integer value except the four cases in S403; S404: Take the front Signal points , arrange the point values ​​in column order, and get OK A dynamic matrix of columns; based on the dynamic matrix, a covariance matrix is ​​obtained, and the covariance matrix is ​​standardized to obtain a second feature map matrix ; and label it , added to the second feature map sample library, and let ; S405: Continuously looping S403 to S404 until all second feature map matrices corresponding to the signals in the signal sample library are obtained, thereby completing the establishment of the second feature map sample library; S406: Obtaining the second estimation classification network model based on the second feature map sample library training .

9. A modulation period estimation method according to claim 1, characterized in that: The modulation period The calculation method is: ； in, represents the second classification result; is a step function; Indicates the number of multiple maximum value points of the corresponding differential signal; Indicates the maximum value point of the differential signal; Indicates the maximum value of the second classification result.

10. A modulation period estimation system, comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes a computer program to implement the steps of the method according to any one of claims 1 to 9.

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