Waveform complexity measurement method, device and equipment based on waveform feature vector

Abstract

The invention provides a waveform complexity measurement method, device and equipment based on a waveform feature vector. According to the waveform complexity measurement method based on the waveform eigenvectors, the waveform eigenvector matrix group is constructed and observed by using the multi-dimensional waveform eigenvectors, and the complexity analysis is performed on the basis, so that the limitation of the traditional one-dimensional sequence analysis is broken through by the data modeling mode, and the situation that multivariable signals are split for analysis is avoided; therefore, the integrity of interdependence between waveform signals is kept; in addition, a fuzzy membership function is adopted to calculate the similarity degree between waveform matrixes, and the robustness and accuracy of the system during noise and uncertain signal processing are effectively improved; according to the method, a complexity analysis task is divided into a plurality of execution modules, and a step-by-step processing calculation process is adopted, so that the calculation burden and error accumulation in the waveform data processing process are reduced, and the analysis efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a waveform complexity measurement method, device and equipment based on waveform feature vectors. Background Art

[0002] With the rapid development of modern communication technology, wireless communication, satellite communication, shortwave communication and other systems are becoming increasingly complex, and signal forms and data structures are showing a trend of diversification. In these systems, signal transmission is not only affected by noise and interference, but also involves the interaction of multiple signal dimensions, such as multiple-input multiple-output (MIMO) systems, cognitive radio networks, and multi-band satellite communication systems. Therefore, accurate measurement of the complexity of communication waveforms is of great significance for analyzing system behavior, optimizing information transmission characteristics, and improving overall communication performance. However, traditional complexity analysis methods are mostly based on one-dimensional sequences, which makes it difficult to fully capture the complex correlations between multivariate signals, resulting in limited accuracy and reliability of analysis results in multidimensional signal environments.

[0003] In order to cope with the limitations of one-dimensional complexity analysis methods, a variety of algorithms have been applied to the complexity analysis of time series, such as approximate entropy algorithm, sample entropy algorithm and fuzzy entropy algorithm. These algorithms have shown certain advantages in the complexity evaluation of univariate time series, and can quantify the frequency of new patterns in the signal and enhance the robustness to noise. However, these algorithms are mainly designed to process one-dimensional scalar sequences. When faced with multidimensional signals, they usually need to be split into multiple single-dimensional signals for analysis one by one, which not only increases the computational complexity, but also may cause key information to be lost during the splitting process, thus failing to accurately reflect the complex relationship between multivariate signals.

[0004] Although existing one-dimensional complexity analysis methods have certain application value in univariate time series analysis, their limitations are increasingly prominent when dealing with multidimensional signals in modern communication systems. First, these methods lack support for multidimensional data and cannot fully capture the coupling relationship and interaction pattern between multi-channel or multi-antenna signals. Secondly, the lack of multivariate analysis capabilities makes it difficult to accurately evaluate the interdependence between signals in complex communication scenarios. In addition, with the rapid growth of the scale of communication data, existing algorithms face severe challenges in computational efficiency and scalability, and it is difficult to meet the needs of real-time processing and dynamic optimization. Therefore, there is an urgent need for a new method that can perform complexity analysis on multidimensional signals to overcome the shortcomings of existing technologies and improve the signal analysis capabilities and overall performance of communication systems. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a waveform complexity measurement method, device and equipment based on waveform feature vectors.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a waveform complexity measurement method based on a waveform feature vector, comprising:

[0008] S101, obtaining a multi-dimensional waveform feature vector of a signal to be analyzed, and constructing a waveform feature sample space;

[0009] S102, constructing an observed waveform feature vector matrix group according to the waveform feature sample space and the preset embedding dimension;

[0010] S103, based on the observed waveform feature vector matrix group, the fuzzy membership function and the preset similarity tolerance, determine the similarity between the current waveform matrix and the remaining waveform matrices, and calculate the average value to obtain a current similarity result; the remaining waveform matrix is ​​all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix;

[0011] S104, obtaining a first similarity degree through current similarity results corresponding to all current waveform matrices;

[0012] S105, expanding the preset embedding dimension to obtain an embedding dimension expansion value, and re-executing S102-S104 using the embedding dimension expansion value as the preset embedding dimension to obtain a second similarity degree;

[0013] S106. Calculate the waveform complexity fuzzy entropy using the first similarity degree and the second similarity degree to obtain a waveform complexity measurement result.

[0014] Optionally, S103 specifically includes:

[0015] Calculate the maximum distance between waveform matrices in the observed waveform feature vector matrix group;

[0016] Based on the fuzzy membership function, the preset similarity tolerance and the maximum distance, the similarity between the current waveform matrix and each remaining waveform matrix is ​​determined, and the average is calculated to obtain the current similarity result.

[0017] Optionally, calculating the maximum distance between waveform matrices in the observed waveform feature vector matrix group includes:

[0018] The Euclidean distance is used to calculate the maximum distance between waveform matrices in the observed waveform feature vector matrix group.

[0019] Optionally, the similarity between the current waveform matrix and the remaining waveform matrices is calculated based on the following formula:

[0020]

[0021] It represents the similarity between the current waveform matrix A(i) and the remaining waveform matrix A(j) under the preset embedding dimension m, d[A(i),A(j)] represents the maximum distance between A(i) and A(j), r represents the preset similarity tolerance, and exp represents the exponential function.

[0022] Optionally, the signal to be analyzed includes: any one of a frequency hopping waveform, a constellation scrambled waveform and a constellation scrambled frequency hopping waveform, or a combined waveform consisting of a frequency hopping waveform, a constellation scrambled waveform and a constellation scrambled frequency hopping waveform.

[0023] Optionally, S102 specifically includes:

[0024] Based on the sliding window method and with the preset embedding dimension as the window size, the waveform feature sample space is divided to obtain a matrix group of observed waveform feature vectors.

[0025] Optionally, the preset embedding dimension m is 2, and the preset similarity tolerance r is 0.5.

[0026] Optionally, the waveform complexity fuzzy entropy is expressed as:

[0027] FuzzyEn(m,r,N)=lnΦ m,N (r)-lnΦ m+1,N (r);

[0028] Where FuzzyEn(m,r,N) represents the waveform complexity fuzzy entropy corresponding to the total number of waveform feature sample space N under the preset embedding dimension m and the preset similarity tolerance r, Φ m,N (r) represents the first similarity, Φ m+1,N (r) indicates the second similarity degree.

[0029] In a second aspect, the present invention provides a waveform complexity measurement device based on waveform feature vectors, the waveform complexity measurement device based on waveform feature vectors comprises: an acquisition unit, a construction unit, a calculation unit and a loop processing unit;

[0030] The acquisition unit is used to: acquire a multi-dimensional waveform feature vector of the signal to be analyzed and construct a waveform feature sample space;

[0031] The construction unit is used to: construct an observed waveform feature vector matrix group according to the waveform feature sample space and the preset embedding dimension;

[0032] The calculation unit is used to: determine the similarity between the current waveform matrix and the remaining waveform matrices based on the observed waveform feature vector matrix group, the fuzzy membership function and the preset similarity tolerance, and calculate the average value to obtain the current similarity result; the remaining waveform matrix is ​​all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix;

[0033] The construction unit is also used to: obtain a first similarity degree through current similarity results corresponding to all current waveform matrices;

[0034] The loop processing unit is used to: expand the preset embedding dimension to obtain an embedding dimension expansion value, and re-execute the actions of the construction unit and the calculation unit using the embedding dimension expansion value as the preset embedding dimension to obtain a second similarity degree;

[0035] The calculation unit is also used to: calculate the waveform complexity fuzzy entropy using the first similarity degree and the second similarity degree to obtain a waveform complexity measurement result.

[0036] In the third aspect, the present invention provides a waveform complexity measurement device based on waveform feature vectors, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, when the waveform complexity measurement device based on waveform feature vectors is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the waveform complexity measurement method based on waveform feature vectors as described in the first aspect above.

[0037] The present invention provides a waveform complexity measurement method, device and equipment based on waveform feature vectors. Among them, a waveform complexity measurement method based on waveform feature vectors includes: S101, obtaining a multidimensional waveform feature vector of a signal to be analyzed, and constructing a waveform feature sample space; S102, constructing an observed waveform feature vector matrix group according to the waveform feature sample space and a preset embedding dimension; S103, determining the similarity between the current waveform matrix and the remaining waveform matrices based on the observed waveform feature vector matrix group, a fuzzy membership function and a preset similarity tolerance, and averaging to obtain a current similarity result; the remaining waveform matrix is ​​all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix; S104, obtaining a first similarity degree through the current similarity results corresponding to all current waveform matrices; S105, extending the preset embedding dimension to obtain an embedded dimension extension value, and re-executing S102-S104 using the embedded dimension extension value as the preset embedding dimension to obtain a second similarity degree; S106, calculating the waveform complexity fuzzy entropy using the first similarity degree and the second similarity degree to obtain a waveform complexity measurement result. In the present invention, a matrix group of observed waveform feature vectors is constructed by using multidimensional waveform feature vectors, and complexity analysis is performed on this basis. This data modeling method breaks the limitations of traditional one-dimensional sequence analysis and avoids splitting multivariate signals for analysis, thereby maintaining the integrity of the interdependence between waveform signals. In addition, a fuzzy membership function is used to calculate the similarity between waveform matrices, which effectively improves the robustness and accuracy of the system when processing noise and uncertain signals. The present invention decomposes the complexity analysis task into multiple execution modules and adopts a step-by-step calculation process to reduce the computational burden and error accumulation in the waveform data processing process, thereby improving the analysis efficiency and accuracy.

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a flow chart of a waveform complexity measurement method based on waveform feature vectors provided in an embodiment of the present invention;

[0040] Figure 2 The waveform variation curves at different observation lengths based on the method of the present invention are shown by way of example;

[0041] Figure 3 The waveform variation curves at different observation lengths based on the fuzzy entropy detection method are shown by way of example;

[0042] Figure 4 A schematic diagram of the structure of a waveform complexity measurement device based on waveform feature vectors provided by an embodiment of the present invention;

[0043] Figure 5 A schematic structural diagram of a waveform complexity measurement device based on waveform feature vectors provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention provides a waveform complexity measurement method based on waveform feature vectors, which extends the measurement of complexity to a multi-dimensional waveform feature vector sequence, uses a fuzzy entropy algorithm with excellent performance to analyze the waveform complexity, and realizes complexity measurement for a multi-dimensional waveform feature vector sequence based on the fuzzy entropy algorithm.

[0045] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0046] In order to reduce the computational burden and error accumulation in the waveform data processing process and improve the efficiency and accuracy of waveform data analysis, an embodiment of the present invention provides a waveform complexity measurement method based on waveform feature vectors. Figure 1 The present invention provides a flow chart of a waveform complexity measurement method based on waveform feature vectors. Figure 1 As shown, including:

[0047] S101. Obtain a multi-dimensional waveform feature vector of a signal to be analyzed and construct a waveform feature sample space.

[0048] Optionally, the signal to be analyzed includes: any one of a frequency hopping waveform, a constellation scrambled waveform, and a constellation scrambled frequency hopping waveform, or a combined waveform consisting of a frequency hopping waveform, a constellation scrambled waveform, and a constellation scrambled frequency hopping waveform. The frequency hopping waveform performs frequency hopping on 16 frequency slots, and the frequency hopping rate is switched 200 times per second; the constellation scrambled waveform uses 12 modulation modes, and the modulation mode is switched 200 times per second; the constellation scrambled frequency hopping waveform switches 200 times per second on 16 frequency slots, and the modulation mode is switched 200 times per second, and the same 12 modulation modes are also used. The 12 modulation modes correspond to: BPSK (binary phase shift keying), QPSK (quadrature phase shift keying), 8PSK (eighth phase shift keying), 16QAM (hexadecimal quadrature amplitude modulation), 32QAM (32-digit quadrature amplitude modulation), 64QAM (64-digit quadrature amplitude modulation), 128QAM (128-digit quadrature amplitude modulation), 256QAM (256-digit quadrature amplitude modulation), FSK (frequency shift keying), GMSK (Gaussian minimum shift keying), OFDM (orthogonal frequency division multiplexing) and DPSK (differential phase shift keying).

[0049] In this embodiment, the multi-dimensional waveform feature vector may be composed of waveform feature vectors of D dimensions, such as frequency, modulation, and polarization.

[0050] The t-th waveform feature sample space v(t) can be expressed as:

[0051] v(t)=[u1(t),u2(t),…,u D (t)], t∈(1,N);

[0052] Among them, u D (t) represents the waveform feature vector of the Dth dimension corresponding to v(t), N represents the total number of waveform feature sample spaces, and all v(t) can constitute the waveform feature sample space {v(t)|t=1,2,…,N}.

[0053] It should be further explained that the waveform feature sample space contains multiple waveform feature samples, and v(t) is specifically the tth sample in the waveform feature sample space.

[0054] It is understandable that in the embodiments of the present invention, through multi-dimensional waveform feature vector modeling and modular calculation, the limitation of information loss in traditional one-dimensional analysis is avoided, and comprehensive capture and accurate analysis of multivariate signals are achieved. This design effectively improves the analysis accuracy of the system and provides strong support for the performance optimization of complex communication systems.

[0055] S102: construct an observed waveform feature vector matrix group according to the waveform feature sample space and the preset embedding dimension.

[0056] Optionally, S102 specifically includes:

[0057] Based on the sliding window method and with the preset embedding dimension as the window size, the waveform feature sample space is divided to obtain a matrix group of observed waveform feature vectors.

[0058] In this embodiment, the observed waveform feature vector matrix group can be expressed as:

[0059] A(1),A(2),...,A(i),...,A(N-m+1);

[0060] Among them, A(i)=[v(i),v(i+1),...,v(i+m-1)]-v0(i), i represents the serial number of the waveform matrix in the observed waveform feature vector matrix group, v0(i) represents the arithmetic mean of v(i), v(i+1), ..., v(i+m-1)], and m represents the preset embedding dimension.

[0061] S103, determining the similarity between the current waveform matrix and the remaining waveform matrices based on the observed waveform feature vector matrix group, the fuzzy membership function and the preset similarity tolerance, and calculating the average value to obtain the current similarity result.

[0062] Optionally, S103 may specifically include:

[0063] Calculate the maximum distance between waveform matrices in the observed waveform feature vector matrix group;

[0064] Based on the fuzzy membership function, the preset similarity tolerance and the maximum distance, the similarity between the current waveform matrix and each remaining waveform matrix is ​​determined, and the average is calculated to obtain the current similarity result.

[0065] Optionally, calculating the maximum distance between waveform matrices in the observed waveform feature vector matrix group includes:

[0066] The Euclidean distance is used to calculate the maximum distance between waveform matrices in the observed waveform feature vector matrix group.

[0067] Optionally, the similarity between the current waveform matrix and the remaining waveform matrices is calculated based on the following formula:

[0068]

[0069] It represents the similarity between the current waveform matrix A(i) and the remaining waveform matrix A(j) under the preset embedding dimension m, d[A(i),A(j)] represents the maximum distance between A(i) and A(j), r represents the preset similarity tolerance, and exp represents the exponential function.

[0070] in,

[0071] Current similarity result of A(i) and the remaining waveform matrix A(j) It can be expressed as:

[0072]

[0073] The first similarity degree Φ can be obtained by averaging the current similarity results corresponding to all current waveform matrices. m,N (r) can be expressed as:

[0074]

[0075] The second similarity can be expressed as:

[0076]

[0077] In this embodiment, the fuzzy membership function converts the similarity of the waveforms at different moments into a membership value between 0 and 1. The closer the membership value is to 1, the higher the similarity of the waveforms at the two groups of moments; the closer the membership value is to 0, the lower the similarity of the waveforms at the two groups of moments.

[0078] Optionally, the preset embedding dimension m is 2, and the preset similarity tolerance r is 0.5.

[0079] Among them, the embedding dimension m determines the local complexity of the signal, and can also be regarded as the size of each window in the fuzzy entropy calculation, that is, how many signal sampling points are needed to construct a valid feature vector. By increasing the value of m, the complexity and amount of information of the signal also increase. The similarity tolerance r is used to measure the similarity of the signal. This value can usually be determined experimentally, and it directly affects the accuracy of the similarity judgment in the subsequent steps. The applicant finally determined through a large number of experiments that using m as 2 and r as 0.5 as the final value can effectively improve the accuracy and robustness of signal processing.

[0080] The remaining waveform matrices are all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix;

[0081] S104. Obtain a first similarity level through current similarity results corresponding to all current waveform matrices.

[0082] S105. Expand the preset embedding dimension to obtain an embedding dimension expansion value, and re-execute S102-S104 using the embedding dimension expansion value as the preset embedding dimension to obtain a second similarity degree.

[0083] S106. Calculate the waveform complexity fuzzy entropy using the first similarity degree and the second similarity degree to obtain a waveform complexity measurement result.

[0084] Optionally, the waveform complexity fuzzy entropy is expressed as:

[0085] FuzzyEn(m,r,N)=lnΦ m,N (r)-lnΦ m+1,N (r);

[0086] Where FuzzyEn(m,r,N) represents the waveform complexity fuzzy entropy corresponding to the total number of waveform feature sample space N under the preset embedding dimension m and the preset similarity tolerance r, Φ m,N (r) represents the first similarity, Φ m+1,N (r) indicates the second similarity degree.

[0087] It should be noted that the waveform complexity measurement method based on waveform feature vectors provided by the present invention can be widely applied to communication application scenarios, including but not limited to MIMO systems and multi-antenna communications, and can also be applied to complex network scenarios such as cognitive radio, satellite communications and the Internet of Things. Its general algorithm structure enables it to flexibly adapt to the needs of various communication environments and provides an efficient tool for performance analysis and optimization of various systems.

[0088] An embodiment of the present invention provides a waveform complexity measurement method based on waveform feature vectors. By using multidimensional waveform feature vectors to construct an observed waveform feature vector matrix group, and performing complexity analysis on this basis, this data modeling method breaks the limitations of traditional one-dimensional sequence analysis and avoids splitting multivariate signals for analysis, thereby maintaining the integrity of the interdependence between waveform signals. In addition, a fuzzy membership function is used to calculate the similarity between waveform matrices, which effectively improves the robustness and accuracy of the system when processing noise and uncertain signals. The embodiment of the present invention decomposes the complexity analysis task into multiple execution modules and adopts a step-by-step calculation process to reduce the calculation burden and error accumulation in the waveform data processing process and improve the analysis efficiency and accuracy.

[0089] In order to verify the effectiveness of a waveform complexity measurement method based on waveform feature vectors provided by an embodiment of the present invention, a simulation verification is also performed in the embodiment of the present invention.

[0090] Specifically, Figure 2 The waveform variation curves of the method of the present invention at different observation lengths are shown as an example. In order to reflect the superiority of the method of the present invention, the traditional fuzzy entropy detection method is used as a comparison. The results are shown in FIG. Figure 3 As shown. Among them, Figure 2 and Figure 3 The horizontal axis represents the observation length, and the vertical axis represents the value of fuzzy entropy (waveform complexity fuzzy entropy).

[0091] from Figure 2 It can be seen from the simulation results that the method of the present invention can distinguish the complexity of traditional single-dimensional agile methods such as frequency hopping and constellation disturbance from that of multidimensional agile waveforms, and the fuzzy entropy value of the frequency hopping waveform with constellation disturbance is the highest, which indicates that the waveform has the highest complexity, which is consistent with the waveform parameter setting. The waveform complexity measurement method based on waveform feature vector of the present invention innovatively adopts waveform tensor, which can detect multidimensional waveform parameters simultaneously, and can obviously distinguish the complexity of multidimensional complex waveforms. Therefore, the method of the present invention can be used for complexity measurement of complex waveforms.

[0092] from Figure 3 It can be seen from the simulation results that the complexity of the frequency hopping waveform is close. Therefore, the traditional fuzzy entropy measurement method cannot distinguish the complexity of the frequency hopping waveform and the frequency hopping waveform with constellation disturbance. Moreover, the complexity of the constellation disturbance waveform is 0, which is obviously inconsistent with the facts. This is because the traditional fuzzy entropy detection method only focuses on the detection of the parameters of the frequency hopping sequence in one dimension and cannot detect the parameters of the constellation disturbance waveform at the same time. Therefore, the waveform with constellation disturbance has 0 complexity. In addition, the traditional fuzzy entropy detection method is also difficult to distinguish multi-dimensional complex waveforms using multiple modulation methods.

[0093] Depend on Figure 2 and Figure 3 The comparison clearly demonstrates the innovation and practicability of the method of the present invention in multi-dimensional complexity measurement technology, and reflects the application value of the method of the present invention in complex communication systems.

[0094] In summary, the waveform complexity measurement method based on waveform feature vector provided by the present invention mainly solves the problems of insufficient precision, information loss, low computational efficiency and poor scalability faced by traditional complexity analysis technology in high-dimensional data processing. First, the present invention expands the single-dimensional signal into a multi-dimensional waveform feature vector matrix to fully capture the complex correlation between different variables, avoiding the loss of key information caused by the simplification of the signal in the traditional method. This design is particularly suitable for complex communication scenarios such as multi-channel and multi-antenna systems, which helps to more accurately analyze system performance and dynamic characteristics. Secondly, the present invention introduces a fuzzy membership function to enhance the robustness of complexity analysis. By calculating the similarity between samples, the method can effectively deal with uncertainty and noise in the signal, thereby improving the accuracy of the analysis results. This design improves the adaptability of the system in a noisy environment, making it suitable for signal processing in changeable environments such as satellite communications and the Internet of Things. In addition, the present invention adopts a modular, step-by-step calculation strategy to gradually improve the stability and consistency of the calculation through iterative optimization. The method decomposes the complexity calculation task into multiple steps, reduces the cumulative error in large-scale data processing, and ensures that high-precision analysis results can still be obtained in a big data environment. These advantages enable the method of the present invention to not only meet the real-time signal processing requirements in communication systems, but also to quickly perform analysis and optimization in complex scenarios such as MIMO systems and cognitive radio.

[0095] The method provided in the embodiment of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc., which is not limited in the embodiment of the present invention.

[0096] Based on the same inventive concept, an embodiment of the present invention further provides a waveform complexity measurement device based on waveform feature vectors. Figure 4 The present invention provides a schematic diagram of a waveform complexity measurement device based on waveform feature vectors. Figure 4 As shown, including:

[0097] An acquisition unit 401, a construction unit 402, a calculation unit 403 and a loop processing unit 404;

[0098] The acquisition unit 401 is used to: acquire a multi-dimensional waveform feature vector of a signal to be analyzed and construct a waveform feature sample space;

[0099] The construction unit 402 is used to: construct an observed waveform feature vector matrix group according to the waveform feature sample space and the preset embedding dimension;

[0100] The calculation unit 403 is used to determine the similarity between the current waveform matrix and the remaining waveform matrices based on the observed waveform feature vector matrix group, the fuzzy membership function and the preset similarity tolerance, and calculate the average value to obtain the current similarity result; the remaining waveform matrix is ​​all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix;

[0101] The construction unit 402 is further used to: obtain a first similarity level through current similarity results corresponding to all current waveform matrices;

[0102] The loop processing unit 404 is used to: expand the preset embedding dimension to obtain an embedding dimension expansion value, and re-execute the actions of the construction unit 402 and the calculation unit 403 using the embedding dimension expansion value as the preset embedding dimension to obtain a second similarity degree;

[0103] The calculation unit 403 is further used to calculate the waveform complexity fuzzy entropy using the first similarity degree and the second similarity degree to obtain a waveform complexity measurement result.

[0104] Figure 5 A schematic diagram of the structure of a waveform complexity measurement device based on a waveform feature vector provided by an embodiment of the present invention includes: a processor 510, a storage medium 520 and a bus 530, wherein the storage medium 520 stores machine-readable instructions executable by the processor 510, and when the waveform complexity measurement device based on the waveform feature vector is running, the processor 510 communicates with the storage medium 520 via the bus 530, and the processor 510 executes the machine-readable instructions to execute the steps of the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0105] The storage medium may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage. Optionally, the storage medium may also be at least one storage device located away from the aforementioned processor.

[0106] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0107] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0108] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0109] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes of the above disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and the meaning of "multiple" is two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0110] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A waveform complexity measurement method based on waveform feature vector, characterized in that: include: S101, obtaining a multi-dimensional waveform feature vector of a signal to be analyzed, and constructing a waveform feature sample space; S102, constructing an observed waveform feature vector matrix group according to the waveform feature sample space and the preset embedding dimension; S103, determining the similarity between the current waveform matrix and the remaining waveform matrices based on the observed waveform feature vector matrix group, the fuzzy membership function and the preset similarity tolerance, and calculating the average value to obtain a current similarity result; The remaining waveform matrices are all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix; S104, obtaining a first similarity degree through the current similarity results corresponding to all current waveform matrices; S105, expanding the preset embedding dimension to obtain an embedding dimension expansion value, and re-executing S102-S104 using the embedding dimension expansion value as the preset embedding dimension to obtain a second similarity degree; S106: Calculate waveform complexity fuzzy entropy using the first similarity level and the second similarity level to obtain a waveform complexity measurement result.

2. The waveform complexity measurement method based on waveform feature vector according to claim 1 is characterized in that: The S103 specifically includes: Calculating the maximum distance between waveform matrices in the observed waveform feature vector matrix group; Based on the fuzzy membership function, the preset similarity tolerance and the maximum distance, the similarity between the current waveform matrix and each of the remaining waveform matrices is determined, and the average is calculated to obtain the current similarity result.

3. The waveform complexity measurement method based on waveform feature vector according to claim 2 is characterized in that: The calculating the maximum distance between waveform matrices in the observed waveform feature vector matrix group comprises: The maximum distance between waveform matrices in the observed waveform feature vector matrix group is calculated using the Euclidean distance.

4. The waveform complexity measurement method based on waveform feature vector according to claim 2 is characterized in that: The similarity between the current waveform matrix and the remaining waveform matrices is calculated based on the following formula: represents the similarity between the current waveform matrix A(i) and the remaining waveform matrix A(j) under the preset embedding dimension m, d[A(i),A(j)] represents the maximum distance between A(i) and A(j), r represents the preset similarity tolerance, and exp represents an exponential function.

5. The waveform complexity measurement method based on waveform feature vector according to claim 1 is characterized in that: The signal to be analyzed includes: any one of a frequency hopping waveform, a constellation scrambled waveform and a constellation scrambled frequency hopping waveform, or a combined waveform consisting of the frequency hopping waveform, the constellation scrambled waveform and the constellation scrambled frequency hopping waveform.

6. The waveform complexity measurement method based on waveform feature vector according to claim 1 is characterized in that: S102 specifically includes: Based on a sliding window approach and taking the preset embedding dimension as the window size, the waveform feature sample space is segmented to obtain the observed waveform feature vector matrix group.

7. The waveform complexity measurement method based on waveform feature vector according to claim 1 is characterized in that: The preset embedding dimension m is 2, and the preset similarity tolerance r is 0.

5.

8. The waveform complexity measurement method based on waveform feature vector according to claim 1 is characterized in that: The waveform complexity fuzzy entropy is expressed as: FuzzyEn(m,r,N)=lnΦ m,N (r)-lnΦ m+1,N (r); Where FuzzyEn(m,r,N) represents the waveform complexity fuzzy entropy corresponding to the total number of waveform feature sample space N under the preset embedding dimension m and the preset similarity tolerance r, Φ m,N (r) represents the first similarity, Φ m+1,N (r) represents the second similarity level.

9. A waveform complexity measurement device based on waveform feature vector, characterized in that: The waveform complexity measurement device based on waveform feature vector includes: an acquisition unit, a construction unit, a calculation unit and a loop processing unit; The acquisition unit is used to: acquire a multi-dimensional waveform feature vector of the signal to be analyzed and construct a waveform feature sample space; The construction unit is used to: construct an observation waveform feature vector matrix group according to the waveform feature sample space and the preset embedding dimension; The calculation unit is used to: determine the similarity between the current waveform matrix and the remaining waveform matrices based on the observed waveform feature vector matrix group, the fuzzy membership function and the preset similarity tolerance, and calculate the average value to obtain a current similarity result; the remaining waveform matrix is ​​all waveform matrices in the observed waveform feature vector matrix group except the current waveform matrix; The construction unit is further used to: obtain a first similarity degree through the current similarity results corresponding to all current waveform matrices; The loop processing unit is used to: expand the preset embedding dimension to obtain an embedding dimension expansion value, and re-execute the actions of the construction unit and the calculation unit using the embedding dimension expansion value as the preset embedding dimension to obtain a second similarity degree; The calculation unit is further used to: calculate the waveform complexity fuzzy entropy using the first similarity degree and the second similarity degree to obtain a waveform complexity measurement result.

10. A waveform complexity measurement device based on waveform feature vector, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the waveform complexity measurement device based on waveform feature vector is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the waveform complexity measurement method based on waveform feature vector as described in any one of claims 1-8.

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