Signal detection method and system based on Gini gamma correlation coefficient matrix eigenvalue

Abstract

The invention relates to the technical field of communication, in particular to a signal detection method and system based on Gini gamma correlation coefficient matrix eigenvalues. The method comprises the following steps: acquiring signals received by multiple paths of sensors, and calculating rank statistics of each path of received signal; calculating Gini gamma correlation coefficients of any two paths of received signals to form a correlation coefficient matrix; obtaining characteristic values of the Gini gamma correlation coefficient matrix, and calculating a geometric mean value and an arithmetic mean value of the characteristic values; constructing test statistics based on the ratio of the geometric mean value to the arithmetic mean value; comparing the test statistic with the detection threshold to complete the detection process; according to the method, the influence of a large abnormal value in the pulse noise is effectively suppressed through Gini gamma correlation transformation, the test statistic constructed based on the ratio of the geometric mean value to the arithmetic mean value of the characteristic values shows excellent robustness in a pulse interference environment, and the signal detection performance is remarkably improved; signal detection can be carried out under background noise containing pulse components.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and specifically to a signal detection method and system based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix. Background Technology

[0002] Signal detection in noise is a research hotspot in radar, sonar, and communications fields. Its fundamental task is to determine the presence of a useful signal of interest, facilitating subsequent parameter estimation, encoding, and recognition operations. Under Gaussian noise interference, energy detectors are the most commonly used signal detection method. However, noise sources in real-world scenarios often exhibit impulse characteristics, resulting in the received signal containing short-duration but high-energy outliers. These outliers can overwhelm the useful signal components, causing a rapid decrease in the system's signal-to-noise ratio and rendering the energy detector completely ineffective. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a signal detection method and system based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix. The method uses the Gini gamma correlation transform of the signal to suppress the negative impact of impulse noise, and then designs corresponding statistics by calculating the ratio of the geometric mean to the arithmetic mean of the eigenvalues ​​of the correlation matrix, thereby achieving high-precision signal detection under impulse interference.

[0004] On one hand, embodiments of the present invention provide a signal detection method based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix, comprising the following steps:

[0005] S100: Acquire signals received from multiple sensors and calculate the rank statistic of each received signal.

[0006] S200, calculate the Gini gamma correlation coefficient between any two received signals to form a correlation coefficient matrix;

[0007] S300, obtain the eigenvalues ​​of the Gini gamma correlation coefficient matrix, and calculate the geometric mean and arithmetic mean of the eigenvalues;

[0008] S400, construct a test statistic based on the ratio of the geometric mean to the arithmetic mean;

[0009] S500 compares the test statistic and the detection threshold to complete the detection process.

[0010] Optionally, in S100, the signals received by the multiplexed sensors include:

[0011] The signal received by the m-th sensor at sampling time n ;Signal From the source signal s(n), gain coefficient and background noise The structure consists of m, which is a positive integer ranging from 1 to M, n, which is a positive integer ranging from 1 to L, M being the number of sensors, and L being the signal length.

[0012] Optionally, in S100, calculating the rank statistic for each received signal includes:

[0013] S110, the signal received by the m-th sensor at sampling time n The signal received by the sensor at sampling time l Perform difference calculation;

[0014] S120, input the difference calculation result into the Heaviside step function for sign transformation;

[0015] S130, the symbol transformation results are summed over the signal length L to obtain the received signal. rank statistics .

[0016] Optionally, in S200, calculating the Gini gamma correlation coefficient of any two received signals to form a correlation coefficient matrix includes:

[0017] S210, the rank statistic for the k-th signal. Rank statistics of the l-th signal Perform a summation operation to obtain a first summation value. Subtract the first summation value from the sum of the signal length L and 1 to obtain a first intermediate value. Take the absolute value of the first intermediate value to obtain a first absolute value.

[0018] S220, the rank statistic for the k-th signal. Rank statistics of the l-th signal Perform a difference operation to obtain a second intermediate value, and take the absolute value of the second intermediate value to obtain a second absolute value;

[0019] S230, the first absolute value is summed over the signal length L to obtain a first sum, the second absolute value is summed over the signal length L to obtain a second sum, and the first sum is subtracted from the second sum to obtain a third intermediate value;

[0020] S240, divide the square of L by 2 and round down to the nearest integer to obtain the denominator value. Divide the third intermediate value by the denominator value to obtain the Gini-gamma correlation coefficient between the k-th signal and the l-th signal. , where k and l are both positive integers ranging from 1 to M;

[0021] S250, the Gini gamma correlation coefficient between every two signals in the M-channel signal. Arrange the matrix to obtain an M-row, M-column Gini-gamma correlation coefficient matrix R, where the diagonal elements of the matrix are all 1.

[0022] Optionally, in S300, obtaining the eigenvalues ​​of the Gini-gamma correlation coefficient matrix and calculating the geometric mean and arithmetic mean of the eigenvalues ​​includes:

[0023] S310, Perform eigenvalue decomposition on the Gini-gamma correlation coefficient matrix R to obtain M eigenvalues. , where i takes the value of a positive integer from 1 to M, and the eigenvalues ​​are arranged in descending order;

[0024] S320, the M feature values Perform a series of multiplications to obtain the product value, and then take the Mth root of the product value to obtain the geometric mean G of the eigenvalues;

[0025] S330, the M feature values The sum is accumulated to obtain a total value. The total value is then divided by M to obtain the arithmetic mean A of the eigenvalues.

[0026] Optionally, in S400, constructing the test statistic based on the ratio of the geometric mean and the arithmetic mean includes:

[0027] Using the geometric mean G as the numerator and the arithmetic mean A as the denominator, a division operation is performed between the numerator and the denominator to obtain the test statistic T.

[0028] Optionally, in S500, the comparison test statistic and the detection threshold are used to complete the detection process, including:

[0029] Compare the test statistic T with the detection threshold. If the test statistic T is less than the detection threshold, the source signal is determined to exist; if the test statistic T is greater than or equal to the detection threshold, the source signal is determined to not exist.

[0030] On the other hand, embodiments of the present invention provide a signal detection system based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix, comprising:

[0031] At least one processor;

[0032] At least one memory for storing at least one program;

[0033] When the at least one program is executed by the at least one processor, the at least one processor performs the method as described in any of the preceding descriptions.

[0034] On the other hand, embodiments of the present invention provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the method described in any of the preceding claims.

[0035] The embodiments of the present invention have the following beneficial effects:

[0036] This invention effectively suppresses large outliers in impulse noise through Gini gamma correlation transform and constructs a statistic based on the ratio of the geometric mean and arithmetic mean of the eigenvalues ​​of the Gini gamma correlation coefficient matrix, exhibiting excellent robustness under impulse interference. Therefore, the signal detection method based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix has excellent detection performance in environmental noise containing impulse components. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating a signal detection method based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix in an embodiment of the present invention.

[0039] Figure 2 This is a comparison chart of the detection probabilities of the energy detector and the eigenvalues ​​of the Gini-Gamma correlation coefficient matrix in the context of impulse noise in this embodiment of the invention. Detailed Implementation

[0040] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0042] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0043] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0045] refer to Figure 1 ,like Figure 1 The figure shows a signal detection method based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix provided by an embodiment of the present invention. The method includes the following steps:

[0046] S100: Acquire signals received from multiple sensors and calculate the rank statistic of each received signal.

[0047] S200, calculate the Gini gamma correlation coefficient between any two received signals to form a correlation coefficient matrix;

[0048] S300, obtain the eigenvalues ​​of the Gini gamma correlation coefficient matrix, and calculate the geometric mean and arithmetic mean of the eigenvalues;

[0049] S400, construct a test statistic based on the ratio of the geometric mean to the arithmetic mean;

[0050] S500 compares the test statistic and the detection threshold to complete the detection process.

[0051] This embodiment achieves effective suppression of impulse noise by constructing a test statistic based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix. By employing the rank statistic and the Gini gamma correlation coefficient, direct calculation of the received signal amplitude is avoided, significantly reducing the impact of large outliers in impulse noise on detection performance. The test statistic, constructed by the ratio of the geometric mean to the arithmetic mean, sensitively reflects changes in the signal subspace dimension, maintaining a high detection probability even with a reduced signal-to-noise ratio. This solves the technical problem of traditional energy detectors failing in impulse noise environments, improving the robustness and reliability of signal detection.

[0052] In a preferred embodiment of this invention, in S100, the signals received by the multi-channel sensor include:

[0053] The signal received by the m-th sensor at sampling time n ;Signal From the source signal s(n), gain coefficient and background noise The structure consists of m, which is a positive integer ranging from 1 to M, n, which is a positive integer ranging from 1 to L, M being the number of sensors, and L being the signal length.

[0054] Specifically, no. Road sensor at all times The received signal has the following form:

[0055] ; ; ;

[0056] in, It is the first Road sensor at sampling time The received observation signal, The source signal to be detected. Indicates the first Gain coefficient of road sensor Indicates background noise. Where L is the number of sensors and L is the signal length.

[0057] The signal model in this embodiment clarifies the superposition relationship between the multi-sensor received signals and the source signal, gain coefficient, and background noise. This is achieved by using the sensor gain coefficient... Introducing the model as an independent parameter allows it to adapt to differences in the receiving sensitivity of different sensors, enhancing the method's adaptability to practical multi-sensor array systems. This model structure provides a clear mathematical foundation for subsequent rank statistic calculations and correlation coefficient matrix construction, ensuring that the detection method maintains stable detection performance in heterogeneous sensor networks.

[0058] In a preferred embodiment of this example, in S100, calculating the rank statistic of each received signal includes:

[0059] S110, the signal received by the m-th sensor at sampling time n The signal received by the sensor at sampling time l Perform difference calculation;

[0060] S120, input the difference calculation result into the Heaviside step function for sign transformation;

[0061] S130, the symbol transformation results are summed over the signal length L to obtain the received signal. rank statistics .

[0062] Specifically, the rank statistic for each received signal can be calculated using the following formula:

[0063] ;

[0064] in, It is the first Road sensor at sampling time Received signal, It is the first Road sensor at sampling time Received signal, Indicates received signal rank statistics, Let L represent the Heaviside step function, where L is the signal length.

[0065] The rank statistic calculation method in this embodiment transforms signal amplitude comparison into rank sorting using a Heaviside step function, converting the received signal into a rank statistic based on relative magnitude. This eliminates the direct impact of impulse noise amplitude outliers on the statistic, as the rank statistic only focuses on the relative magnitude relationship between signal sampling points rather than absolute amplitude values. The summation operation transforms the time-domain signal into a stable statistic, providing impulse-resistant input data for subsequent Gini-gamma correlation coefficient calculation, significantly improving the robustness of the detection method in impulse noise environments.

[0066] In a preferred embodiment of this invention, step S200, calculating the Gini gamma correlation coefficient between any two received signals to form a correlation coefficient matrix, includes:

[0067] S210, the rank statistic for the k-th signal. Rank statistics of the l-th signal Perform a summation operation to obtain a first summation value. Subtract the first summation value from the sum of the signal length L and 1 to obtain a first intermediate value. Take the absolute value of the first intermediate value to obtain a first absolute value.

[0068] S220, the rank statistic for the k-th signal. Rank statistics of the l-th signal Perform a difference operation to obtain a second intermediate value, and take the absolute value of the second intermediate value to obtain a second absolute value;

[0069] S230, the first absolute value is summed over the signal length L to obtain a first sum, the second absolute value is summed over the signal length L to obtain a second sum, and the first sum is subtracted from the second sum to obtain a third intermediate value;

[0070] S240, divide the square of L by 2 and round down to the nearest integer to obtain the denominator value. Divide the third intermediate value by the denominator value to obtain the Gini-gamma correlation coefficient between the k-th signal and the l-th signal. , where k and l are both positive integers ranging from 1 to M;

[0071] S250, the Gini gamma correlation coefficient between every two signals in the M-channel signal. Arrange the matrix to obtain an M-row, M-column Gini-gamma correlation coefficient matrix R, where the diagonal elements of the matrix are all 1.

[0072] Specifically, the process of calculating the Gini gamma correlation coefficient of any two received signals and forming the correlation coefficient matrix is ​​as follows:

[0073] ;

[0074] , ;

[0075] ;

[0076] in, Indicates the first Road signal and the The Gini gamma correlation coefficient between the road signals Represents the Gini gamma correlation coefficient matrix. Indicates received signal rank statistics, Indicates received signal rank statistics, Represents the lower rounding symbol, Where L is the number of sensors and L is the signal length.

[0077] In this embodiment, the Gini-gamma correlation coefficient calculation process further enhances the suppression of impulse noise through a combination of L1 norm distance metric (absolute value operation) and rank statistic. The difference structure between the first and second absolute value terms effectively eliminates the influence of co-current impulse interference; floor operation and normalization ensure scale invariance of the correlation coefficient. The resulting correlation coefficient matrix fully preserves the correlation structure between multiple signals while suppressing interference from outlier sampling points, providing a high-quality input matrix for eigenvalue decomposition and improving the reliability of subsequent test statistics.

[0078] In a preferred embodiment of this example, step S300, obtaining the eigenvalues ​​of the Gini-Gamma correlation coefficient matrix and calculating the geometric mean and arithmetic mean of the eigenvalues, includes:

[0079] S310, Perform eigenvalue decomposition on the Gini-gamma correlation coefficient matrix R to obtain M eigenvalues. , where i takes the value of a positive integer from 1 to M, and the eigenvalues ​​are arranged in descending order;

[0080] S320, the M feature values Perform a series of multiplications to obtain the product value, and then take the Mth root of the product value to obtain the geometric mean G of the eigenvalues;

[0081] S330, the M feature values The sum is accumulated to obtain a total value. The total value is then divided by M to obtain the arithmetic mean A of the eigenvalues.

[0082] Specifically, the eigenvalues ​​of the Gini gamma correlation coefficient matrix are obtained, and the geometric mean and arithmetic mean of the eigenvalues ​​are calculated as follows:

[0083] ; ; ;

[0084] in, This represents the M eigenvalues ​​of the Gini-gamma correlation coefficient matrix. The geometric mean of the eigenvalues. The arithmetic mean of the eigenvalues ​​is represented by M, where M is the number of sensors.

[0085] The eigenvalue acquisition and mean calculation method in this embodiment extracts the intrinsic structural features of the correlation coefficient matrix through eigenvalue decomposition. The geometric mean, obtained through a series of multiplications and square root operations, is more sensitive to changes in smaller eigenvalues; the arithmetic mean, obtained through cumulative averaging, reflects the overall energy level. The combined use of these two means preserves the principal component information of the signal subspace while capturing subtle changes in the noise subspace, enabling the test statistic to distinguish the differences in eigenvalue distribution when the signal is present or absent, thus improving detection sensitivity.

[0086] In a preferred embodiment of this example, in S400, the step of constructing a test statistic based on the ratio of the geometric mean and the arithmetic mean includes:

[0087] Using the geometric mean G as the numerator and the arithmetic mean A as the denominator, a division operation is performed between the numerator and the denominator to obtain the test statistic T.

[0088] Specifically, the formula for constructing the test statistic based on the ratio of the geometric mean to the arithmetic mean is as follows:

[0089]

[0090] in, This represents the test statistic. Represents the Gini gamma correlation coefficient matrix 1 eigenvalue, The geometric mean of the eigenvalues. The arithmetic mean of the eigenvalues. This represents the number of sensors.

[0091] In this embodiment, the test statistic is constructed using the ratio of the geometric mean to the arithmetic mean, forming a dimensionless normalized statistic. This ratio approaches 1 when the signal is absent and decreases significantly when the signal is present, exhibiting clear physical meaning and threshold characteristics. The division operation enhances the sensitivity of the statistic to the distribution of eigenvalues, reduces its dependence on absolute energy levels, makes the detection threshold setting more stable, reduces false alarm probability fluctuations, and improves the adaptability of the detection method under different impulse noise intensities.

[0092] In a preferred embodiment of this invention, step S500, which compares the test statistic and the detection threshold to complete the detection process, includes:

[0093] Compare the test statistic T with the detection threshold. If the test statistic T is less than the detection threshold, the source signal is determined to exist; if the test statistic T is greater than or equal to the detection threshold, the source signal is determined to not exist.

[0094] The decision rule in this embodiment achieves binary hypothesis testing by comparing the relationship between the test statistic and the detection threshold. The decision criterion that determines the presence of a signal if the value is less than the threshold matches the distribution characteristics of the constructed test statistic, achieving a constant false alarm rate even in impulse noise environments. This decision rule is computationally simple, suitable for real-time implementation, and consistent with Monte Carlo simulation results, ensuring the operability and reliability of the detection method in practical applications.

[0095] To analyze the eigenvalues ​​of the Gini gamma correlation coefficient matrix and the performance of the energy detector in signal detection under impulse noise, this invention will be verified through Monte Carlo experiments.

[0096] The experimental parameters are set as follows:

[0097] The source signal is randomly generated from a signal of length L = 200 that follows a standard normal distribution.

[0098] Impulse noise is simulated using a mixture of Gaussian distributions:

[0099] ;

[0100] in This represents the probability of the impulse component occurring in the entire impulse noise environment. This represents the standard deviation of the pulse component. The signal-to-noise ratio (SNR) of the received signal can then be defined as:

[0101] ;

[0102] Monte Carlo experiments were conducted to compare and analyze the performance of the Gini gamma correlation coefficient matrix eigenvalues ​​and the energy detector under different signal-to-noise ratios, verifying the robustness of the Gini gamma correlation coefficient matrix eigenvalues ​​in impulse noise environments. The number of experiments was [number missing]. Next, false alarm probability Number of sensors Gain coefficient The experimental results are as follows: Figure 2 As shown.

[0103] Depend on Figure 2 The experimental results show that, due to the presence of the pulse component, the detection probability curve of the energy detector is close to a line. The horizontal line completely lost its detection effect, while the eigenvalues ​​of the Gini gamma correlation coefficient matrix had a high detection probability, demonstrating robustness to impulse interference. This indicates that the eigenvalues ​​of the Gini gamma correlation coefficient matrix can be used as a powerful tool for signal detection in impulse interference environments.

[0104] This invention also provides a signal detection system based on the eigenvalues ​​of a Gini gamma correlation coefficient matrix, comprising: at least one processor; at least one memory for storing at least one program; and when the at least one program is executed by the at least one processor, causing the at least one processor to implement the method as described in any of the preceding embodiments.

[0105] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), or other processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the system to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the methods described in the embodiments of the present invention. Various application programs and various data may also be stored in the computer-readable storage medium.

[0106] The content of the above method embodiments is applicable to this embodiment. The specific functions implemented in this embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. Therefore, they will not be repeated here.

[0107] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0108] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0109] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0110] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0111] This invention also provides a computer program product, including a computer program or computer instructions, which are stored in a memory. A processor of a computer device reads the computer program or computer instructions from the memory and executes the computer program or computer instructions, causing the computer device to perform the above-described method.

[0112] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0113] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A signal detection method based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix, characterized in that, The method includes the following steps: S100: Acquire signals received from multiple sensors and calculate the rank statistic of each received signal. S200, calculate the Gini gamma correlation coefficient between any two received signals to form a correlation coefficient matrix; S300, obtain the eigenvalues ​​of the Gini gamma correlation coefficient matrix, and calculate the geometric mean and arithmetic mean of the eigenvalues; S400, construct a test statistic based on the ratio of the geometric mean to the arithmetic mean; S500 compares the test statistic and the detection threshold to complete the detection process.

2. The method according to claim 1, characterized in that, In S100, the signals received by the multiple sensors include: The signal received by the m-th sensor at sampling time n ;Signal From the source signal s(n), gain coefficient and background noise The structure consists of m, which is a positive integer ranging from 1 to M, n, which is a positive integer ranging from 1 to L, M being the number of sensors, and L being the signal length.

3. The method according to claim 1, characterized in that, In S100, the calculation of the rank statistic for each received signal includes: S110, the signal received by the m-th sensor at sampling time n The signal received by the sensor at sampling time l Perform difference calculation; S120, input the difference calculation result into the Heaviside step function for sign transformation; S130, the symbol transformation results are summed over the signal length L to obtain the received signal. rank statistics .

4. The method according to claim 1, characterized in that, In S200, the calculation of the Gini gamma correlation coefficient between any two received signals to form a correlation coefficient matrix includes: S210, the rank statistic for the k-th signal. Rank statistics of the l-th signal Perform a summation operation to obtain a first summation value. Subtract the first summation value from the sum of the signal length L and 1 to obtain a first intermediate value. Take the absolute value of the first intermediate value to obtain a first absolute value. S220, the rank statistic for the k-th signal. Rank statistics of the l-th signal Perform a difference operation to obtain a second intermediate value, and take the absolute value of the second intermediate value to obtain a second absolute value; S230, the first absolute value is summed over the signal length L to obtain a first sum, the second absolute value is summed over the signal length L to obtain a second sum, and the first sum is subtracted from the second sum to obtain a third intermediate value; S240, divide the square of L by 2 and round down to the nearest integer to obtain the denominator value. Divide the third intermediate value by the denominator value to obtain the Gini-gamma correlation coefficient between the k-th signal and the l-th signal. , where k and l are both positive integers ranging from 1 to M; S250, the Gini gamma correlation coefficient between every two signals in the M-channel signal. Arrange the matrix to obtain an M-row, M-column Gini-gamma correlation coefficient matrix R, where the diagonal elements of the matrix are all 1.

5. The method according to claim 1, characterized in that, In S300, obtaining the eigenvalues ​​of the Gini-Gamma correlation coefficient matrix and calculating the geometric mean and arithmetic mean of the eigenvalues ​​includes: S310, Perform eigenvalue decomposition on the Gini-gamma correlation coefficient matrix R to obtain M eigenvalues. , where i takes the value of a positive integer from 1 to M, and the eigenvalues ​​are arranged in descending order; S320, the M feature values Perform a series of multiplications to obtain the product value, and then take the Mth root of the product value to obtain the geometric mean G of the eigenvalues; S330, the M feature values The sum is accumulated to obtain a total value. The total value is then divided by M to obtain the arithmetic mean A of the eigenvalues.

6. The method according to claim 1, characterized in that, In S400, the construction of the test statistic based on the ratio of the geometric mean and the arithmetic mean includes: Using the geometric mean G as the numerator and the arithmetic mean A as the denominator, a division operation is performed between the numerator and the denominator to obtain the test statistic T.

7. The method according to claim 1, characterized in that, In S500, the comparison test statistic and the detection threshold are used to complete the detection process, including: Compare the test statistic T with the detection threshold. If the test statistic T is less than the detection threshold, the source signal is determined to exist; if the test statistic T is greater than or equal to the detection threshold, the source signal is determined to not exist.

8. A signal detection system based on the eigenvalues ​​of the Gini gamma correlation coefficient matrix, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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