Signal detection method and system based on Spearman rank correlation matrix inverse eigenvalue

Abstract

The invention provides a signal detection method and system based on Spearman rank correlation matrix inverse eigenvalues. The method comprises the following steps: acquiring observation signals received by multiple paths of sensors; calculating Spearman rank correlation coefficients of any two paths of signals to form a Spearman rank correlation matrix; acquiring a characteristic value of the Spearman rank correlation matrix, and calculating a corresponding inverse characteristic value; calculating statistics based on the sum of the inverse feature values; and setting a detection threshold value, and comparing the statistical magnitude with the detection threshold value to complete the detection process. According to the invention, the Spearman rank correlation transformation of the signal is used to suppress the negative influence of the pulse noise, and the sum of the inverse characteristic values of the Spearman rank correlation matrix is calculated to design the corresponding statistical magnitude, so that the high-performance signal detection under the pulse interference is realized. The method has high anti-impulse noise robustness, and signal detection can be carried out under background noise containing pulse components.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a signal detection method and system based on inverse eigenvalues ​​of a Spearman rank correlation matrix. Background Art

[0002] In the field of communication technology, signal detection is one of the core tasks of signal processing, and its purpose is to identify the presence or absence of useful signals from complex background noise. The accuracy and reliability of signal detection are crucial for subsequent operations such as parameter estimation and coding recognition. Signal detection methods usually need to consider the statistical characteristics of noise to optimize detection performance. Ideally, background noise is usually assumed to be Gaussian distributed, and traditional energy detectors can achieve good performance. However, the noise in actual communication environments is often more complex, especially the presence of impulse noise, which poses new challenges to signal detection.

[0003] At present, energy detectors are widely used in the field of signal detection due to their excellent characteristics such as easy implementation, simple structure, and complete theory. Energy detectors determine the presence or absence of a signal by measuring the energy of the signal. When the background noise follows a Gaussian distribution, the energy detector can effectively detect useful signals with high detection accuracy and low false alarm rate. In addition, the implementation of the energy detector does not require assumptions about prior knowledge of the signal, so it has wide applicability in practical applications.

[0004] However, in real environments, noise sources often exhibit pulse characteristics, that is, they contain some outliers with short duration but large amplitude. These outliers will drown out useful signal components, causing the system signal-to-noise ratio to drop rapidly. In this case, the detection performance of traditional energy detectors will be significantly reduced or even completely ineffective due to their sensitivity to pulse noise. The presence of pulse noise makes it impossible for traditional signal detection methods to effectively distinguish between signals and noise, and thus cannot accurately identify the presence or absence of useful signals. Therefore, existing signal detection technologies have obvious deficiencies when facing pulse interference and cannot meet the high requirements for signal detection in actual communication environments. Summary of the invention

[0005] According to the technical problem raised above, a signal detection method of the inverse eigenvalue of the Spearman rank correlation matrix under pulse interference is provided. The present invention uses the Spearman rank correlation transform of the signal to suppress the negative impact of pulse noise, and then designs the corresponding statistics by calculating the sum of the inverse eigenvalues ​​of the Spearman rank correlation matrix, thereby realizing high-performance signal detection under pulse interference.

[0006] The technical means adopted by the present invention are as follows: A signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix, comprising: S1, obtaining observation signals received by multiple sensors; S2, calculating the Spearman rank correlation coefficient of any two signals to form a Spearman rank correlation matrix; S3, obtaining the eigenvalues ​​of the Spearman rank correlation matrix and calculating the corresponding inverse eigenvalues; S4, calculating statistics based on the sum of inverse eigenvalues; S5. Set the detection threshold and compare the statistics with the detection threshold to complete the detection process.

[0007] Furthermore, in step S1, the observed signal obtained is specifically:

[0008] in, Indicates The road sensor at the sampling time The received observation signal, represents the source signal to be detected, Indicates The gain factor of the sensor, represents the background noise, , Indicates the signal length, , Indicates the number of sensors.

[0009] Furthermore, step S2 specifically includes: S21. Calculate the Spearman rank correlation coefficient of any two signals. The calculation formula is as follows:

[0010] in, Indicates Road signal and The Spearman rank correlation coefficient between the road signals, express In the Rank statistics in the path signal, express In the Rank statistics in path signals; , , Indicates the number of sensors; Indicates the signal length; S22, based on the Spearman rank correlation coefficient calculated in step S21, a Spearman rank correlation matrix is ​​formed, and the formula is as follows:

[0011] in, represents the Spearman rank correlation matrix, represents the Spearman rank correlation coefficient between the first signal and the second signal, Indicates the first signal and the The Spearman rank correlation coefficient between the road signals, represents the Spearman rank correlation coefficient between the second signal and the first signal, Indicates the second signal and the The Spearman rank correlation coefficient between the road signals, Indicates The Spearman rank correlation coefficient between the first signal and the second signal, Indicates The Spearman rank correlation coefficient between the first signal and the second signal.

[0012] Further, step S3 specifically includes: S31. Get the Spearman rank correlation matrix Eigenvalues ,in, ; S32, based on acquisition Eigenvalues , calculate the corresponding inverse eigenvalue, the calculation formula is as follows:

[0013] in, Indicates The inverse eigenvalues, , Represents the lower rounding symbol, Indicates the number of sensors; represents the Spearman rank correlation matrix eigenvalues, represents the Spearman rank correlation matrix feature values.

[0014] Furthermore, in step S4, based on the sum of the inverse eigenvalues, the statistic is calculated, and the calculation formula is as follows:

[0015] in, Represents statistics.

[0016] Furthermore, step S5 specifically includes: S51, set the detection threshold, denoted as , S52. Comparative Statistics and detection threshold The size of , it means the source signal exists; if , it means the source signal does not exist.

[0017] The present invention also provides a signal detection system based on the inverse eigenvalue of the Spearman rank correlation matrix implemented based on the signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix, comprising: A signal acquisition module is used to acquire observation signals received by multiple sensors; A Spearman rank correlation matrix calculation module is used to calculate the Spearman rank correlation coefficient of any two signals to form a Spearman rank correlation matrix; The eigenvalue acquisition and inverse eigenvalue calculation module is used to obtain the eigenvalues ​​of the Spearman rank correlation matrix and calculate the corresponding inverse eigenvalues; A statistics calculation module, used for calculating statistics based on the sum of inverse eigenvalues; The detection module is used to set the detection threshold and compare the size of the statistic with the detection threshold to complete the detection process.

[0018] Compared with the prior art, the present invention has the following advantages: The present invention realizes effective suppression of large outliers in impulse noise through Spearman rank correlation transformation, and calculates statistics based on the sum of inverse eigenvalues ​​of Spearman rank correlation matrix, showing excellent robustness under impulse interference. Therefore, in environmental noise containing impulse components, the signal detection method based on the inverse eigenvalue of Spearman rank correlation matrix has excellent detection performance.

[0019] Based on the above reasons, the present invention can be widely promoted in the fields of communication and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in 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 labor.

[0021] Figure 1 The figure is a flow chart of the method of the present invention.

[0022] Figure 2 A comparison diagram of detection probabilities of signals detected in an impulse noise environment by an energy detector and a Spearman rank correlation matrix inverse eigenvalue detector provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0025] like Figure 1 As shown, the present invention provides a signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix under pulse interference, comprising: S1, obtaining observation signals received by multiple sensors; S2, calculating the Spearman rank correlation coefficient of any two signals to form a Spearman rank correlation matrix; S3, obtaining the eigenvalues ​​of the Spearman rank correlation matrix and calculating the corresponding inverse eigenvalues; S4, calculating statistics based on the sum of inverse eigenvalues; S5. Set the detection threshold and compare the statistics with the detection threshold to complete the detection process.

[0026] In specific implementation, as a preferred embodiment of the present invention, in step S1, the observation signal obtained is specifically:

[0027] in, Indicates The road sensor at the sampling time The received observation signal, represents the source signal to be detected, Indicates The gain factor of the sensor, represents the background noise, , Indicates the signal length, , Indicates the number of sensors.

[0028] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes: S21. Calculate the Spearman rank correlation coefficient of any two signals. The calculation formula is as follows:

[0029] in, Indicates Road signal and The Spearman rank correlation coefficient between the road signals, express In the Rank statistics in the path signal, express In the Rank statistics in path signals; , , Indicates the number of sensors; Indicates the signal length; S22, based on the Spearman rank correlation coefficient calculated in step S21, a Spearman rank correlation matrix is ​​formed, and the formula is as follows:

[0030] in, represents the Spearman rank correlation matrix, represents the Spearman rank correlation coefficient between the first signal and the second signal, Indicates the first signal and the The Spearman rank correlation coefficient between the road signals, represents the Spearman rank correlation coefficient between the second signal and the first signal, Indicates the second signal and the The Spearman rank correlation coefficient between the road signals, Indicates The Spearman rank correlation coefficient between the first signal and the second signal, Indicates The Spearman rank correlation coefficient between the first signal and the second signal.

[0031] In specific implementation, as a preferred embodiment of the present invention, step S3 specifically includes: S31. Get the Spearman rank correlation matrix Eigenvalues ,in, ; S32, based on acquisition Eigenvalues , calculate the corresponding inverse eigenvalue, the calculation formula is as follows:

[0032] in, Indicates The inverse eigenvalues, , Represents the lower rounding symbol, Indicates the number of sensors; represents the Spearman rank correlation matrix eigenvalues, represents the Spearman rank correlation matrix feature values.

[0033] In specific implementation, as a preferred embodiment of the present invention, in step S4, based on the sum of the inverse eigenvalues, the statistic is calculated, and the calculation formula is as follows:

[0034] in, Represents statistics.

[0035] In specific implementation, as a preferred embodiment of the present invention, step S5 specifically includes: S51, set the detection threshold, denoted as , S52. Comparative Statistics and detection threshold The size of , it means the source signal exists; if , it means the source signal does not exist.

[0036] Corresponding to the signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix in the present application, the present application also provides a signal detection system based on the inverse eigenvalue of the Spearman rank correlation matrix, including: A signal acquisition module is used to acquire observation signals received by multiple sensors; A Spearman rank correlation matrix calculation module is used to calculate the Spearman rank correlation coefficient of any two signals to form a Spearman rank correlation matrix; The eigenvalue acquisition and inverse eigenvalue calculation module is used to obtain the eigenvalues ​​of the Spearman rank correlation matrix and calculate the corresponding inverse eigenvalues; A statistics calculation module, used for calculating statistics based on the sum of inverse eigenvalues; The detection module is used to set the detection threshold and compare the size of the statistic with the detection threshold to complete the detection process.

[0037] As for the embodiments of the present invention, since they correspond to the embodiments above, the description is relatively simple. For the relevant similarities, please refer to the description of the above embodiments, which will not be described in detail here.

[0038] Example In order to analyze the performance of the Spearman rank correlation matrix inverse eigenvalue detector and the energy detector in signal detection under impulse noise, the present invention will be verified by Monte Carlo experiment. The experimental parameters are set as follows: The source signal consists of a segment of length A signal that follows a standard normal distribution is generated randomly.

[0039] Impulse noise is simulated by a mixture of Gaussian distributions:

[0040] in, represents the probability of the pulse component occurring in the entire pulse noise environment, Represents the standard deviation of the pulse component. At this time, the signal-to-noise ratio of the received signal can be defined as:

[0041] Through Monte Carlo experiments, the performance of the Spearman rank correlation matrix inverse eigenvalue detector and the energy detector under different signal-to-noise ratios is compared and analyzed, which verifies that the Spearman rank correlation matrix inverse eigenvalue detector is robust in impulse noise environments.

[0042] The number of experiments is times, false alarm probability , number of sensors , gain factor The experimental results are as follows Figure 2 As shown. Figure 2 The experimental results show that due to the existence of the pulse component, the detection probability curve of the energy detector is close to a The horizontal line completely loses the detection effect, while the Spearman rank correlation matrix inverse eigenvalue detector has a higher detection probability and shows robustness to pulse interference, indicating that the Spearman rank correlation matrix inverse eigenvalue detector can be used as a powerful tool for signal detection in a pulse interference environment.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix, characterized in that: include: S1, obtaining observation signals received by multiple sensors; S2, calculating the Spearman rank correlation coefficient of any two signals to form a Spearman rank correlation matrix; S3, obtaining the eigenvalues ​​of the Spearman rank correlation matrix and calculating the corresponding inverse eigenvalues; S4, calculating statistics based on the sum of inverse eigenvalues; S5. Set the detection threshold and compare the statistics with the detection threshold to complete the detection process.

2. The signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix according to claim 1, characterized in that: In step S1, the observed signal obtained is specifically: in, Indicates The road sensor at the sampling time The received observation signal, represents the source signal to be detected, Indicates The gain factor of the sensor, represents the background noise, , Indicates the signal length, , Indicates the number of sensors.

3. The signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix according to claim 1, characterized in that: Step S2 specifically includes: S21. Calculate the Spearman rank correlation coefficient of any two signals. The calculation formula is as follows: in, Indicates Road signal and The Spearman rank correlation coefficient between the road signals, express In the Rank statistics in the path signal, express In the Rank statistics in path signals; , , Indicates the number of sensors; Indicates the signal length; S22, based on the Spearman rank correlation coefficient calculated in step S21, a Spearman rank correlation matrix is ​​formed, and the formula is as follows: in, represents the Spearman rank correlation matrix, represents the Spearman rank correlation coefficient between the first signal and the second signal, Indicates the first signal and the The Spearman rank correlation coefficient between the road signals, represents the Spearman rank correlation coefficient between the second signal and the first signal, Indicates the second signal and the The Spearman rank correlation coefficient between the road signals, Indicates The Spearman rank correlation coefficient between the first signal and the second signal, Indicates The Spearman rank correlation coefficient between the first signal and the second signal.

4. The signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix according to claim 1, characterized in that: Step S3 specifically includes: S31. Get the Spearman rank correlation matrix Eigenvalues ,in, ; S32, based on acquisition Eigenvalues , calculate the corresponding inverse eigenvalue, the calculation formula is as follows: in, Indicates The inverse eigenvalues, , Represents the lower rounding symbol, Indicates the number of sensors; represents the Spearman rank correlation matrix eigenvalues, represents the Spearman rank correlation matrix feature values.

5. The signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix according to claim 1, characterized in that: In step S4, the statistic is calculated based on the sum of the inverse eigenvalues, and the calculation formula is as follows: in, Represents statistics.

6. The signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix according to claim 1, characterized in that: Step S5 specifically includes: S51, set the detection threshold, denoted as , S52. Comparative Statistics and detection threshold The size of , it means the source signal exists; if , it means the source signal does not exist.

7. A signal detection system based on the inverse eigenvalue of the Spearman rank correlation matrix implemented by the signal detection method based on the inverse eigenvalue of the Spearman rank correlation matrix according to any one of claims 1 to 6, characterized in that: include: A signal acquisition module is used to acquire observation signals received by multiple sensors; A Spearman rank correlation matrix calculation module is used to calculate the Spearman rank correlation coefficient of any two signals to form a Spearman rank correlation matrix; The eigenvalue acquisition and inverse eigenvalue calculation module is used to obtain the eigenvalues ​​of the Spearman rank correlation matrix and calculate the corresponding inverse eigenvalues; A statistics calculation module, used for calculating statistics based on the sum of inverse eigenvalues; The detection module is used to set the detection threshold and compare the size of the statistic with the detection threshold to complete the detection process.

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