A signal detection method based on generalized Gaussian rank correlation in impulse noise environment

Abstract

The present invention belongs to the field of signal processing technology and provides a signal detection method based on generalized Gaussian rank correlation in an impulse noise environment, comprising: collecting received signals observed by two receivers, and calculating the rank statistics and kurtosis coefficient of the received signals; performing a generalized Gaussian rank transform on the received signals according to the rank statistics and kurtosis coefficient; performing correlation operations on the two transformed signals and constructing a test statistic; setting a decision threshold, constructing a decision model based on the decision threshold, inputting the test statistic into the decision model, and determining a signal detection result; the present invention fully suppresses the harmful effects of large outliers in impulse noise on the signal detection algorithm by performing a generalized Gaussian rank transform on the signal, and exhibits good robustness under environmental noise containing impulse components; in an environment of impulse noise interference, the method is an effective tool for signal detection with excellent performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular to a signal detection method based on generalized Gaussian rank correlation in an impulse noise environment. Background Art

[0002] Signal detection in noise is an important research topic in signal processing fields such as radar, sonar, and communications. Traditional signal detection methods, such as energy detectors, typically assume that background noise follows a Gaussian distribution. However, noise sources in real systems often exhibit distinct pulse characteristics, meaning their probability density functions have thicker tails and exhibit short durations but large amplitudes in the time domain. This type of noise is called impulse noise. Because impulse noise contains large-amplitude outliers, it can severely degrade the performance of energy detectors. While current technologies propose Gaussian rank-based correlation detectors to suppress the negative impact of large outliers in impulse noise, their detection performance still needs to be further improved when the pulse intensity is high.

[0003] To this end, those skilled in the art have proposed a signal detection method based on generalized Gaussian rank correlation in an impulse noise environment to solve the problem raised in the background technology. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a signal detection method based on generalized Gaussian rank correlation in an impulse noise environment, which can suppress large outliers in the impulse noise, and then construct a test statistic by calculating the correlation function of the transformed signal, thereby achieving the purpose of high-precision signal detection in an impulse noise environment.

[0005] A signal detection method based on generalized Gaussian rank correlation in an impulse noise environment comprises the following steps:

[0006] S1. Collecting received signals observed by two receivers and calculating the rank statistics and kurtosis coefficient of the received signals;

[0007] S2. performing a generalized Gaussian rank transform on the received signal according to the rank statistic and the kurtosis coefficient;

[0008] S3, performing correlation operation on the two transformed signals and constructing a test statistic;

[0009] S4. Setting a decision threshold, constructing a decision model based on the decision threshold, inputting the test statistic into the decision model, and determining a signal detection result.

[0010] Preferably, in step S1, the received signals observed by the two receivers at sampling time i are specifically:

[0011] x(i)=θ1s(i)+z1(i);

[0012] y(i)=θ2s(i)+z2(i);

[0013] i=1,2,...,n;

[0014] in, is the received signal observed by the two receivers at time i, s(i) is the source signal to be detected, θ l ≤1 indicates the signal gain of the corresponding receiver, z l (i) represents the background noise corresponding to the lth receiver, l = 1, 2 represents the lth receiver, and n is the signal length.

[0015] Preferably, in step S1, the formula for calculating the rank statistic of the received signal is as follows:

[0016]

[0017]

[0018] i=1,2,...,n;

[0019] Among them, R i represents the rank statistic of the first received signal x(i), Q i represents the rank statistic of the second received signal y(i), n is the signal length, H(·) represents the step function, and the expression of the step function is as follows:

[0020]

[0021] Preferably, in step S1, the formula for calculating the kurtosis coefficient of the two received signals is as follows:

[0022]

[0023] in, represents the sample mean of the first received signal, represents the sample mean of the second received signal, K x Indicates the kurtosis coefficient of the first received signal, K y Indicates the kurtosis coefficient of the second received signal.

[0024] Preferably, in step S2, performing a generalized Gaussian rank transform on the received signals observed by the two receivers is specifically:

[0025]

[0026] i=1,2,...,n;

[0027] Among them, Φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function Φ, Indicates the truncation coefficient of the first received signal, represents the truncation coefficient of the second received signal, min represents the minimum value function, max represents the maximum value function, and I(·) represents the indicator function, which is expressed as follows:

[0028]

[0029] Preferably, in step S3, the correlation operation is performed on the two transformed signals and the test statistics are constructed as follows:

[0030]

[0031] Wherein, T represents the test statistic, x′ represents the first transformed signal, and y′ represents the second transformed signal.

[0032] Preferably, in step S4, the construction of the decision model based on the decision threshold is specifically described as follows:

[0033]

[0034] Where T represents the test statistic, τ represents the decision threshold, H0 indicates that the source signal does not exist, and H1 indicates that the source signal exists.

[0035] Preferably, in step S4, the test statistic is input into the decision model to determine the specific description of the signal detection result as follows: compare the test statistic and the decision threshold. If the test statistic T is greater than the decision threshold τ, the source signal s exists; if the test statistic T is less than the decision threshold τ, it is considered that the source signal s does not exist.

[0036] A signal detection system based on generalized Gaussian rank correlation in an impulse noise environment, comprising:

[0037] Signal acquisition module: The signal acquisition module is responsible for collecting the received signals observed by the two receivers;

[0038] A preprocessing module, the preprocessing module is used to calculate the rank statistics and kurtosis coefficient of the received signal;

[0039] A generalized Gaussian rank transformation module, wherein the generalized Gaussian rank transformation module is used to perform a generalized Gaussian rank transformation on the received signal according to the rank statistic and the kurtosis coefficient;

[0040] A correlation operation and test statistic construction module, wherein the correlation operation and test statistic construction module performs a correlation operation on the two transformed signals to construct a test statistic;

[0041] The decision module is used to make a decision on the test statistic based on a set decision threshold to determine the signal detection result.

[0042] A processor is configured to execute the above-mentioned signal detection method based on generalized Gaussian rank correlation in an impulse noise environment.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned signal detection method based on generalized Gaussian rank correlation in an impulse noise environment.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention fully suppresses the harmful effects of large outliers in impulse noise on the signal detection algorithm by performing a generalized Gaussian rank transform on the signal, and exhibits good robustness under environmental noise containing impulse components; in an environment with impulse noise interference, the signal detection method based on generalized Gaussian rank correlation is an effective tool for signal detection with excellent performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the detection process of the detection method of the present invention;

[0047] Figure 2 Schematic diagram of the detection structure of the detection method of the present invention;

[0048] Figure 3 A comparison diagram of detection probabilities of the generalized Gaussian rank correlation detector, the Gaussian rank correlation detector, and the energy detector of the present invention when performing signal detection under impulse noise interference;

[0049] Figure 4 This is a framework diagram of the signal detection system based on generalized Gaussian rank correlation in an impulse noise environment of the present invention. DETAILED DESCRIPTION

[0050] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0051] Embodiment: The present invention provides a signal detection method based on generalized Gaussian rank correlation in an impulse noise environment, comprising the following steps:

[0052] S1. Collecting received signals observed by two receivers and calculating the rank statistics and kurtosis coefficient of the received signals;

[0053] S2. performing a generalized Gaussian rank transform on the received signal according to the rank statistic and the kurtosis coefficient;

[0054] S3, performing correlation operation on the two transformed signals and constructing a test statistic;

[0055] S4. Setting a decision threshold, constructing a decision model based on the decision threshold, inputting the test statistic into the decision model, and determining a signal detection result.

[0056] As a preferred implementation of this embodiment, the specific formula of the received signal observed by the two receivers at sampling time i is as follows:

[0057] x(i)=θ1s(i)+z1(i);

[0058] y(i)=θ2s(i)+z2(i);

[0059] i=1,2,...,n;

[0060] in, is the received signal observed by the two receivers at time i, s(i) is the source signal to be detected, θ l ≤1 indicates the signal gain of the corresponding receiver, z l (i) represents the background noise corresponding to the lth receiver, l = 1, 2 represents the lth receiver, and n is the signal length.

[0061] As a preferred implementation of this embodiment, the calculation of the rank statistic of the received signal can be implemented by the following formula:

[0062]

[0063] i=1,2,...,n;

[0064] Among them, R i represents the rank statistic of the first received signal x(i), Q i represents the rank statistic of the second received signal y(i), n is the signal length, H(·) represents the step function, and the expression of the step function is as follows:

[0065]

[0066] As a preferred implementation of this embodiment, the calculation of the kurtosis coefficients of the two received signals may be implemented by the following formula:

[0067]

[0068] in, represents the sample mean of the first received signal, represents the sample mean of the second received signal, K xIndicates the kurtosis coefficient of the first received signal, K y Indicates the kurtosis coefficient of the second received signal.

[0069] As a preferred implementation of this embodiment, the generalized Gaussian rank transform of the received signals observed by the two receivers is specifically performed as follows:

[0070]

[0071] i=1,2,...,n;

[0072] Among them, Φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function Φ, Indicates the truncation coefficient of the first received signal, represents the truncation coefficient of the second received signal, min represents the minimum value function, max represents the maximum value function, and I(·) represents the indicator function, which is expressed as follows:

[0073]

[0074] As a preferred implementation of this embodiment, the test statistic of performing correlation operation on the two transformed signals and constructing the test statistic is constructed by the following formula:

[0075]

[0076] Wherein, T represents the test statistic, x′ represents the first transformed signal, and y′ represents the second transformed signal.

[0077] As a preferred implementation of this embodiment, the formula for constructing the decision model based on the decision threshold is as follows:

[0078]

[0079] Where T represents the test statistic, τ represents the decision threshold, H0 indicates that the source signal does not exist, and H1 indicates that the source signal exists.

[0080] As a preferred implementation of this embodiment, the specific description of inputting the test statistic into the decision model to determine the signal detection result is as follows: comparing the size of the test statistic and the decision threshold, if the test statistic T is greater than the decision threshold τ, the source signal s exists; if the test statistic T is less than the decision threshold τ, it is considered that the source signal s does not exist.

[0081] Verification Example: In order to analyze the performance of the generalized Gaussian rank correlation detector, the Gaussian rank correlation detector, and the energy detector in signal detection under impulse noise, the present invention will be verified through Monte Carlo experiments:

[0082] Figure 2 is a schematic diagram of the structure of the generalized Gaussian rank correlation detector, where x(1), x(2), ..., x(n) and y(1), y(2), ..., y(n) are the received signals observed by the two receivers, R1, R2, ..., R n and Q1,Q2,...,Q n are the rank statistics of the two received signals, K x and K y are the kurtosis coefficients of the two received signals, x′(1), x′(2), ..., x′(n) and y′(1), y′(2), ..., y′(n) are the signals of the two received signals after generalized Gaussian rank transformation, T is the test statistic constructed by correlation operation of the two transformed signals, and finally the test statistic T is compared with the decision threshold τ to obtain the result of the binary hypothesis test (H0: source signal does not exist or H1: source signal exists).

[0083] The experimental parameters are set as follows:

[0084] The source signal is randomly generated by a signal with a length of n = 500 and obeying the standard normal distribution;

[0085] Impulse noise is modeled by a Gaussian mixture distribution:

[0086]

[0087] Where ε = 0.2 represents the probability of the pulse component occurring in the entire pulse noise environment, and δ2 = 10 >> δ1 represents the standard deviation of the pulse component. In this case, the signal-to-noise ratio of the received signal can be defined as:

[0088]

[0089] From the above, we can see that by comparing and analyzing the performance of the generalized Gaussian rank correlation detector, Gaussian rank correlation detector and energy detector under different signal-to-noise ratios through Monte Carlo experiments, we can verify that the generalized Gaussian rank correlation detector is robust in impulse noise environments. The number of experiments is 10 5 times, false alarm probability P f =0.1, signal attenuation factor θ1=θ2=0.4; the experimental results are as follows Figure 3 As shown;

[0090] Depend on Figure 3 The experimental results show that due to the presence of impulse noise, the detection probability curve of the energy detector is close to a P f= 0.1, completely loses the detection effect, while the Gaussian rank correlation detector and the generalized Gaussian rank correlation detector show robustness to impulse noise; in addition, due to the large intensity of impulse noise (ε = 0.2), the generalized Gaussian rank correlation detector has a higher detection probability than the Gaussian rank correlation detector, thus showing the superiority of the generalized Gaussian rank correlation detector in signal detection under strong impulse noise environment.

[0091] like Figure 4 As shown: A signal detection system based on generalized Gaussian rank correlation in an impulse noise environment, including a signal acquisition module, a preprocessing module, a generalized Gaussian rank transformation module, a correlation operation and test statistic construction module and a judgment module, wherein the signal acquisition module, the preprocessing module, the generalized Gaussian rank transformation module, the correlation operation and test statistic construction module and the judgment module are electrically connected in sequence:

[0092] Signal acquisition module: The signal acquisition module is responsible for collecting the received signals observed by the two receivers;

[0093] A preprocessing module, the preprocessing module is used to calculate the rank statistics and kurtosis coefficient of the received signal;

[0094] A generalized Gaussian rank transformation module, wherein the generalized Gaussian rank transformation module is used to perform a generalized Gaussian rank transformation on the received signal according to the rank statistic and the kurtosis coefficient;

[0095] A correlation operation and test statistic construction module, wherein the correlation operation and test statistic construction module performs a correlation operation on the two transformed signals to construct a test statistic;

[0096] The decision module is used to make a decision on the test statistic based on a set decision threshold to determine the signal detection result.

[0097] An embodiment of the present application provides an electronic device applicable to the signal detection method based on generalized Gaussian rank correlation in the above-mentioned impulse noise environment, including:

[0098] Memory, used to protect computer programs and data;

[0099] Processor, used to run system programs.

[0100] An embodiment of the present application provides a computer storage medium, which is applicable to the signal detection method based on generalized Gaussian rank correlation in the above-mentioned impulse noise environment, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0101] Those skilled in the art will appreciate that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The present application is described with reference to the flow charts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flow charts and / or block diagrams, as well as the combination of the processes and / or boxes in the flow charts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flow charts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0106] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0107] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0108] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, commodity, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, commodity, or apparatus comprising the element.

[0109] The accompanying drawings illustrate various embodiments generally by way of example and not limitation, and together with the description and claims, serve to explain embodiments of the invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive of the embodiments of the present apparatus or method.

[0110] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A signal detection method based on generalized Gaussian rank correlation in an impulse noise environment, characterized by: The following steps are involved: S1. Collecting received signals observed by two receivers and calculating the rank statistics and kurtosis coefficient of the received signals; In step S1, the formula for calculating the rank statistic of the received signal is as follows: i=1,2,...,n; Among them, R i represents the rank statistic of the first received signal x(i), Q i represents the rank statistic of the second received signal y(i), n is the signal length, H(·) represents the step function, and the expression of the step function is as follows: In step S1, the formula for calculating the kurtosis coefficient of the two received signals is as follows: in, represents the sample mean of the first received signal, represents the sample mean of the second received signal, K x Indicates the kurtosis coefficient of the first received signal, K y Indicates the kurtosis coefficient of the second received signal; In step S1, the received signals observed by the two receivers at sampling time i are specifically: x(i)=θ1s(i)+z1(i); y(i)=θ2s(i)+z2(i); i=1,2,...,n; in, is the received signal observed by the two receivers at time i, s(i) is the source signal to be detected, θ l ≤1 indicates the signal gain of the corresponding receiver, z l (i) represents the background noise corresponding to the lth receiver, l = 1, 2 represents the lth receiver, and n is the signal length; S2. performing a generalized Gaussian rank transform on the received signal according to the rank statistic and the kurtosis coefficient; In step S2, the generalized Gaussian rank transform is performed on the received signals observed by the two receivers as follows: i=1,2,...,n; Among them, Φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function Φ, Indicates the truncation coefficient of the first received signal, represents the truncation coefficient of the second received signal, min represents the minimum value function, max represents the maximum value function, and I(·) represents the indicator function, which is expressed as follows: S3, performing correlation operation on the two transformed signals and constructing a test statistic; In step S3, the correlation operation is performed on the two transformed signals to construct the test statistic as follows: Where T represents the test statistic, x′ represents the first transformed signal, and y′ represents the second transformed signal; In step S4, the decision model is constructed based on the decision threshold. The specific description is as follows: Where T represents the test statistic, τ represents the decision threshold, H0 indicates that the source signal does not exist, and H1 indicates that the source signal exists; In step S4, the test statistic is input into the decision model, and the specific description of the signal detection result is determined as follows: compare the test statistic and the decision threshold. If the test statistic T is greater than the decision threshold τ, the source signal s exists; if the test statistic T is less than the decision threshold τ, it is considered that the source signal s does not exist.

2. A signal detection system based on generalized Gaussian rank correlation in an impulse noise environment, applying the method according to claim 1, characterized in that: include: Signal acquisition module: The signal acquisition module is responsible for collecting the received signals observed by the two receivers; A preprocessing module, the preprocessing module is used to calculate the rank statistics and kurtosis coefficient of the received signal; A generalized Gaussian rank transformation module, wherein the generalized Gaussian rank transformation module is used to perform a generalized Gaussian rank transformation on the received signal according to the rank statistic and the kurtosis coefficient; A correlation operation and test statistic construction module, wherein the correlation operation and test statistic construction module performs a correlation operation on the two transformed signals to construct a test statistic; The decision module is used to make a decision on the test statistic based on a set decision threshold to determine the signal detection result.

3. A processor, characterized in that: The method is configured to execute the signal detection method based on generalized Gaussian rank correlation in an impulse noise environment according to claim 1.

4. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the signal detection method based on generalized Gaussian rank correlation in an impulse noise environment as claimed in claim 1 is implemented.

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