A method and system for decomposing mixed signals in a strong noise background and a storage medium

Abstract

This invention provides a method, system, and storage medium for decomposing mixed signals under strong noise background. The method includes: Step 1, constructing a database containing various signals; Step 2, calculating the residual R obtained by subtracting the signals in the database from the analog signal to be detected. k The NS value, if the residual R k If the NS value of the subtracted database signal decreases the most compared to the NS value of the analog signal, it indicates that the subtracted database signal is a component of the analog signal; if the residual R k If the NS value of the analog signal does not decrease compared to the NS value of the analog signal, then it indicates that the signal X in the database... k If the signal is not a component of the analog signal, the operation ends. Step 3: If the decreased NS value is 0, it means all signals in the analog signal have been found, and the operation ends. If the decreased NS value is not 0, it indicates the signal is still non-stationary, meaning there are still signals in the analog signal that haven't been found. Subtract the signals from the database from the analog signal and repeat step 2. This invention utilizes the characteristic that the non-stationarity degree (NS) method is unaffected by perturbation data distribution to achieve matching detection between signals hidden by noise and signals in the constructed database.

Description

Technical Field

[0001] This invention relates to the field of signal detection technology, specifically a method, system, and storage medium for decomposing mixed signals under strong noise background. Background Technology

[0002] Currently, under the strong noise of the magnetic background of ocean diversity, weak target signals are submerged by colored noise, Gaussian white noise, and other noises, making them impossible to detect effectively.

[0003] The existing main solution is to use a detection algorithm based on orthogonal basis function decomposition, which is suitable for Gaussian white noise. For anomalous signals contaminated by Gaussian white noise, the target detection algorithm based on orthogonal basis function decomposition has a low false alarm rate, but there are still errors in signal discrimination. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and storage medium for decomposing mixed signals under strong noise background. This method performs matching identification and decomposition of mixed signals contained in strong noise background. It utilizes the characteristic of the non-stationarity degree (NS) method that is not affected by the distribution of disturbed data to achieve matching detection of signals hidden in noise and signals in the constructed database.

[0005] The technical solution of this invention is to provide a method for decomposing mixed signals under strong noise background, including the following steps:

[0006] Step 1: Build a database containing various signals;

[0007] Step 2: Calculate the residual R obtained by subtracting the signal in the database from the analog signal to be detected. k The NS value, if the residual R k If the NS value of the subtracted database signal decreases the most compared to the NS value of the analog signal, it indicates that the subtracted database signal is a component of the analog signal; if the residual R k If the NS value of the analog signal does not decrease compared to the NS value of the analog signal, then it indicates that the signal X in the database... k If the signal is not a component of an analog signal, the operation ends.

[0008] Step 3: If the NS value after the drop is 0, it means that all signals in the analog signal have been found, and the operation ends; if the NS value after the drop is not 0, it means that the signal is still unstable, and there are still signals in the analog signal that have not been found. Subtract the signals in the database from the analog signal and repeat the operation of Step 2.

[0009] A system for decomposing mixed signals in a noisy environment includes: a computer-readable storage medium and a processor;

[0010] The computer-readable storage medium is used to store executable instructions;

[0011] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the method for decomposing mixed signals under strong noise background.

[0012] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for decomposing mixed signals in a strong noise background.

[0013] The advantages of this invention are as follows:

[0014] 1) Signal identification and decomposition are very convenient, and signal detection time is short;

[0015] 2) Various non-stationary signals that reach a certain intensity can be detected. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the analog signal and its constituent signals according to Embodiment 1 of the present invention;

[0017] Figure 2 This is a schematic diagram of the analog signal and its constituent signals according to Embodiment 2 of the present invention;

[0018] Figure 3 This is a schematic diagram of the simulated signal and its constituent signals according to Embodiment 3 of the present invention;

[0019] Figure 4 This is a schematic diagram of the analog signal and its constituent signals in Embodiment 4 of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The inventors of this application discovered during the research process of realizing this invention that although the marine environment is complex, many non-stationary signals in the ocean have been recorded. Therefore, a method for matching signals can be provided, which can identify various non-stationary mixed signals in the presence of strong Gaussian noise in the ocean, and can distinguish the primary and secondary status of various signals. Compared with detection, the signal matching method has no error. A relatively complete theory and method for calculating the non-stationarity of data streams was published in 2013 (Tan Qiuheng, Measurement of Non-stationarity of Time Series and Its Application (D), Doctoral Dissertation (Supervisor Ding Yiming), University of Chinese Academy of Sciences, 2013). A non-stationarity metric for a data stream is a real number between 0 and 1, independent of the noise distribution, and depends only on the stationarity of the data: the closer the value is to 0, the more stationary the data stream; the closer it is to 1, the less stationary the data stream.

[0022] If we denote the target signal as X1, adding Gaussian white noise ε yields the masked analog signal X = X1 + ε. The signal located in the database is denoted as {X... k}, k = 1, 2, ..., n. Consider the residual R. k =XX k =(X1-X k If the k-th signal X + ε, k If R is the same as the target signal X1, then k That is, the noise ε added earlier; otherwise, R k The signal X1-X contains the difference between the target signal and the signal in the database. k Nonstationarity measures (NS) can detect how close the residuals are to noise, thus enabling the aforementioned discrimination problem.

[0023] The following is a detailed description using specific embodiments:

[0024] Example 1

[0025] This invention uses computer software technology to implement the operation process, which includes the following steps:

[0026] 1. Construct a database {Y1(t), Y2(t), Y3(t), Y4(t), Y5(t), Y6(t), Y7(t), Y8(t), Y9(t)}, where the analog signal is Y(t) = Y1(t) + Y2(t) + ε(t). Assume that... ε(t) is Gaussian white noise with a signal-to-noise ratio of 2, t = 1, ... 3000, and the analog signal and its constituent signals are as follows: Figure 1 As shown.

[0027] 2. Calculate Y(t) minus the NS value of the signal in the database. If the non-stationary value of the signal decreases rapidly, then the signal is a component of the analog signal. The results are shown in Table 1.

[0028] Table 1

[0029]

[0030] The NS value of Y(t)-Y4(t) is 0.33, which can be determined to be a component of Y(t). Therefore, let Y′(t) = Y(t)-Y4(t) and continue to search for the signal.

[0031] 3. Repeat step 2 and use Y′(t) for detection. The results are shown in Table 2.

[0032] Table 2

[0033]

[0034] 4. The NS value of Y′(t)-Y1(t) is 0, indicating that Y1(t) is a component of Y(t). Y(t) contains signals Y1(t) and Y4(t). Since Y4(t) is matched and identified first, it is the primary trend signal, while Y1(t) is the secondary trend signal. Y1(t) is an elementary function, and Y4(t) is a power function; both are non-stationary signals with trends on t = 1, ... 3000. The initial NS value of the analog signal is 0.97, indicating a non-stationary signal. After detecting signals Y1(t) and Y4(t), the remaining signal is Gaussian white noise with a signal-to-noise ratio of 2 (ε(t)), with an NS value of 0, indicating a stationary signal.

[0035] Example 2

[0036] This invention uses computer software technology to implement the operation process, which includes the following steps:

[0037] 1. Construct a database {Y1(t), Y2(t), Y3(t), Y4(t), Y5(t), Y6(t), Y7(t), Y8(t), Y9(t)}, where the analog signal is Y(t) = Y2(t) + Y6(t) + ε(t). Assume that Y2(t) = t / 400. ε(t) is Gaussian white noise with a signal-to-noise ratio of 2, t = 1, ... 1000, and the analog signal and its constituent signals are as follows: Figure 2 As shown.

[0038] 2. Calculate Y(t) minus the NS value of the signal in the database. If the non-stationary value of the signal decreases rapidly, then the signal is a component of the analog signal. The results are shown in Table 3.

[0039] Table 3

[0040]

[0041] The NS values ​​of Y(t)-Y2(t) are 0.41 and Y(t)-Y6(t) are 0.43. It can be determined that Y2(t) and Y6(t) are components of X(t). Therefore, let Y′(t) = Y(t)-Y2(t)-Y6(t) and continue to search for signals.

[0042] 5. Repeat step 2 and use Y′(t) for detection. The results are shown in Table 4.

[0043] Table 4

[0044]

[0045] No other component signals of Y(t) can be found in the database. Therefore, Y(t) can be decomposed into signals Y2(t) and Y6(t). Since Y2(t) and Y6(t) are identified simultaneously, they are signals with similar trends. Y2(t) is an elementary function, and Y6(t) is a logarithmic function; both are non-stationary signals with trends on t = 1, ... 1000. The initial NS value of the analog signal is 0.97, indicating a non-stationary signal. After detecting signals Y2(t) and Y6(t), the remaining signal is Gaussian white noise with a signal-to-noise ratio of 2 (ε(t)), and the NS value is 0, indicating a stationary signal.

[0046] Example 3

[0047] This invention uses computer software technology to implement the operation process, which includes the following steps:

[0048] 1. Construct a database {Y1(t), Y2(t), Y3(t), Y4(t), Y5(t), Y6(t), Y7(t), Y8(t), Y9(t)}, where the analog signal is Y(t) = Y3(t) + Y5(t) + ε(t). Assume that... ε(t) is Gaussian white noise with a signal-to-noise ratio of 2, t = 1, ... 3000, and the analog signal and its constituent signals are as follows: Figure 3 As shown.

[0049] 2. Calculate Y(t) minus the NS value of the signal in the database. If the non-stationary value of the signal decreases rapidly, then the signal is a component of the analog signal. The results are shown in Table 5.

[0050] Table 5

[0051]

[0052] The NS value of Y(t)-Y5(t) is 0.38, which indicates that Y5(t) is a component of Y(t). Therefore, let Y′(t) = Y(t)-Y5(t) and continue searching for the signal.

[0053] 3. Repeat step 2 and use Y′(t) for detection. The results are shown in Table 6.

[0054] Table 6

[0055]

[0056] 4. The NS value of Y′(t)-Y3(t) is 0, indicating that Y3(t) is a component of Y(t). Y(t) contains signals Y3(t) and Y5(t). Since Y5(t) is matched and identified first, it is the primary trend signal, while Y3(t) is the secondary trend signal. Y3(t) is a trigonometric function and is a stationary signal, while Y5(t) is a power function, but both are non-stationary signals with trends on t=1, ...3000. The initial NS value of the analog signal is 0.97, indicating a non-stationary signal. After detecting signals Y3(t) and Y5(t), the remaining signal is Gaussian white noise with a signal-to-noise ratio of 2 (ε(t)), with an NS value of 0, indicating a stationary signal.

[0057] Example 4

[0058] This invention uses computer software technology to implement the operation process, which includes the following steps:

[0059] 1. Construct a database {Y1(t), Y2(t), Y3(t), Y4(t), Y5(t), Y6(t), Y7(t), Y8(t), Y9(t)}, where the analog signal is Y(t) = Y7(t) + Y8(t) + Y9(t) + ε(t). Assume that... ε(t) is Gaussian white noise with a signal-to-noise ratio of 2, t = 1, ... 3000, and the analog signal and its constituent signals are as follows: Figure 4 As shown.

[0060] 2. Calculate Y(t) minus the NS value of the signal in the database. If the non-stationary value of the signal decreases rapidly, then the signal is a component of the analog signal. The results are shown in Table 7.

[0061] Table 7

[0062]

[0063] The NS value of Y(t)-Y7(t) is 0.50, which indicates that Y7(t) is a component of Y(t). Therefore, let Y′(t) = Y(t)-Y7(t) and continue searching for the signal.

[0064] 3. Repeat step 2 and use Y′(t) for detection. The results are shown in Table 8.

[0065] Table 8

[0066]

[0067] The NS values ​​of Y′(t)-Y8(t) and Y′(t)-Y9(t) are both 0, indicating that Y8(t) and Y9(t) are components of Y′(t). Therefore, let Y″(t) = Y′(t)-Y8(t)-Y9(t) and continue searching for the signal.

[0068] 4. Repeat step 2 and use Y″(t) for detection. The results are shown in Table 9.

[0069] Table 9

[0070]

[0071] The NS value of Y″(t) is 0, so signals Y7(t), Y8(t), and Y9(t) exist within Y(t). Since Y7(t) is matched and identified first, it is the primary trend signal, while Y8(t) and Y9(t) are secondary trend signals. Y7(t) is a logarithmic function, Y8(t) is a trigonometric function, and Y9(t) is a trigonometric function. All three are non-stationary signals with trends on t = 1, ... 3000. The initial NS value of the analog signal is 0.97, indicating a non-stationary signal. After detecting signals Y7(t), Y8(t), and Y9(t), the remaining signal is Gaussian white noise with a signal-to-noise ratio of 2, and the NS value is 0, indicating a stationary signal.

[0072] This invention can not only perform blind matching and identification of various mixed signals under strong noise in the ocean, but can also be applied to identity authentication, group confidentiality, etc.

[0073] Another aspect of the present invention provides a system for decomposing mixed signals under strong noise background, comprising: a computer-readable storage medium and a processor;

[0074] The computer-readable storage medium is used to store executable instructions;

[0075] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the method for decomposing mixed signals under strong noise background.

[0076] Another aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for decomposing mixed signals under strong noise background.

[0077] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for decomposing mixed signals under strong noise background, characterized in that, Includes the following steps: Step 1: Build a database containing various signals; Step 2: Calculate the residuals obtained by subtracting the signals in the database from the analog signals to be detected. The NS value, if the residual If the NS value of the subtracted database signal decreases the most compared to the NS value of the analog signal, it indicates that the subtracted database signal is a component of the analog signal; if the residual... If the NS value of the analog signal does not decrease compared to the NS value of the analog signal, then it indicates that the signal in the database... If the signal is not a component of the analog signal, the operation ends, where the NS value is the non-stationarity value. Step 3: If the NS value after the drop is 0, it means that all signals in the analog signal have been found, and the operation ends; if the NS value after the drop is not 0, it means that the signal is still unstable, and there are still signals in the analog signal that have not been found. Subtract the signals in the database from the analog signal and repeat the operation of Step 2.

2. A system for decomposing mixed signals under strong noise background, comprising: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the method for decomposing mixed signals under strong noise background as described in claim 1.

3. A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for decomposing mixed signals under strong noise background as described in claim 1.

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