Compressed sensing-based mixed domain dual-mode signal detection method and related equipment

Abstract

The invention discloses a mixed domain dual-mode signal detection method based on compressed sensing and related equipment, and the method comprises the steps: obtaining a to-be-detected original signal, carrying out the preprocessing of the original signal, and obtaining a preprocessed original signal; performing double-domain sparse decomposition on the preprocessed original signal to obtain time domain output and frequency domain output; performing compressed sampling on the time domain output and the frequency domain output to obtain a compressed sampling result; and performing signal reconstruction according to the compressed sampling result to obtain a reconstructed signal, and performing signal detection based on the reconstructed signal to obtain a signal detection result of the original signal. Feature extraction is accurately carried out on collected original signals in dual-mode communication by utilizing dual-domain sparse decomposition to obtain time-domain high-frequency features and frequency-domain low-frequency features, so that accurate separation of the original signals is realized, the calculation complexity of signal reconstruction after compression sampling is reduced, the calculation efficiency of reconstruction is improved, and the method is suitable for large-scale popularization and application. And more accurate signal reconstruction also improves the accuracy of signal detection.

Description

Technical Field

[0001] This application relates to the technical field of dual-mode communication in the electricity meter industry, and particularly to a method for detecting hybrid-domain dual-mode signals based on compressive sensing and related devices. Background Art

[0002] When traditional compressive sensing technology is used for dual-mode communication based on high-speed power line carrier (HPLC) and high-speed radio frequency (HRF) in the electricity meter industry, there are certain problems, such as inaccurate signal detection. There are many reasons for inaccurate signal detection, including incomplete denoising of the original signal, high computational complexity resulting in low computational efficiency, inability to simultaneously adapt to the time-frequency characteristics of power line carrier (HPLC) and the impulse response of wireless communication (HRF), and inaccurate signal reconstruction.

[0003] Therefore, there is an urgent need for a signal detection method that can improve the accuracy of signal detection in the power industry. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a method for detecting hybrid-domain dual-mode signals based on compressive sensing and related devices, so as to solve the technical problem of low accuracy of signal detection in the electricity meter industry based on dual-mode communication in related technologies.

[0005] In a first aspect, the embodiments of this application provide a method for detecting hybrid-domain dual-mode signals based on compressive sensing, including:

[0006] Obtain the original signal to be detected, and preprocess the original signal to obtain the preprocessed original signal;

[0007] Perform dual-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output;

[0008] Perform compressive sampling on the time-domain output and the frequency-domain output to obtain a compressive sampling result;

[0009] Perform signal reconstruction according to the compressive sampling result to obtain a reconstructed signal, and perform signal detection based on the reconstructed signal to obtain a signal detection result for the original signal.

[0010] In a second aspect, the embodiments of this application provide a device for detecting hybrid-domain dual-mode signals based on compressive sensing, including:

[0011] A first processing module, configured to obtain the original signal to be detected, and preprocess the original signal to obtain the preprocessed original signal;

[0012] A second processing module, configured to perform dual-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output;

[0013] A compressive sampling module, configured to perform compressive sampling on the time-domain output and the frequency-domain output to obtain a compressive sampling result;

[0014] An inspection and processing module, configured to perform signal reconstruction according to the compressive sampling result, and perform signal detection based on the obtained reconstructed signal to obtain a signal detection result of the original signal.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a computer program or an embedded program stored in the internal storage of the memory and executable on the processor. When the processor executes the computer program or the embedded program, the steps in the detection method of the hybrid-domain dual-mode signal based on compressive sensing described in any one of the above are implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program or an embedded program. When the computer program or the embedded program is executed by a processor, the steps in the detection method of the hybrid-domain dual-mode signal based on compressive sensing described in any one of the above are implemented.

[0017] An embodiment of the present application provides a detection method and related devices for a hybrid-domain dual-mode signal based on compressive sensing. When performing signal detection and processing, first obtain an original signal to be detected, and perform preprocessing on the original signal to obtain a preprocessed original signal. Then perform dual-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output. Next, perform compressive sampling on the time-domain output and the frequency-domain output to obtain a compressive sampling result. Finally, perform signal reconstruction according to the compressive sampling result to obtain a reconstructed signal, and perform signal detection based on the reconstructed signal to obtain a signal detection result of the original signal. When detecting and analyzing signals in dual-mode communication, use dual-domain sparse decomposition to accurately extract the time-domain high-frequency features and frequency-domain low-frequency features in the original signal, realize the accurate separation of the original signal. At the same time, sparse decomposition can reduce the computational complexity when performing signal reconstruction after compressive sampling, improve the computational efficiency of reconstruction, and can construct a more accurate signal through accurate extraction of signal features, improving the accuracy of signal detection. Description of the Drawings

[0018] Figure 1 is a flowchart of the steps of the detection method of the hybrid-domain dual-mode signal based on compressive sensing provided by an embodiment of the present application;

[0019] Figure 2 is a flowchart of the steps of obtaining the compressive sampling result provided by an embodiment of the present application;

[0020] Figure 3 is a schematic flow chart of a step for obtaining a coupling matrix provided by an embodiment of the present application;

[0021] Figure 4 is a schematic structural diagram of a detection device for a hybrid-domain dual-mode signal based on compressive sensing provided by an embodiment of the present application;

[0022] Figure 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0023] Figure 6 is another schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope protected by the present application.

[0025] It should be understood that the steps recorded in the method embodiments disclosed in the present application can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope disclosed in the present application is not limited in this regard.

[0026] As used herein, the term "including" and its variants are open-ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0027] In the related art, in the dual-mode communication based on high-speed power line carrier (HPLC) and high-speed radio frequency (HRF) in the electricity meter industry, there are certain problems with traditional compressive sensing technology, such as inaccurate signal detection. There are many reasons for inaccurate signal detection, including incomplete denoising of the original signal, high computational complexity resulting in low computational efficiency, inability to simultaneously adapt to the time-frequency characteristics of power line carrier (HPLC) and the impulse response of wireless communication (HRF), and inaccurate signal reconstruction.

[0028] To solve the technical problems existing in the related art, an embodiment of the present application provides a method for detecting a hybrid-domain dual-mode signal based on compressive sensing. Please refer to Figure 1 , Figure 1It is a schematic flowchart of the steps of the detection method for hybrid-domain dual-mode signals based on compressive sensing provided by an embodiment of the present application. The method includes steps 101 to 104.

[0029] Step 101: Obtain the original signal to be detected, and perform preprocessing on the original signal to obtain the preprocessed original signal.

[0030] In one embodiment, when detecting the collected signal, first perform preprocessing on the collected signal, including but not limited to filtering the noise contained in the signal, etc. Therefore, after obtaining the original signal to be detected, perform preprocessing on the original signal to obtain the processed original signal. Among them, the original signal is the signal in the electricity meter industry based on dual-mode communication.

[0031] Exemplarily, when preprocessing the original signal, the noise of the original signal can be filtered, and at the same time, the original signal after noise filtering is unified and standardized to reduce the influence of the noise not completely filtered on subsequent detections.

[0032] Specifically, when preprocessing the original signal, it includes: performing band-pass filtering on the original signal to obtain the original signal after band-pass filtering; performing normalization on the filtered original signal to obtain the processed original signal.

[0033] In fact, the main purpose of preprocessing is to denoise the original signal, reduce the influence of noise in the signal on subsequent detections, and at the same time, it can also reduce the computational amount of subsequent detection calculations and improve the computational efficiency. Therefore, when performing preprocessing, first denoise the original signal, filter out most of the noise that can be removed, and then perform normalization to obtain the preprocessed original signal.

[0034] In the actual processing process, when performing band-pass filtering on the original signal, the Butterworth second-order method is used for processing, and the mathematical model used is as follows:

[0035] (Suppress 45 - 55Hz power frequency interference);

[0036] Among them, when implemented based on C language, the implementation code is as follows:

[0037]

[0038] Among them, b = [0.0003, 0, -0.0003]: numerator coefficient, controlling the passband range; a = [1, -1.9997, 0.9997]: Denominator coefficient, determining the stopband attenuation slope (-40 dB / dec); s = jω: Complex frequency variable (ω = 2πf, f is the actual frequency).

[0039] When performing normalization processing, the signal data is processed based on the following data formula:

[0040] (Output range [-1, 1]);

[0041] Among them, max(|x|) is the maximum value of the absolute value of the signal (to prevent overflow).

[0042] And when implementing based on C language, the implementation code is as follows:

[0043]

[0044] Through the above preprocessing of the original signal, the noise contained in the original signal can be effectively removed. For the noise that cannot be effectively removed, the influence of the partially unremoved noise can be reduced through normalization processing. At the same time, the normalization processing can provide more stable values for subsequent processing and improve the efficiency of subsequent decomposition and calculation.

[0045] Step 102, perform double-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output.

[0046] In one embodiment, after preprocessing the original signal, the preprocessed original signal will be decomposed. Specifically, perform double-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output. Among them, when performing double-domain sparse decomposition on the original signal, it is decomposed in the time domain and the frequency domain respectively, and the time-domain output and the frequency-domain output are obtained based on the constructed sparsity.

[0047] Actually, when performing double-domain sparse decomposition on the processed original signal, the separation of high-frequency (HPLC) components and low-frequency (HRF) components is achieved through the decomposition process, and sparse representation is realized (such as 5% time-domain sparsity and 3% frequency-domain sparsity).

[0048] Exemplarily, when performing double-domain sparse decomposition on the preprocessed original signal, the preprocessed original signal is processed independently and separately in the time domain and the frequency domain. Therefore, when processing the preprocessed original signal to obtain a time-domain output and a frequency-domain output, it includes: performing time-domain decomposition on the preprocessed original signal, and extracting high-frequency signal features from the first decomposition result obtained by the time-domain decomposition to obtain a time-domain output; performing frequency-domain decomposition on the preprocessed original signal, and extracting low-frequency signal features from the second decomposition result obtained by the frequency-domain decomposition to obtain a frequency-domain output.

[0049] That is, by separately obtaining the time-domain signal and frequency-domain signal of the processed original signal, then processing them separately in the time domain and frequency domain, and extracting signal features according to different requirements, corresponding time-domain output and frequency-domain output are obtained.

[0050] Among them, when extracting the characteristic signal from the first decomposition result obtained by time-domain decomposition, a high-frequency signal can be obtained. Specifically, when performing time-domain decomposition, the mathematical formula used is as follows:

[0051]

[0052] Among them, θ = π / 3, which is the fractional-order rotation angle to balance the time-frequency resolution; N = 1024, which is the signal length to match the power line communication frame structure; m and n are time-frequency joint domain indices (0 ≤ m, n < N)

[0053] When implemented based on C language, the implementation code is as follows:

[0054]

[0055] When extracting the physical sign signal from the second decomposition result obtained by frequency-domain decomposition, a low-frequency signal can be obtained. Specifically, when performing frequency-domain decomposition (Daubechies wavelet), the mathematical formula used is as follows:

[0056] ψ j,k (t) = 2 j / 2 ψ(2 j t - k);

[0057] Among them, j = 4, which is the wavelet scale parameter to match the low-frequency characteristics of the HRF signal, and ψ is the Daubechies-4 wavelet basis function (with 4th-order vanishing moments).

[0058] When implemented based on C language, the implementation code is as follows:

[0059]

[0060] In practical applications, the time-domain output α_h and frequency-domain output α_w obtained from the dual-domain sparse decomposition will be used as the input for compressive sampling in subsequent processing. By performing sparsity processing on the time-domain output and frequency-domain output (5% for the time domain / 3% for the frequency domain), the data dimension can be effectively reduced and the processing efficiency can be improved.

[0061] Step 103: Perform compressive sampling on the time-domain output and frequency-domain output to obtain the compressive sampling result.

[0062] In one embodiment, after performing dual-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output, compression sensing sampling processing is performed on the obtained time-domain output and frequency-domain output to obtain a compressed sampling result when the compression sampling processing is completed.

[0063] Exemplarily, after obtaining the time-domain output and the frequency-domain output, compression sensing technology is used to perform compression sampling processing on the time-domain output and the frequency-domain output, which can effectively reduce the amount of data in subsequent calculation processing, thereby improving the efficiency of subsequent calculation processing.

[0064] When performing compression sampling processing on the time-domain output and the frequency-domain output, reference can be made to Figure 2 , Figure 2 which is a schematic flow diagram of a step for obtaining a compressed sampling result provided by an embodiment of the present application. Among them, this step includes steps 201 to 203.

[0065] Step 201: Construct a time-domain matrix and a frequency-domain matrix based on the error feedback result during matrix construction, and construct a coupling matrix according to the time-domain matrix and the frequency-domain matrix;

[0066] Step 202: Obtain an output matrix after dual-domain sparse decomposition of the original signal according to the time-domain output and the frequency-domain output;

[0067] Step 203: Calculate the compressed sampling result corresponding to the original signal according to the coupling matrix and the output matrix.

[0068] Specifically, when performing compression sampling processing, first construct a time-domain matrix and a frequency-domain matrix required for compression sampling calculation to obtain a coupling matrix for compression sampling. At the same time, obtain an output matrix after dual-domain sparse decomposition of the original signal according to the obtained time-domain output and frequency-domain output. Finally, perform calculations according to the obtained coupling matrix and output matrix to obtain the compressed sampling result corresponding to the original signal.

[0069] Exemplarily, when constructing the time-domain matrix and the frequency-domain matrix, generally, the time-domain matrix is constructed based on a 102×1024 Toeplitz matrix, and the frequency-domain matrix is constructed based on a 102×1024 partial Fourier matrix. In practical applications, to improve the accuracy of subsequent signal reconstruction, the matrix can be constructed in different ways for signals in different channel modes. The channel modes include the HPLC mode and the HRF mode. In different modes, the requirements for high-frequency and low-frequency features in the signal for signal reconstruction are different, and different requirements can make the reconstructed signal have better accuracy. Therefore, the basis for the matrices used in different modes is different. Among them, in the HPLC mode, a Toeplitz matrix (matching the time-domain correlation of power line impulse noise) can be used to construct the corresponding time-domain matrix, and in the HRF mode, a Gaussian random matrix (adapting to the frequency-domain flat fading characteristics of the wireless channel) can be used to construct the corresponding time-domain matrix. For the constructed frequency-domain matrix, it is constructed based on a 102×1024 partial Fourier matrix regardless of the mode.

[0070] Therefore, when constructing the time-domain matrix and the frequency-domain matrix to obtain the coupling matrix, reference can be made to Figure 3 , Figure 3 which is a schematic flowchart of a step for obtaining the coupling matrix provided by an embodiment of the present application. The step includes steps 301 to 303.

[0071] Step 301, determine the channel mode of the original signal, where the channel mode includes the HPLC mode and the HRF mode;

[0072] Step 302, construct the time-domain matrix according to the error feedback result during matrix construction and the channel mode, and construct the frequency-domain matrix according to the error feedback result during matrix construction;

[0073] Step 303, perform coupling processing on the time-domain matrix and the frequency-domain matrix to obtain the corresponding coupling matrix.

[0074] Among them, the channel mode is determined based on the current state of the HPLC channel. When the signal-to-noise ratio of the HPLC channel < 15 dB or the bit error rate of the HRF channel > 1e-5, it will be triggered to enter the HRF mode, or when a power outage event occurs, the HRF channel will be forced to be enabled to enter the HRF mode, and other situations can be regarded as being in the HPLC mode.

[0075] Furthermore, when performing compressive sampling, first determine the current channel mode to determine the matrix basis for current matrix construction, and obtain the time-domain matrix and frequency-domain matrix constructed based on the error feedback result and channel mode during matrix construction. Among them, the frequency-domain matrix is independent of the channel mode, and the time-domain matrix is related to the channel mode. Finally, after constructing the time-domain matrix and frequency-domain matrix, perform even coupling processing on the two matrices to obtain the corresponding coupling matrix for subsequent calculation processing.

[0076] In practical applications, assume that the time-domain matrix is Φ h , and the frequency-domain matrix is Φ w . At this time, when coupling the two matrices, the obtained coupling matrix is as follows:

[0077]

[0078] Meanwhile, the output matrix obtained based on the time-domain output and frequency-domain output is as follows:

[0079]

[0080] Then, the compressive sampling result obtained by calculating based on the coupling matrix and the output matrix is as follows:

[0081]

[0082] That is, after performing compressive sampling, the time-domain measurement y h = Φ h * a h , where Φ h is a 102×1024 Toeplitz matrix, and the frequency-domain measurement y w = Φ w * a w , where Φ w is a 102×1024 partial Fourier matrix. The final observation value: y = [y h ; y w (dimension 204×1).

[0083] Meanwhile, when implementing based on C language, the implementation code is as follows:

[0084]

[0085]

[0086] Further, based on the above description, when constructing the time-domain matrix and the frequency-domain matrix, in addition to constructing based on the current channel mode, it is also necessary to determine based on the error feedback result during matrix construction. That is, the constructed time-domain matrix and frequency-domain matrix are dynamic matrices, which are determined according to the actual signal transmission situation, specifically determined by the error of sampling and processing historical signals. Among them, the calculation formula for obtaining the error feedback result can be as follows:

[0087]

[0088] Where e i is the historical error sequence (recording the errors of the last k samplings), α = 0.1, which is the power frequency interference attenuation factor.

[0089] When implemented based on C language, the implementation code is as follows:

[0090]

[0091] After completing the compressive sampling processing of the time-domain output and the frequency-domain output based on the above steps, the result for subsequent signal reconstruction is obtained, that is, the compressive sampling result.

[0092] Step 104, perform signal reconstruction based on the compressive sampling result to obtain a reconstructed signal, and perform signal detection based on the reconstructed signal to obtain the signal detection result of the original signal.

[0093] In an embodiment, after completing the compressive sampling processing of the time-domain output and the frequency-domain output, signal reconstruction processing will be performed, and then signal detection processing will be performed based on the signal obtained by reconstruction to obtain the signal detection result of the original signal. Therefore, after obtaining the compressive sampling result, signal reconstruction processing can be performed based on the corresponding method, and when it is determined that the reconstructed signal meets the set relevant conditions, signal detection processing is performed based on the reconstructed signal to obtain the corresponding signal detection result.

[0094] Exemplarily, when performing signal reconstruction, joint reconstruction can be performed based on the Alternating Direction Method of Multipliers (ADMM) to obtain the reconstructed signal corresponding to the original signal. When performing joint reconstruction based on ADMM, reconstruction is performed by means of block iteration and soft threshold constraint to recover the original signal, and the reconstructed signal reconstructed and recovered at this time can reduce the computational complexity to O(N 1. ), effectively reducing the computational complexity and improving the computational efficiency.

[0095] Specifically, when performing signal reconstruction, it includes: separately performing signal extraction processing on the time-domain signal and the frequency-domain signal in the compressive sampling result according to the channel mode to obtain the processed compressive sampling result; performing signal reconstruction on the compressive sampling result based on the alternating direction multiplier method, and determining whether the signal reconstruction is completed based on the objective function for signal reconstruction and the soft threshold constructed; when it is determined that the signal reconstruction is completed according to the objective function and the soft threshold, the reconstructed signal is obtained.

[0096] In practical applications, the requirements for high-frequency and low-frequency signals are different under different channel modes. For example, in the HPLC mode, more high-frequency details are required, that is, more high-frequency signals are needed. Therefore, when performing signal reconstruction, more time-domain signals with high-frequency characteristics and a small amount of frequency-domain signals with low-frequency characteristics can be obtained. Another example is that in the HRF mode, a small amount of high-frequency signals are required. Therefore, when performing signal reconstruction, a small amount of time-domain signals with high-frequency characteristics and more frequency-domain signals with low-frequency characteristics can be obtained.

[0097] Therefore, when performing signal reconstruction, by determining the current channel mode, the signals for signal reconstruction in the compressive sampling result are determined, including the time-domain signal and the frequency-domain signal, and then the signal reconstruction process is carried out based on the set reconstruction method, and the reconstructed signal is obtained when it is determined that the reconstruction is completed.

[0098] Since the reconstruction process is an iterative process, when determining whether the reconstruction is completed, the objective function and the soft threshold are introduced as the judgment basis for whether the reconstruction is completed.

[0099] Among them, the mathematical formula of the objective function can be as follows:

[0100]

[0101] Among them, λ1 = 0.1 is the time-domain sparsity weight (HPLC signal is sparser), λ2 = 0.05 is the frequency-domain sparsity weight (HRF signal has stronger continuity), and ‖.‖1 is the L1 norm (forcing sparsity).

[0102] When implementing based on C language, the implementation code is as follows:

[0103]

[0104]

[0105] In addition, for the variables in the reconstructed signal during reconstruction, when updating the variables, the mathematical formula used is as follows:

[0106]

[0107] Among them, ρ = 1.5, which is the ADMM penalty factor (controlling the convergence speed), and z h and z w are auxiliary variables (soft threshold output), and u h and u w are dual variables (Lagrange multipliers).

[0108] Furthermore, after the reconstruction of the original signal is completed, subsequent detection processing will be performed based on the reconstructed signal obtained from the reconstruction. When performing signal detection, it includes: performing phase synchronization detection and consistency detection on the time-domain signal and frequency-domain signal in the reconstructed signal to obtain the synchronization detection result and the consistency index; obtaining the detection result of the reconstructed signal according to the synchronization detection result and the consistency index, and using the detection result as the signal detection result of the original signal.

[0109] Specifically, when performing signal detection, phase synchronization detection and consistency detection will be performed. Among them, when performing phase synchronization detection, the pre-constructed phase synchronization error model can be used, and the formula of this phase synchronization error model is as follows:

[0110]

[0111] Among them, t std (i) is the zero-crossing time of the Beidou clock reference, and t test (i) is the zero-crossing time of the signal to be measured.

[0112] Through the phase synchronization detection, the alignment accuracy of the signal in time sequence can be verified to ensure the synchronization of the time-domain signal and frequency-domain signal in the dual-mode communication.

[0113] In addition, when performing consistency detection, the consistency index (CI) is calculated through the used consistency calculation formula. Among them, the used consistency calculation formula is as follows:

[0114] (threshold CI linit ≤ 3);

[0115] Among them, μ(i) is the mean value of the reference signal at wavelength point i, and σ(i) is the standard deviation of the reference signal at wavelength point i.

[0116] Through the consistency detection, the consistency of the detection spectrum shape can be determined, and the industrial frequency interference residue or protocol violation can be effectively identified and processed.

[0117] In addition, when performing phase synchronization detection and consistency detection, the code used when implementing based on C language code is as follows:

[0118]

[0119] Further, after obtaining the reconstructed signal, gradient update can also be performed based on the reconstructed signal, and the gradient during preprocessing in step 101 can be updated using the updated gradient. Therefore, after obtaining the reconstructed signal, gradient update can also be carried out. The update formula used during gradient update is as follows:

[0120]

[0121] where s k is a reference signal segment (HRF heartbeat signal, length L = 100), and η k is an adaptive step size (to prevent gradient explosion).

[0122] Among them, when implementing gradient update based on C language, the implementation code is as follows:

[0123]

[0124] In summary, the above embodiments provide a detection method for hybrid-domain dual-mode signals based on compressive sensing. When performing signal detection processing, first, the original signal to be detected is obtained, and the original signal is preprocessed to obtain the preprocessed original signal. Then, the preprocessed original signal is subjected to dual-domain sparse decomposition to obtain a time-domain output and a frequency-domain output. Next, the time-domain output and the frequency-domain output are compressed sampled to obtain a compressed sampling result. Finally, signal reconstruction is performed based on the compressed sampling result to obtain a reconstructed signal, and signal detection is performed based on the reconstructed signal to obtain the signal detection result of the original signal. The time-domain high-frequency features and frequency-domain low-frequency features in the original signal are accurately extracted using dual-domain sparse decomposition to achieve precise separation of the original signal. At the same time, sparse decomposition can reduce the computational complexity during signal reconstruction after compressed sampling and improve the computational efficiency of reconstruction. By accurately extracting signal features, a more accurate signal can be constructed to improve the accuracy of signal detection.

[0125] According to the method described in the above embodiments, this embodiment will be further described from the perspective of a detection device for hybrid-domain dual-mode signals based on compressive sensing. The detection device for hybrid-domain dual-mode signals based on compressive sensing can be specifically implemented as an independent entity or integrated in an electronic device, such as a terminal. The terminal can include a mobile phone, a tablet computer, etc.

[0126] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a detection device for hybrid-domain dual-mode signals based on compressive sensing provided by an embodiment of the present application. As Figure 4 shown, the detection device 400 for hybrid-domain dual-mode signals based on compressive sensing provided by an embodiment of the present application includes:

[0127] The first processing module 401 is configured to obtain the original signal to be detected, and preprocess the original signal to obtain the preprocessed original signal;

[0128] The second processing module 402 is configured to perform dual-domain sparse decomposition on the preprocessed original signal to obtain a time-domain output and a frequency-domain output;

[0129] The compressive sampling module 403 is configured to perform compressive sampling on the time-domain output and the frequency-domain output to obtain a compressive sampling result;

[0130] The inspection processing module 404 is configured to perform signal reconstruction based on the compressive sampling result, and perform signal detection based on the obtained reconstructed signal to obtain a signal detection result of the original signal.

[0131] In one embodiment, the first processing module 401 is further configured to:

[0132] Perform band-pass filtering on the original signal to obtain the original signal after band-pass filtering;

[0133] Perform normalization on the filtered original signal to obtain the processed original signal.

[0134] In one embodiment, the second processing module 402 is further configured to:

[0135] Perform time-domain decomposition on the preprocessed original signal, and extract high-frequency signal features from the first decomposition result obtained by the time-domain decomposition to obtain a time-domain output;

[0136] Perform frequency-domain decomposition on the preprocessed original signal, and extract low-frequency signal features from the second decomposition result obtained by the frequency-domain decomposition to obtain a frequency-domain output.

[0137] In one embodiment, the compressive sampling module 403 is further configured to:

[0138] Construct a time-domain matrix and a frequency-domain matrix based on the error feedback result during matrix construction, and construct a coupling matrix according to the time-domain matrix and the frequency-domain matrix;

[0139] According to the time-domain output and the frequency-domain output, obtain an output matrix after dual-domain sparse decomposition of the original signal;

[0140] Calculate the compressive sampling result corresponding to the original signal according to the coupling matrix and the output matrix.

[0141] In one embodiment, the compressive sampling module 403 is further configured to:

[0142] Determine the channel mode of the original signal, where the channel mode includes HPLC mode and HRF mode;

[0143] Construct a time-domain matrix based on the error feedback result during matrix construction and the channel mode, and construct a frequency-domain matrix based on the error feedback result during matrix construction;

[0144] Perform coupling processing on the time-domain matrix and the frequency-domain matrix to obtain a corresponding coupling matrix.

[0145] In one embodiment, the compressive sampling module 403 is further configured to:

[0146] Extract the time-domain signal and the frequency-domain signal in the compressive sampling result respectively according to the channel mode to obtain a processed compressive sampling result;

[0147] Reconstruct the signal in the compressive sampling result based on the alternating direction method of multipliers, and determine whether the signal reconstruction is completed based on the objective function for signal reconstruction and the soft threshold;

[0148] When it is determined that the signal reconstruction is completed according to the objective function and the soft threshold, obtain a reconstructed signal.

[0149] In one embodiment, the inspection processing module 404 is further configured to:

[0150] Perform phase synchronization detection and consistency detection on the time-domain signal and the frequency-domain signal in the reconstructed signal to obtain a synchronization detection result and a consistency index;

[0151] Obtain a detection result of the reconstructed signal according to the synchronization detection result and the consistency index, and use the detection result as the signal detection result of the original signal.

[0152] In addition, please refer to Figure 5 , Figure 5 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 5 shown, the electronic device 500 includes a processor 501 and a memory 502. Among them, the processor 501 is electrically connected to the memory 502.

[0153] The processor 501 is the control center of the electronic device 500, connects various parts of the entire electronic device through various interfaces and circuits and other lines, runs or loads an application program stored in the internal flash of the memory 502, and this application program can be an embedded program, and calls the data stored in the memory 502 to implement the detection and analysis processing of the signal based on the above-described detection method for hybrid-domain dual-mode signals based on compressive sensing for the signals obtained in dual-mode communication.

[0154] In this embodiment, the processor 501 in the electronic device 500 loads the instructions corresponding to the processes of one or more application programs into the memory 502 according to the steps in the above-described detection method of the hybrid-domain dual-mode signal based on compressive sensing, and the processor 501 runs the application programs stored in the memory 502, thereby realizing the detection and analysis of the hybrid-domain dual-mode signal based on compressive sensing.

[0155] The electronic device 500 can implement the steps in any embodiment of the detection method of the hybrid-domain dual-mode signal based on compressive sensing provided in this application embodiment. Therefore, it can achieve the beneficial effects that any detection method of the hybrid-domain dual-mode signal based on compressive sensing provided in this application embodiment can achieve. For details, please refer to the previous embodiments and will not be elaborated here.

[0156] Please refer to Figure 6 , Figure 6 which is another structural schematic diagram of the electronic device provided in this application embodiment. As Figure 6 shown, Figure 6 it shows the specific structural block diagram of the electronic device provided in this application embodiment. The electronic device can be used to implement the detection method of the hybrid-domain dual-mode signal based on compressive sensing provided in the above embodiment. Specifically, the electronic device 600 can be a device related to an electric meter in the electric meter industry.

[0157] The RF circuit 610 is used to receive and transmit electromagnetic waves, realizing the mutual conversion between electromagnetic waves and electrical signals, so as to communicate with a communication network or other devices. The RF circuit 610 may include various existing circuit components for performing these functions. For example, antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, subscriber identity module (SIM) cards, memories, and so on. The RF circuit 610 can communicate with various networks such as the Internet, enterprise intranets, wireless networks or communicate with other devices through wireless networks. The above-mentioned wireless networks may include cellular phone networks, wireless local area networks or metropolitan area networks. The above-mentioned wireless networks can use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and / or IEEE 802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, and may even include those protocols that have not been developed yet.

[0158] The memory 620 can be used to store software programs and modules, such as the program instructions / modules corresponding to the detection method of the hybrid-domain dual-mode signal based on compressive sensing in the above embodiments. The processor 680 executes various functional applications to implement the detection method of the hybrid-domain dual-mode signal based on compressive sensing by running the dual-mode module stored in the internal flash of the memory 620 and the program for controlling the dual-mode module. Among them, the program for controlling the dual-mode module can be an embedded program and is stored in the internal flash of the memory 620.

[0159] The memory 620 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 620 may further include a memory remotely located with respect to the processor 680, and these remote memories may be connected to the electronic device 600 through a network. Examples of the above-mentioned network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0160] The input unit 630 can be used to receive uploaded digital or character information, and generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function controls. Specifically, the input unit 630 may include a touch-sensitive surface 631 and other input devices 632. The touch-sensitive surface 631, also known as a touch display screen or a touchpad, can collect touch operations of the user thereon or nearby (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch-sensitive surface 631), and drive corresponding connection devices according to a preset program. Optionally, the touch-sensitive surface 631 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch position of the user and detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 680, and can receive and execute commands sent by the processor 680. In addition, various types such as resistive, capacitive, infrared, and surface acoustic wave can be used to implement the touch-sensitive surface 631. In addition to the touch-sensitive surface 631, the input unit 630 may further include other input devices 632. Specifically, the other input devices 632 may include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, joysticks, etc.

[0161] The display unit 640 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device 600. These graphical user interfaces can be composed of graphics, text, icons, videos, and any combination thereof. The display unit 640 may include a display panel 641. Optionally, the display panel 641 can be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface 631 can cover the display panel 641. When the touch-sensitive surface 631 detects a touch operation on or near it, it is transmitted to the processor 680 to determine the type of touch event. Subsequently, the processor 680 provides a corresponding visual output on the display panel 641 according to the type of touch event. Although in the figure, the touch-sensitive surface 631 and the display panel 641 are implemented as two independent components to realize the input and output functions, in some embodiments, the touch-sensitive surface 631 and the display panel 641 can be integrated to realize the input and output functions.

[0162] The electronic device 600 may further include at least one sensor 650, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. Among them, the ambient light sensor can adjust the brightness of the display panel 641 according to the brightness of the ambient light, and the proximity sensor can generate an interruption when the flip cover is closed or opened. As a kind of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in all directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometer, tapping), etc. As for other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors that the electronic device 600 can also be configured with, they will not be elaborated here.

[0163] The audio circuit 660, the speaker 661, and the microphone 662 can provide an audio interface between the user and the electronic device 600. The audio circuit 660 can transmit the electrical signal converted from the received audio data to the speaker 661, and the speaker 661 converts it into a sound signal for output. On the other hand, the microphone 662 converts the collected sound signal into an electrical signal, which is received by the audio circuit 660 and converted into audio data. Then, after the audio data is output to the processor 680 for processing, it is sent to another terminal, for example, via the RF circuit 610, or the audio data is output to the memory 620 for further processing. The audio circuit 660 may also include an earphone jack to provide communication between the external earphone and the electronic device 600.

[0164] The electronic device 600 can help users receive requests, send information, etc. through the transmission module 670 (such as a Wi-Fi module), which provides users with wireless broadband Internet access. Although the transmission module 670 is shown in the figure, it can be understood that it does not belong to the essential components of the electronic device 600 and can be omitted entirely within the scope of not changing the essence of the invention as needed.

[0165] The processor 680 is the control center of the electronic device 600, connecting various parts of the entire mobile phone through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 620, and by calling the data stored in the memory 620, it executes various functions of the electronic device 600 and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 680 may include one or more processing cores; in some embodiments, the processor 680 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 680 either.

[0166] The electronic device 600 also includes a power source 690 (such as a battery) that powers each component. In some embodiments, the power source can be logically connected to the processor 680 through a power management system, thereby realizing functions such as management of charging, discharging, and power consumption management through the power management system. The power source 690 may also include any components such as one or more DC or AC power sources, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0167] Specifically in this embodiment, the display unit of the electronic device is a touch screen display. The mobile terminal also includes a memory, and one or more programs, where one or more programs are stored in the memory and are configured to be executed by one or more processors to implement any step in the detection method of the compressed sensing-based hybrid-domain dual-mode signal provided in the above embodiment.

[0168] Specifically in implementation, the above-mentioned each module can be implemented as an independent entity, or can be combined arbitrarily to be implemented as the same or several entities. For the specific implementation of the above-mentioned each module, reference can be made to the method embodiments described above, and details will not be repeated here.

[0169] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. For this purpose, the embodiments of the present application provide a storage medium in which multiple instructions are stored. When these instructions are executed by a processor, any step in the detection method of the hybrid-domain dual-mode signal based on compressive sensing provided by the above embodiments can be realized.

[0170] Among them, the storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc. Specifically, the storage medium may be the internal flash. That is, the embedded program used to implement the above detection method of the hybrid-domain dual-mode signal based on compressive sensing can be stored in the internal flash.

[0171] Since the instructions stored in the storage medium can execute the steps in any embodiment of the detection method of the hybrid-domain dual-mode signal based on compressive sensing provided by the embodiments of the present application, the beneficial effects that can be achieved by any detection method of the hybrid-domain dual-mode signal based on compressive sensing provided by the embodiments of the present application can be realized. For details, please refer to the previous embodiments and will not be elaborated here.

[0172] The above has introduced in detail a detection method, device, electronic device and storage medium of a hybrid-domain dual-mode signal based on compressive sensing provided by the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application. Moreover, for those of ordinary skill in the technical field, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present application.

Claims

1. A detection method for mixed-domain dual-mode signals based on compressed sensing, characterized in that: include: Acquire an original signal to be detected, and preprocess the original signal to obtain a preprocessed original signal; Perform dual-domain sparse decomposition on the preprocessed original signal to obtain time domain output and frequency domain output; Performing compressive sampling on the time domain output and the frequency domain output to obtain a compressive sampling result; Signal reconstruction is performed according to the compressed sampling result to obtain a reconstructed signal, and signal detection is performed based on the reconstructed signal to obtain a signal detection result of the original signal.

2. The method according to claim 1, characterized in that The preprocessing of the original signal to obtain the preprocessed original signal includes: Performing bandpass filtering on the original signal to obtain the original signal after bandpass filtering; The original signal after filtering is normalized to obtain the processed original signal.

3. The method according to claim 1, characterized in that The dual-domain sparse decomposition is performed on the preprocessed original signal to obtain a time domain output and a frequency domain output, including: Performing time domain decomposition on the preprocessed original signal, and extracting high-frequency signal features from a first decomposition result obtained by the time domain decomposition to obtain a time domain output; The preprocessed original signal is decomposed in the frequency domain, and the low-frequency signal feature is extracted from the second decomposition result obtained by the frequency domain decomposition to obtain a frequency domain output.

4. The method according to claim 1, characterized in that The compressing and sampling the time domain output and the frequency domain output to obtain the compressed sampling result includes: A time domain matrix and a frequency domain matrix are constructed based on the error feedback result during matrix construction, and a coupling matrix is ​​constructed based on the time domain matrix and the frequency domain matrix; Obtaining an output matrix after dual-domain sparse decomposition of the original signal according to the time domain output and the frequency domain output; A compressed sampling result corresponding to the original signal is calculated according to the coupling matrix and the output matrix.

5. The method according to claim 4, characterized in that The error feedback result during matrix construction is used to construct a time domain matrix and a frequency domain matrix, and a coupling matrix is ​​constructed based on the time domain matrix and the frequency domain matrix, including: Determining a channel mode of the original signal, wherein the channel mode includes an HPLC mode and an HRF mode; A time domain matrix is ​​obtained based on the error feedback result when constructing based on the matrix and the channel mode, and a frequency domain matrix is ​​obtained based on the error feedback result when constructing based on the matrix; The time domain matrix and the frequency domain matrix are coupled to obtain a corresponding coupling matrix.

6. The method according to claim 5, characterized in that The step of reconstructing a signal according to the compressed sampling result to obtain a reconstructed signal includes: According to the channel mode, signal extraction processing is performed on the time domain signal and the frequency domain signal in the compressed sampling result respectively to obtain a processed compressed sampling result; Performing signal reconstruction on the compressed sampling result based on an alternating direction multiplier method, and determining whether the signal reconstruction is completed based on constructing an objective function and a soft threshold for signal reconstruction; When it is determined according to the objective function and the soft threshold that the signal reconstruction is completed, a reconstructed signal is obtained.

7. The method according to claim 1, characterized in that The performing signal detection based on the reconstructed signal to obtain a signal detection result of the original signal includes: Performing phase synchronization detection and consistency detection on the time domain signal and the frequency domain signal in the reconstructed signal to obtain a synchronization detection result and a consistency index; A detection result of the reconstructed signal is obtained according to the synchronization detection result and the consistency index, and the detection result is used as the signal detection result of the original signal.

8. A detection device for mixed-domain dual-mode signals based on compressed sensing, characterized in that: include: The first processing module is used to obtain the original signal to be detected, and preprocess the original signal to obtain the preprocessed original signal; The second processing module is used to perform dual-domain sparse decomposition on the pre-processed original signal to obtain a time domain output and a frequency domain output; A compression sampling module, used for performing compression sampling on the time domain output and the frequency domain output to obtain a compression sampling result; The inspection processing module is used to reconstruct the signal according to the compressed sampling result, and perform signal detection based on the obtained reconstructed signal to obtain the signal detection result of the original signal.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program or an embedded program stored in the internal storage of the memory and executable on the processor, and the processor implements the steps in the method according to any one of claims 1 to 7 when executing the computer program or the embedded program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or an embedded program, and when the computer program or the embedded program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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