Signal recovery method and device based on signal noise and sparse characteristics
By combining signal noise and sparse characteristics in the signal recovery method, the OMP-SSRN algorithm is used to recover signals, which solves the problem of difficulty in signal recovery in street battle scenarios, and achieves a more efficient and accurate signal recovery effect.
Patent Information
- Application Number
- CN202411807424.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-04-11
AI Technical Summary
In the prior art, OMP algorithms are difficult to effectively recover signal when dealing with severe noise pollution and large electromagnetic interference in street battle scenarios.
A signal recovery method based on signal noise and sparse characteristics is proposed. Signal recovery is achieved by initializing communication equipment parameters, obtaining key parameters of street battle scenes, signal modeling, initially recovering signals, further optimizing signals, dynamically adjusting algorithm parameters until iterative convergence is converged.
It improves the stability and accuracy of signal recovery, reduces the sparseness of signals and the impact of noise interference, and achieves rapid and accurate recovery of sparse signals in a complex urban battlefield environment.
Smart Images
Figure CN119814176A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of signal processing technology, and in particular to a signal recovery method and device based on signal noise and sparse characteristics. Background Art
[0002] In the field of modern communications, especially in military communications and signal processing in complex environments, signal recovery technology plays a vital role. The dense buildings, narrow streets and complex channels in urban street fighting battlefields seriously hinder the propagation path of wireless signals, causing the signal to be severely distorted when it reaches the receiving end. In addition, the fast-moving nodes and dynamically changing battlefield situations in street fighting also increase the complexity of signal processing. In such an environment, traditional signal recovery methods such as linear filtering and frequency domain filtering are often difficult to effectively deal with the problems caused by these non-ideal factors.
[0003] Communication challenges in street fighting also include the difficulty in obtaining reconnaissance intelligence, the difficulty in effectively using firepower, the complex spatial structure of urban battlefields, and the presence of electromagnetic interference and hostile interference. These characteristics make signal recovery in street fighting more difficult, so specially designed algorithms are needed to adapt to this environment. Summary of the invention
[0004] In order to solve the technical problems of serious noise pollution and electromagnetic interference in the OMP (Orthogonal Matching Pursuit, OMP) algorithm in the existing technology when processing street fighting scenes, the embodiment of the present invention provides a signal recovery method and device based on signal noise and sparse characteristics. The technical solution is as follows:
[0005] On the one hand, a signal recovery method based on signal noise and sparse characteristics is provided, characterized in that the method comprises:
[0006] S1. Initialize the parameters of the communication device and generate a time vector based on the initialized parameters;
[0007] S2, obtaining key parameters in the street fighting scene and inputting the parameters into the communication device;
[0008] S3, based on the key parameters of the input street fighting scene, signal modeling of the street fighting scene;
[0009] S4. Based on the signal modeling of the street fighting scene, the OMP-SSRN algorithm is used to perform preliminary signal recovery to obtain a preliminary recovery signal;
[0010] S5. Further optimize the initial recovery signal using the L1 norm penalty term to obtain the OMP-SSRN algorithm; dynamically adjust the OMP-SSRN algorithm parameters according to changes in the street fighting battlefield environment until the OMP-SSRN algorithm iteratively converges to obtain the recovery signal;
[0011] S6. Calculate the error between the restored signal and the original signal, post-process and analyze the restored signal, evaluate the restoration quality, and complete the signal recovery based on signal noise and sparse characteristics for street fighting.
[0012] Optionally, in S1, initializing parameters of the communication device and generating a time vector based on the initialized parameters includes:
[0013] Initialize the parameters of the communication device, including the center frequency ,bandwidth and sampling frequency ;
[0014] Based on the sampling frequency Generate a time vector.
[0015] Optionally, key parameters in the street fighting scenario include:
[0016] Noise Level , Electromagnetic Interference Level , sparsity and the number of multipath paths .
[0017] Optionally, signal modeling is performed for street fighting scenarios, including:
[0018] Define a dictionary matrix , including signal characteristics existing in street fighting environments;
[0019] Generate sparse signals, sparse signals in street fighting Modeling;
[0020] Collect complex environment information and analyze signals containing noise and interference Modeling:
[0021]
[0022] in, It's noise and interference.
[0023] Optionally, in S4, based on signal modeling of the street fighting scene, the OMP-SSRN algorithm is used to perform preliminary signal recovery to obtain a preliminary recovered signal, including:
[0024] Based on the signal modeling of the street fighting scene, the residual is calculated:
[0025] ;
[0026] in, To observe the signal; is the current sparse signal estimate;
[0027] Calculate the correlation vector :
[0028] ;
[0029] in, is the dictionary matrix The transpose of
[0030] Select the most relevant signal components ; The most relevant signal component is the position of the element with the maximum value in the correlation vector;
[0031] Updating sparse signal representation .
[0032] Optionally, in S5, the preliminary restored signal is further optimized using an L1 norm penalty term, including:
[0033] According to the following formula, the L1 norm penalty term is used to further optimize the preliminary recovered signal:
[0034]
[0035] in, is the regularization parameter.
[0036] Optionally, in S5, according to the change of the street fighting battlefield environment, the OMP-SSRN algorithm parameters are dynamically adjusted until the OMP-SSRN algorithm is iteratively converged to obtain a recovery signal, including:
[0037] According to the changes in the street fighting battlefield environment, the number of sparsity parameters in the OMP-SSRN algorithm is adaptively adjusted and the regularization parameter of the L1 norm penalty term ;
[0038] Preset the iteration threshold and adjust the sparsity parameter in the OMP-SSRN algorithm and the regularization parameter of the L1 norm penalty term Iterate until the algorithm converges and obtains the restored signal;
[0039] Among them, the convergence condition is that the residual and error in the OMP-SSRN algorithm are lower than the preset threshold.
[0040] Optionally, in S6, calculating the error between the restored signal and the original signal includes:
[0041] The error ratio between the restored signal and the original signal is:
[0042] .
[0043] On the other hand, a signal recovery device based on signal noise and sparse characteristics is provided, and the device is applied to a signal recovery method based on signal noise and sparse characteristics, and the device comprises:
[0044] An initialization module, used for initializing parameters of the communication device and generating a time vector based on the initialized parameters;
[0045] A parameter acquisition module is used to obtain key parameters in street fighting scenarios and input the parameters into the communication device;
[0046] A modeling module, used for signal modeling of the street fighting scene based on key parameters in the input street fighting scene;
[0047] A preliminary recovery module is used to perform preliminary signal recovery based on the signal modeling of the street fighting scene using the OMP-SSRN algorithm to obtain a preliminary recovery signal;
[0048] The signal recovery module is used to further optimize the initial recovery signal using the L1 norm penalty term to obtain the OMP-SSRN algorithm; according to the changes in the street fighting battlefield environment, the OMP-SSRN algorithm parameters are dynamically adjusted until the OMP-SSRN algorithm iteratively converges to obtain the recovery signal;
[0049] The evaluation module is used to calculate the error between the restored signal and the original signal, post-process and analyze the restored signal, evaluate the restoration quality, and complete the signal recovery based on signal noise and sparse characteristics for street fighting.
[0050] On the other hand, a signal recovery device based on signal noise and sparse characteristics is provided, and the signal recovery device based on signal noise and sparse characteristics comprises: a processor; a memory, wherein computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned signal recovery methods based on signal noise and sparse characteristics is implemented.
[0051] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned signal recovery methods based on signal noise and sparse characteristics.
[0052] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:
[0053] In an embodiment of the present invention, an improved OMP algorithm based on signal noise and sparsity characteristics is provided. The method comprehensively considers factors such as the mobility of nodes in street fighting, signal recovery capability, and communication distance, and by proposing an L1 norm penalty term, improves the stability and accuracy of signal recovery, reduces the sparsity of the signal and the influence of noise interference in the street fighting environment. The method can improve the accuracy of signal recovery while maintaining computational efficiency. The method provided by the present invention can achieve rapid and accurate recovery of sparse signals in complex urban battlefield environments, for different street fighting scenarios such as alleys and buildings. BRIEF DESCRIPTION OF THE DRAWINGS
[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0055] Figure 1 It is a flow chart of an adaptive sparse signal recovery method for street fighting provided by an embodiment of the present invention;
[0056] Figure 2 A comparison diagram of the original and restored signals provided by an embodiment of the present invention;
[0057] Figure 3 A signal diagram of interruption and recovery provided by an embodiment of the present invention;
[0058] Figure 4 A comparison chart of restoration accuracy provided by an embodiment of the present invention;
[0059] Figure 5 is a block diagram of a signal recovery device based on signal noise and sparse characteristics provided by an embodiment of the present invention;
[0060] Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0061] The technical solution of the present invention is described below in conjunction with the accompanying drawings.
[0062] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.
[0063] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.
[0064] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.
[0065] The embodiment of the present invention provides a signal recovery method based on signal noise and sparse characteristics, which can be implemented by a signal recovery device based on signal noise and sparse characteristics, and the signal recovery device based on signal noise and sparse characteristics can be a terminal or a server. Figure 1 The signal recovery method based on signal noise and sparse characteristics is shown in the flowchart, and the processing flow of the method may include the following steps:
[0066] S1. Initialize the parameters of the communication device and generate a time vector based on the initialized parameters;
[0067] In a feasible implementation manner, in S1, initializing parameters of the communication device and generating a time vector based on the initialized parameters include:
[0068] Initialize the parameters of the communication device, including the center frequency ,bandwidth and sampling frequency ;
[0069] Based on the sampling frequency Generate a time vector.
[0070] In a feasible implementation, according to specific parameters of the battlefield environment, such as the Boltzmann constant , center frequency ,bandwidth Etc., initialize the signal processing environment. This step ensures that the algorithm can adapt to different battlefield conditions.
[0071] In a feasible implementation, according to the sampling frequency , generating the time vector , which will be used as a time reference in the subsequent signal collection and recovery process. Defines the time base of a signal and provides an accurate time reference for each sampling point of the signal.
[0072] S2, obtaining key parameters in the street fighting scene and inputting the parameters into the communication device;
[0073] In a feasible implementation, key parameters in the street fighting scenario include:
[0074] Noise Level , Electromagnetic Interference Level , sparsity and the number of multipath paths ,These parameters will directly affect the performance of the signal recovery algorithm.
[0075] S3, based on the key parameters of the input street fighting scene, signal modeling of the street fighting scene;
[0076] In a feasible implementation, signal modeling is performed for a street fighting scenario, including:
[0077] Define a dictionary matrix , contains the signal features existing in street fighting environments, which are used for sparse signal representation and recovery;
[0078] Generate sparse signals, real signals in street fighting Modeling;
[0079] Collect complex environment information and analyze signals containing noise and interference Modeling:
[0080] (1)
[0081] in, is the noise and interference, which can be expressed as:
[0082] (2)
[0083] (3)
[0084] (4).
[0085] S4. Based on the signal modeling of the street fighting scene, the OMP-SSRN algorithm is used to perform preliminary signal recovery to obtain a preliminary recovered signal.
[0086] In a feasible implementation manner, the present invention utilizes an improved OMP algorithm (Orthogonal Matching Pursuit for Sparse Signal Recovery with Noise, OMP-SSRN) to perform preliminary recovery of the signal, which can quickly identify key components in the signal and maintain high efficiency even when the signal is severely sparse in a street fighting environment.
[0087] In a feasible implementation, in S4, based on signal modeling of the street fighting scene, the OMP algorithm is used to perform preliminary signal recovery to obtain a preliminary recovered signal, including:
[0088] Based on the signal modeling of the street fighting scene, the residual is calculated: hour,
[0089] (5)
[0090] in, To observe the signal; is the current sparse signal estimate;
[0091] Calculate the correlation vector :
[0092] (6)
[0093] in, is the dictionary matrix The transpose of is a correlation vector, which indicates the correlation degree between each atom in the dictionary matrix and the current residual.
[0094] Select the most relevant signal components:
[0095] (7)
[0096] The most correlated signal component is the position of the element with the maximum value in the correlation vector;
[0097] Update the sparse signal representation:
[0098] (8)
[0099] in, is the coefficient corresponding to the most correlated atom in the sparse signal representation. is the column vector of the most relevant atoms in the dictionary matrix. is the transpose of the most relevant atom.
[0100] S5. Use the L1 norm penalty term to further optimize the initial recovery signal to obtain the OMP-SSRN algorithm; according to the changes in the street fighting battlefield environment, dynamically adjust the OMP-SSRN algorithm parameters until the OMP-SSRN algorithm iteratively converges to obtain the recovery signal.
[0101] In a feasible implementation, the OMP algorithm can quickly find the most relevant signal components when processing sparse signals due to its simplicity and speed; however, it is sensitive to noise, and the presence of large noise in the signal will affect the performance of the algorithm. By proposing the L1 norm penalty term, the noise and sparsity in the signal can be effectively processed, and the stability and accuracy of signal recovery can be improved.
[0102] In a feasible implementation, in S5, the initial recovery signal is further optimized using the L1 norm penalty term, including:
[0103] According to the following formula, the L1 norm penalty term is used to further optimize the preliminary recovered signal:
[0104]
[0105] in, is the regularization parameter.
[0106] In a feasible implementation, the algorithm parameters are dynamically adjusted according to the changes in the street fighting battlefield environment to adapt to different combat scenarios in street fighting. This step ensures that the algorithm can maintain optimal performance in a dynamically changing battlefield environment. Adaptive adjustment of sparsity parameters in the OMP algorithm and the regularization parameter of the L1 norm penalty term The adjustments of the two are interdependent to achieve better signal recovery performance. The two are iterated until convergence, where the convergence condition is that the residual and error are lower than the preset threshold.
[0107] In a feasible implementation, in S5, dynamically adjusting the OMP-SSRN algorithm parameters until the OMP-SSRN algorithm iteratively converges to obtain a recovery signal includes:
[0108] Adaptively adjust the sparsity parameters in the OMP-SSRN algorithm according to the changes in the street fighting battlefield environment and the regularization parameter of the L1 norm penalty term ;
[0109] Preset the iteration threshold and adjust the sparsity parameter in the OMP-SSRN algorithm and the regularization parameter of the L1 norm penalty term Iterate until the algorithm converges and obtain the recovered signal:
[0110]
[0111]
[0112] Among them, the convergence condition is that the residual and error in the OMP-SSRN algorithm are lower than the preset threshold; is the residual threshold, which is used to determine whether the sparsity needs to be increased. is the adjustment factor, is the error threshold, which is used to determine whether the regularization parameter needs to be adjusted. To initialize the sparsity parameter, is the initial regularization parameter.
[0113] S6. Calculate the error between the restored signal and the original signal, evaluate the restoration quality, and post-process and analyze the restored signal to provide a deeper battlefield situation awareness and complete signal recovery based on signal noise and sparse characteristics for street fighting.
[0114] In one possible implementation, this step provides a feedback mechanism for the algorithm for further optimization.
[0115] In a feasible implementation manner, in S6, calculating the error between the restored signal and the original signal includes:
[0116] The error ratio between the restored signal and the original signal is:
[0117] .
[0118] In a feasible implementation mode, according to Figure 2-4 The experimental data of the present invention are used to describe the above scheme in detail: Specific parameters Experimental parameters:
[0119] Boltzmann constant k = 1.38e-23;
[0120] Center frequency = 4.5e9;
[0121] bandwidth = 500e6;
[0122] Pulse width pulse_width = 1e-6;
[0123] Sampling frequency = 2 * bw;
[0124] Time vector t=0:1 / :pulse_width-1 / ;
[0125] Noise Level (between 0 and 1): 0.5;
[0126] Electromagnetic interference level (between 0 and 1): 0.5;
[0127] Input sparsity (at least 2): 4;
[0128] Number of multipath paths (between 1 and 10): 5;
[0129] like Figure 2 A comparison diagram of the original and restored signals provided by an embodiment of the present invention is shown, showing the original signal (blue solid line), the signal restored by the OMP algorithm (green dashed line), and the signal restored by the OMP-SSRN algorithm (pink dot-dash line).
[0130] like Figure 3 The figure below shows the interruption signal (solid blue line) and the signal after the simulated interruption (dashed red line) provided by the embodiment of the present invention. The figure below shows the interruption signal (solid green line) restored using the OMP-SSRN algorithm.
[0131] like Figure 4 The figure shows a comparison chart of restoration accuracy provided by an embodiment of the present invention, which compares the restoration errors of the OMP and OMP-SSRN algorithms through a bar chart. It can be seen from the bar chart that the restoration error of the OMP-SSRN algorithm is slightly lower than that of the OMP algorithm, which means that the OMP-SSRN algorithm is more robust when processing signal restoration tasks.
[0132] In the embodiment of the present invention, an L1 norm penalty term is proposed to optimize the algorithm, so that the OMP-SSRN algorithm can adaptively adjust the algorithm parameters to adapt to the changing environment and signal characteristics in street fighting. By comprehensively considering the sparsity of the signal and the influence of noise interference, this method not only improves the accuracy of signal recovery, but also enhances the robustness and reliability of the communication system in a complex battlefield environment, especially in a street fighting environment.
[0133] Figure 5 is a block diagram of a signal recovery device 300 based on signal noise and sparse characteristics according to an exemplary embodiment. The device 300 is used in a signal recovery method based on signal noise and sparse characteristics. Figure 5 The device includes an initialization module 310, a parameter acquisition module 320, a modeling module 330, a preliminary recovery module 340, a signal recovery module 350 and an evaluation module 360. Among them:
[0134] An initialization module 310, configured to initialize parameters of the communication device and generate a time vector based on the initialized parameters;
[0135] A parameter acquisition module 320 is used to acquire key parameters in the street fighting scene and input the parameters to the communication device;
[0136] A modeling module 330 is used to perform signal modeling on the street fighting scene based on key parameters in the street fighting scene input;
[0137] A preliminary recovery module 340 is used to perform preliminary recovery of the signal using the OMP algorithm based on signal modeling of the street fighting scene to obtain a preliminary recovered signal;
[0138] The signal recovery module 350 is used to further optimize the preliminary recovered signal by using the L1 norm penalty term to obtain the OMP-SSRN algorithm; dynamically adjust the OMP-SSRN algorithm parameters according to the changes in the street fighting battlefield environment until the OMP-SSRN algorithm iteratively converges to obtain the recovered signal;
[0139] The evaluation module 360 is used to calculate the error between the restored signal and the original signal, perform post-processing and analysis on the restored signal, evaluate the restoration quality, and complete the signal recovery based on signal noise and sparse characteristics for street fighting.
[0140] Optionally, the initialization module 310 is used to initialize the parameters of the communication device, the parameters including the center frequency ,bandwidth and sampling frequency ;
[0141] Based on the sampling frequency Generate a time vector.
[0142] Optionally, key parameters in the street fighting scenario include:
[0143] Noise Level , Electromagnetic Interference Level , sparsity and the number of multipath paths .
[0144] Optionally, signal modeling is performed for street fighting scenarios, including:
[0145] Define a dictionary matrix , including signal characteristics existing in street fighting environments;
[0146] Generate sparse signals, sparse signals in street fighting Modeling;
[0147] Collect complex environment information and analyze signals containing noise and interference Modeling:
[0148]
[0149] in, It's noise and interference.
[0150] Optionally, the preliminary recovery module 340 is used to calculate the residual based on signal modeling of the street fighting scene:
[0151] ;
[0152] in, To observe the signal; is the current sparse signal estimate;
[0153] Calculate the correlation vector :
[0154] ;
[0155] in, is the dictionary matrix The transpose of
[0156] Select the most relevant signal components ; The most relevant signal component is the position of the element with the maximum value in the correlation vector;
[0157] Updating sparse signal representation .
[0158] Optionally, the signal recovery module 350 is used to further optimize the preliminary recovered signal using an L1 norm penalty term according to the following formula:
[0159]
[0160] in, is the regularization parameter.
[0161] Optionally, the signal recovery module 350 is used to adaptively adjust the sparsity parameter number in the OMP-SSRN algorithm according to changes in the street fighting battlefield environment. and the regularization parameter of the L1 norm penalty term ;
[0162] Preset the iteration threshold and adjust the sparsity parameter in the OMP-SSRN algorithm and the regularization parameter of the L1 norm penalty term Iterate until the algorithm converges and obtains the restored signal;
[0163] Among them, the convergence condition is that the residual and error in the OMP-SSRN algorithm are lower than the preset threshold.
[0164] Optionally, the evaluation module 360 is used to calculate the error ratio between the restored signal and the original signal:
[0165] .
[0166] In the embodiment of the present invention, an L1 norm penalty term is proposed to optimize the algorithm, so that the OMP-SSRN algorithm can adaptively adjust the algorithm parameters to adapt to the changing environment and signal characteristics in street fighting. By comprehensively considering the sparsity of the signal and the influence of noise interference, this method not only improves the accuracy of signal recovery, but also enhances the robustness and reliability of the communication system in a complex battlefield environment, especially in a street fighting environment.
[0167] Figure 6 is a schematic diagram of the structure of a signal recovery device based on signal noise and sparse characteristics provided by an embodiment of the present invention, such as Figure 6 As shown, the signal recovery device based on signal noise and sparse characteristics may include the above Figure 5 The signal recovery device based on signal noise and sparse characteristics is shown. Optionally, the signal recovery device based on signal noise and sparse characteristics 410 may include a first processor 2001.
[0168] Optionally, the signal recovery device 410 based on signal noise and sparse characteristics may further include a memory 2002 and a transceiver 2003 .
[0169] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.
[0170] Combine the following Figure 6 The components of the signal recovery device 410 based on signal noise and sparse characteristics are specifically introduced as follows:
[0171] The first processor 2001 is the control center of the signal recovery device 410 based on signal noise and sparse characteristics, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).
[0172] Optionally, the first processor 2001 may execute various functions of the signal recovery device 410 based on signal noise and sparse characteristics by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .
[0173] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 6 CPU0 and CPU1 are shown in FIG.
[0174] In a specific implementation, as an embodiment, the signal recovery device 410 based on signal noise and sparse characteristics may also include multiple processors, such as Figure 6 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).
[0175] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.
[0176] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and access the first processor 2001 through the interface circuit ( Figure 6 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.
[0177] The transceiver 2003 is used to communicate with a network device or a terminal device.
[0178] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 6 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.
[0179] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 6(not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.
[0180] It should be noted that Figure 6 The structure of the signal recovery device 410 based on signal noise and sparse characteristics shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.
[0181] In addition, the technical effects of the signal recovery device 410 based on signal noise and sparse characteristics can refer to the technical effects of the signal recovery method based on signal noise and sparse characteristics described in the above method embodiment, which will not be repeated here.
[0182] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0183] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).
[0184] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable sensors. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.
[0185] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.
[0186] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0187] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0188] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0189] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0190] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
[0191] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
Claims
1. A signal recovery method based on signal noise and sparse characteristics, characterized in that: The method comprises: S1. Initialize the parameters of the communication device and generate a time vector based on the initialized parameters; S2. Obtain key parameters in the street fighting scenario and input the parameters into the communication device; S3, based on the key parameters of the input street fighting scene, signal modeling of the street fighting scene; S4, based on the signal modeling of the street fighting scene, the OMP algorithm is used to perform preliminary signal recovery to obtain a preliminary recovery signal; S5. Further optimize the initial recovery signal using the L1 norm penalty term to obtain the OMP-SSRN algorithm; dynamically adjust the OMP-SSRN algorithm parameters according to changes in the street fighting battlefield environment until the OMP-SSRN algorithm iteratively converges to obtain the recovery signal; S6. Calculate the error between the restored signal and the original signal, post-process and analyze the restored signal, evaluate the restoration quality, and complete the signal recovery based on signal noise and sparse characteristics for street fighting.
2. The signal recovery method based on signal noise and sparse characteristics according to claim 1, characterized in that: In S1, the parameters of the communication device are initialized, and a time vector is generated based on the initialized parameters, including: Initialize the parameters of the communication device, including the center frequency ,bandwidth and sampling frequency ; Based on the sampling frequency Generate a time vector.
3. The signal recovery method based on signal noise and sparse characteristics according to claim 2, characterized in that: Key parameters in street fighting scenarios include: Noise Level , Electromagnetic Interference Level , sparsity and the number of multipath paths .
4. The signal recovery method based on signal noise and sparse characteristics according to claim 1, characterized in that: The signal modeling of the street fighting scene includes: Define a dictionary matrix , including signal characteristics existing in street fighting environments; Generate sparse signals, sparse signals in street fighting Modeling; Collect complex environmental information and analyze signals containing noise and interference Modeling: ； in, It's noise and interference.
5. The signal recovery method based on signal noise and sparse characteristics according to claim 4, characterized in that: In S4, based on the signal modeling of the street fighting scene, the OMP-SSRN algorithm is used to perform preliminary signal recovery to obtain a preliminary recovered signal, including: Based on the signal modeling of the street fighting scene, the residual is calculated: ； in, To observe the signal; is the current sparse signal estimate; Calculate the correlation vector : ； in, is the dictionary matrix The transpose of Select the most relevant signal components ; The most relevant signal component is the position of the element with the maximum value in the correlation vector; Updating sparse signal representation .
6. The signal recovery method based on signal noise and sparse characteristics according to claim 5, characterized in that: In S5, the initial restored signal is further optimized using the L1 norm penalty term, including: According to the following formula, the L1 norm penalty term is used to optimize the initial recovery signal to obtain a sparse representation of the signal: ； in, is the regularization parameter.
7. The signal recovery method based on signal noise and sparse characteristics according to claim 6, characterized in that: In S5, according to the change of the street fighting battlefield environment, the OMP-SSRN algorithm parameters are dynamically adjusted until the OMP-SSRN algorithm is iteratively converged to obtain the recovery signal, including: Adaptively adjust the sparsity parameters in the OMP-SSRN algorithm according to the changes in the street fighting battlefield environment and the regularization parameter of the L1 norm penalty term ; The preset iteration threshold is used to adjust the sparsity parameter in the OMP-SSRN algorithm. and the regularization parameter of the L1 norm penalty term Iterate until the algorithm converges and obtains the restored signal; Among them, the convergence condition is that the residual and error in the OMP-SSRN algorithm are lower than the preset threshold.
8. The signal recovery method based on signal noise and sparse characteristics according to claim 7, characterized in that: In S6, calculating the error between the restored signal and the original signal includes: The error ratio between the restored signal and the original signal is: 。 9. A signal recovery device based on signal noise and sparse characteristics, the signal recovery device based on signal noise and sparse characteristics is used to implement the signal recovery method based on signal noise and sparse characteristics as claimed in any one of claims 1 to 8, characterized in that: The device comprises: An initialization module, used to initialize the parameters of the communication device and generate a time vector based on the initialized parameters; A parameter acquisition module, used to acquire key parameters in the street fighting scene and input the parameters to the communication device; A modeling module, used for signal modeling of the street fighting scene based on key parameters in the input street fighting scene; A preliminary recovery module is used to perform preliminary signal recovery based on the signal modeling of the street fighting scene using the OMP-SSRN algorithm to obtain a preliminary recovery signal; The signal recovery module is used to further optimize the initial recovery signal using the L1 norm penalty term to obtain the OMP-SSRN algorithm; according to the changes in the street fighting battlefield environment, the OMP-SSRN algorithm parameters are dynamically adjusted until the OMP-SSRN algorithm iteratively converges to obtain the recovery signal; The evaluation module is used to calculate the error between the restored signal and the original signal, evaluate the restoration quality, and complete the signal recovery based on signal noise and sparse characteristics for street fighting.
10. A signal recovery device based on signal noise and sparse characteristics, characterized in that: The signal recovery device based on signal noise and sparse characteristics includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 8 is implemented.