A joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system

Abstract

This invention proposes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing (OFDM) system, belonging to the field of non-destructive measurement. It solves the problems of balancing high resolution and anti-interference capability, and the trade-off between real-time performance and measurement accuracy. The method includes: receiving OFDM signals and performing time-frequency decomposition; decomposing the signal into multiple orthogonal subcarriers using a fast Fourier transform; dynamically disabling interfered subcarriers based on their signal-to-noise ratio (SNR); performing Bayesian phase compensation on the retained subcarriers; iteratively optimizing the subcarrier switching states and phase compensation parameters, dynamically adjusting the SNR threshold to separate signal and noise; reconstructing the sparse signal using compressed sensing technology; and improving ranging and imaging resolution using an orthogonal matched pursuit algorithm. It is mainly used in fields such as underground target detection and dynamic radar sensing networks.

Description

Technical Field

[0001] This invention belongs to the field of non-destructive measurement, and in particular relates to a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system. Background Technology

[0002] Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier modulation technique characterized by distributing the data to be transmitted across multiple orthogonal subcarriers. These subcarriers are closely arranged in the frequency domain and do not interfere with each other, thus achieving high spectral efficiency. Each subcarrier of an OFDM signal can be independently modulated and demodulated, and efficient time-frequency conversion is achieved through Fast Fourier Transform (FFT). Due to this advantage, OFDM technology has been widely used in high-speed wireless communication, radar ranging, target imaging, and real-time dynamic measurement, such as 5G communication, radar sensing networks, UAV positioning, and intelligent transportation systems.

[0003] Currently, in the field of nondestructive testing (NDT), pulse signals and stepped-frequency continuous wave (SFCW) signals are mainly relied upon for NDT and imaging. However, both signal systems have certain limitations in practical applications. Traditional SFCW signals achieve wide bandwidth coverage by progressively scanning frequency points. While performing well in high-resolution target imaging and ranging, their progressive transmission and acquisition mechanism leads to significant deficiencies in noise immunity and dynamic environment adaptability. Since each frequency point is measured only once, SFCW signals are susceptible to noise interference, especially when the interfering signal is within the signal band. Noise components are repeatedly acquired and dominate the spectrum synthesis, making effective suppression difficult. Furthermore, the measurement speed of SFCW signals is linearly related to the number of frequency points, making it difficult to meet the real-time requirements of dynamic environment applications. In contrast, while pulse signals have advantages in time domain resolution, achieving a time resolution of 0.1 ns, their instantaneous peak power is extremely high, resulting in low power efficiency. Moreover, pulse signals are susceptible to narrowband interference, further limiting their application in practical scenarios. Summary of the Invention

[0004] In view of this, the present invention aims to propose a joint optimization and sparse signal processing method based on orthogonal frequency division multiplexing system to solve the problem of difficulty in balancing high resolution and anti-interference capability, and the mutual constraint between real-time performance and measurement accuracy.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system, the method comprising:

[0007] Step 1: Receive the OFDM signal and perform time-frequency decomposition. Decompose the signal into multiple orthogonal subcarriers using Fast Fourier Transform.

[0008] Step 2: Dynamically disable interfered subcarriers based on subcarrier signal-to-noise ratio, including: estimating the noise power of each subcarrier; calculating the instantaneous signal-to-noise ratio of the subcarrier, and disabling subcarriers with a signal-to-noise ratio below a preset threshold;

[0009] Step 3: Perform Bayesian phase compensation on the retained subcarriers;

[0010] Step 4: Iteratively optimize the subcarrier switching state and phase compensation parameters, and dynamically adjust the signal-to-noise ratio threshold to separate the signal from the noise;

[0011] Step 5: Reconstruct the sparse signal based on compressed sensing technology, and improve ranging and imaging resolution using the orthogonal matching pursuit algorithm.

[0012] Furthermore, a preferred embodiment is proposed, wherein step 1 includes:

[0013]

[0014] Among them, Y m Let y(n) be the frequency domain signal value on the m-th subcarrier, y(n) be the discrete sampled value of the time-domain received signal, and n be the index of the time-domain sampling point. c is the total number of subcarriers in the system or the number of points in the DFT, m is the index of the frequency domain subcarrier, e is the base of the natural logarithm, and j is the imaginary unit.

[0015] Furthermore, a preferred method is proposed, wherein estimating the noise power of each subcarrier in step 2 includes:

[0016]

[0017] Where k is the subcarrier index, N w Y is the window length. i The frequency domain received signal is the i-th subcarrier.

[0018] Furthermore, a preferred method is proposed, wherein a preset threshold γ is used in step 2. th The preset is based on the Neyman-Pearson criterion, which shuts down subcarriers with a signal-to-noise ratio below a preset threshold, including:

[0019]

[0020] Among them, Xk This is the subcarrier switching state.

[0021] Furthermore, a preferred embodiment is proposed, wherein step 3 includes:

[0022] Construct a Bayesian posterior probability model, and obtain the maximized posterior probability based on the Bayesian posterior probability model:

[0023] The optimal time delay is estimated by maximizing the posterior probability.

[0024]

[0025] in, f is the optimal time delay value to be estimated. k Y is the normalized frequency of the k-th subcarrier. k To receive the signal, σ φ Let σ be the variance of the phase noise. τ Let μ be the prior variance of the time delay. τ The prior mean of the time delay;

[0026] Phase alignment is performed on the compensated frequency domain signal:

[0027] Furthermore, a preferred method is proposed, wherein the alternating iterative optimization in step 4 includes:

[0028] Fixed subcarrier switching state X k Optimize phase φ k Align the residual interference phase to the main lobe of the channel response:

[0029]

[0030] Among them, H k This represents the channel frequency response of the k-th subcarrier with multipath phase offset. The estimated interference phase deviation of the k-th subcarrier during the nth iteration or symbol period;

[0031] Fixed phase φ k Update subcarrier switch state X k And dynamically adjust the signal-to-noise ratio threshold through adaptive learning rate α.

[0032] Furthermore, a preferred embodiment is proposed, wherein the update formula for the signal-to-noise ratio threshold is:

[0033]

[0034] in, The signal-to-noise ratio threshold for the (n-1)th iteration. This is the symbol estimate for the (n-1)th iteration.

[0035] Furthermore, a preferred embodiment is proposed, wherein the compressed sensing reconstruction in step 5 includes:

[0036] Constructing an observation model:

[0037] y = Φx + n

[0038] in, For the measurement matrix, For observation noise, x is the sparse signal, and y is the target echo signal;

[0039] The reconstruction of the original signal is conceived as an optimization problem;

[0040] The optimization problem is solved using l1-norm relaxation.

[0041]

[0042] Where x is the sparse signal to be solved, Φ is the measurement matrix, and ∈ is the error tolerance;

[0043] The support set and residuals are iteratively updated using the orthogonal matching pursuit algorithm until convergence.

[0044] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system as described in any of the preceding claims.

[0045] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system as described above.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] Beneficial technical effects

[0048] 1. The method proposed in this invention effectively avoids narrowband noise pollution and bandwidth waste caused by traditional fixed filtering by estimating subcarrier noise power in real time and dynamically shutting down interfered subcarriers based on a signal-to-noise ratio threshold. Compared with the traditional point-by-point sampling mechanism of SFCW signals, this invention significantly improves anti-interference performance while preserving effective frequency bands, and is especially suitable for dynamic scenarios in complex electromagnetic environments.

[0049] 2. The method proposed in this invention employs Bayesian estimation to optimally compensate for phase shifts caused by multipath delays. By combining prior distributions and observational data, it solves the phase alignment problem under low signal-to-noise ratio conditions. Compared with traditional linear phase correction methods, this method improves compensation accuracy by more than 30% by fusing prior knowledge of multipath delays through a probabilistic model, and its advantages are particularly significant in dense multipath environments.

[0050] 3. The method proposed in this invention dynamically updates the signal-to-noise ratio threshold by alternately iterating the subcarrier switching state and phase parameters, thereby achieving gradual separation of signal and noise. This mechanism overcomes the limitation of traditional single-step optimization being prone to getting trapped in local optima. While ensuring the integrity of the useful signal, it improves the suppression efficiency of residual interference by more than 40%, significantly enhancing the system's robustness.

[0051] 4. The method proposed in this invention combines compressed sensing technology and utilizes the temporal sparsity characteristics of target scattering points to achieve high-precision signal reconstruction through an orthogonal matching pursuit algorithm. Compared with traditional frequency domain synthesis methods, this method effectively expands the virtual bandwidth, improves the distance resolution to the subwavelength level, and breaks through hardware bandwidth limitations.

[0052] 5. The method proposed in this invention utilizes a dynamic subcarrier disabling mechanism to reduce the amount of invalid data processing, compressed sensing technology to reduce sampling rate requirements, and a joint optimization framework to reduce computational complexity through step-by-step iteration. Experiments show that, at the same number of frequency points, the processing speed of this method is more than 50% faster than that of traditional SFCW signals, and the peak power consumption is reduced by 60%, meeting the real-time requirements of scenarios such as UAV positioning and intelligent transportation.

[0053] 6. The method proposed in this invention, through multi-level linkage of noise avoidance, phase compensation and sparse reconstruction, can maintain stable ranging and imaging accuracy even under extreme conditions such as low signal-to-noise ratio (SNR<0dB) and dense multipath interference (delay spread exceeds the signal period). Applicable scenarios cover industrial non-destructive testing, underground target detection and dynamic radar perception networks. Attached Figure Description

[0054] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0055] Figure 1 This is a flowchart of a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system as described in this invention;

[0056] Figure 2 This paper compares the multipath interference suppression effect of the method described in this invention with that of traditional methods. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other, and the described embodiments are only some embodiments of the present invention, not all embodiments.

[0058] Implementation Method 1, see [link] Figure 1 This embodiment describes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system. The method includes:

[0059] Step 1: Receive the OFDM signal and perform time-frequency decomposition. Decompose the signal into multiple orthogonal subcarriers using Fast Fourier Transform.

[0060] Step 2: Dynamically disable interfered subcarriers based on subcarrier signal-to-noise ratio, including: estimating the noise power of each subcarrier; calculating the instantaneous signal-to-noise ratio of the subcarrier, and disabling subcarriers with a signal-to-noise ratio below a preset threshold;

[0061] Step 3: Perform Bayesian phase compensation on the retained subcarriers;

[0062] Step 4: Iteratively optimize the subcarrier switching state and phase compensation parameters, and dynamically adjust the signal-to-noise ratio threshold to separate the signal from the noise;

[0063] Step 5: Reconstruct the sparse signal based on compressed sensing technology, and improve ranging and imaging resolution using the orthogonal matching pursuit algorithm.

[0064] This implementation addresses the problem of traditional methods' inability to flexibly suppress narrowband interference by shutting down contaminated subcarriers through noise power estimation and signal-to-noise ratio threshold analysis. This not only preserves effective signal frequency bands but also avoids bandwidth waste caused by traditional fixed filtering, thus improving the system's robustness in complex electromagnetic environments.

[0065] Furthermore, the method proposed in this embodiment also solves the problem of phase distortion caused by multipath delay, and achieves phase alignment under low signal-to-noise ratio conditions based on Bayesian inference phase compensation technology.

[0066] Furthermore, the method proposed in this embodiment adopts an alternating optimization strategy of subcarrier switching state and phase parameter, and solves the objective function step by step through iterative solution, while taking into account the coordinated adjustment of discrete variables and continuous parameters.

[0067] Furthermore, the method proposed in this embodiment introduces compressed sensing theory, which utilizes the sparsity of signals in the time domain to effectively increase virtual bandwidth and improve the resolution of ranging and imaging.

[0068] Implementation Method Two: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method One. Step 1 includes:

[0069]

[0070] Among them, Y m Let y(n) be the frequency domain signal value on the m-th subcarrier, y(n) be the discrete sampled value of the time-domain received signal, and n be the index of the time-domain sampling point. c is the total number of subcarriers in the system or the number of points in the DFT, m is the index of the frequency domain subcarrier, e is the base of the natural logarithm, and j is the imaginary unit.

[0071] Implementation Method 3: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method 1. Step 2, estimating the noise power of each subcarrier, includes:

[0072]

[0073] Where k is the subcarrier index, N w Y is the window length. i The frequency domain received signal is the i-th subcarrier.

[0074] Implementation Method Four: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method One. In step 2, the preset threshold γ... th The preset is based on the Neyman-Pearson criterion, which shuts down subcarriers with a signal-to-noise ratio below a preset threshold, including:

[0075]

[0076] Among them, X k This is the subcarrier switching state.

[0077] Implementation Method 5: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method 2. Step 3 includes:

[0078] Construct a Bayesian posterior probability model, and obtain the maximized posterior probability based on the Bayesian posterior probability model:

[0079] The optimal time delay is estimated by maximizing the posterior probability.

[0080]

[0081] in, f is the optimal time delay value to be estimated.k Y is the normalized frequency of the k-th subcarrier. k To receive the signal, σ φ Let σ be the variance of the phase noise. τ Let μ be the prior variance of the time delay. τ The prior mean of the time delay;

[0082] Phase alignment is performed on the compensated frequency domain signal:

[0083]

[0084] Implementation Method Six: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method Five. The alternating iterative optimization in step 4 includes:

[0085] Fixed subcarrier switching state X k Optimize phase φ k Align the residual interference phase to the main lobe of the channel response:

[0086]

[0087] Among them, H k This represents the channel frequency response of the k-th subcarrier with multipath phase offset. The estimated interference phase deviation of the k-th subcarrier during the nth iteration or symbol period;

[0088] Fixed phase φ k Update subcarrier switch state X k And dynamically adjust the signal-to-noise ratio threshold through adaptive learning rate α.

[0089] Implementation Method Seven: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method Six. The update formula for the signal-to-noise ratio threshold is:

[0090]

[0091] in, The signal-to-noise ratio threshold for the (n-1)th iteration. This is the symbol estimate for the (n-1)th iteration.

[0092] Implementation Method Eight: This implementation method further defines the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Implementation Method One. The compressed sensing reconstruction in step 5 includes:

[0093] Constructing an observation model:

[0094] y = Φx + n

[0095] in, For the measurement matrix, For observation noise, x is the sparse signal, and y is the target echo signal;

[0096] The reconstruction of the original signal is conceived as an optimization problem;

[0097] The optimization problem is solved using l1-norm relaxation.

[0098]

[0099] Where x is the sparse signal to be solved, Φ is the measurement matrix, and ∈ is the error tolerance;

[0100] The support set and residuals are iteratively updated using the orthogonal matching pursuit algorithm until convergence.

[0101] Implementation Method Nine: A computer device according to this implementation method includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system according to any one of Implementation Methods One to Eight.

[0102] Implementation Method 10: A computer-readable storage medium according to this embodiment stores a computer program, which, when executed by a processor, performs the steps of a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system as described in any one of Implementation Methods 1 to 8.

[0103] Implementation Method 11, see below Figure 2 This embodiment describes a specific example of the joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system described in Embodiment 1. It also serves to explain Embodiments 2 through 8. Specifically:

[0104] The first step is to perform time-frequency decomposition and subcarrier decomposition. Based on orthogonal frequency division multiplexing theory, a model of the received signal is constructed, assuming the signal obtained after mixing and sampling is:

[0105]

[0106] Among them, H k X represents the channel frequency response of the k-th subcarrier including multipath phase offset. k ∈{0,1} represents the subcarrier activation state control variable used for noise avoidance. This represents complex Gaussian white noise.

[0107] Perform a Fast Fourier Transform on the acquired raw signal to convert the signal into the frequency domain and decompose it into N c Processing subcarriers with orthogonal properties:

[0108]

[0109] The second step is to disable smart subcarriers. This is done after obtaining N... c After identifying subcarriers with orthogonal characteristics, the noise power of each subcarrier is estimated. This invention employs a sliding window-based method to statistically analyze the frequency domain power of each subcarrier.

[0110]

[0111] Where N w This is the window length.

[0112] To minimize the negative impact of noise on ranging and imaging accuracy, this invention employs a subcarrier intelligent selection mechanism based on a signal-to-noise ratio threshold. This is achieved by introducing a binary activation factor X. k Where k is the subcarrier index, the optimal allocation of spectrum resources for the signal is achieved: when the instantaneous signal-to-noise ratio (SNR) of a subcarrier is lower than a preset threshold, the activation factor is set to 0, indicating that the subcarrier is dynamically discarded; conversely, when the SNR is higher than the preset value, the activation factor is set to 1, indicating that the subcarrier is reserved for subsequent signal processing. This mechanism achieves efficient allocation of spectrum resources and effective avoidance of noise interference by real-time monitoring and evaluation of the SNR characteristics of subcarriers, thereby ensuring the robustness and accuracy of the ranging and imaging system in complex environments.

[0113]

[0114] Where γ th Based on the Neyman-Pearson criterion.

[0115] The third step is to perform Bayesian phase compensation on the preserved signal. To achieve accurate correction of the echo signal, this invention employs a phase compensation algorithm based on Bayesian estimation. In free space, ranging and imaging signals inevitably encounter multipath interference during propagation, causing nonlinear distortion of the echo's phase characteristics. Specifically, multipath propagation causes a delay in the signal in the time domain, which then manifests as a linear phase shift in the frequency domain.

[0116]

[0117] It is assumed that the delay τ follows a Gaussian prior.

[0118] To mitigate this effect, this invention constructs a Bayesian posterior probability model and combines it with prior phase distribution and observation data to optimally estimate and compensate for the phase distortion of the echo signal. Maximizing the posterior probability P(τ|Y,X) can be expressed as:

[0119]

[0120] After simplifying using log-likelihood, we get:

[0121]

[0122] The frequency domain signal after phase compensation in this way can be represented as:

[0123]

[0124] The fourth step involves iterative optimization and adaptive parameter adjustment. Through an alternating optimization mechanism, the system dynamically suppresses residual interference while ensuring the effective maintenance of the signal-to-noise ratio and integrity of the useful signal.

[0125]

[0126] Among them, X k ∈{0,1} represents the switching state of the subcarrier, φ k This indicates that adjacent subcarriers have continuous phase.

[0127] The optimization process employs a step-by-step iterative strategy, adjusting system parameters sequentially. First, the subcarrier switching state X is fixed. k Optimize phase φ k Align the residual interference phase to the main lobe of the channel response:

[0128]

[0129] Then, fix the phase φ k Update subcarrier switch state X k Calculate the signal-to-interference-plus-noise ratio:

[0130]

[0131] Then perform dynamic threshold updates:

[0132]

[0133] Where α is the learning rate. Then, binary decision-making is implemented:

[0134]

[0135] In the fifth stage, the system employs compressed sensing technology to achieve sparse data reconstruction. Based on the physical characteristics of the detected or imaged target, its scattering points often exhibit a sparse distribution in the time domain, meaning that only a small number of scattering points significantly contribute to the echo signal. Therefore, the following compressed sensing mathematical model can be constructed:

[0136] Assuming the target echo signal For the observed values, and their relationship with sparse signals The relationship can be represented as:

[0137] y = Φx + n (14)

[0138] in For the measurement matrix, To observe noise. Since x is sparsity, meaning its number of non-zero elements K << N, accurate reconstruction of the original signal can be achieved by solving the following optimization problem:

[0139]

[0140] Where ||·||0 represents the l0 norm, and ∈ represents the noise tolerance. To reduce computational complexity, l1-norm relaxation is typically used, transforming the problem into a convex optimization form:

[0141]

[0142] Then, the orthogonal matching pursuit algorithm is used for step-by-step analysis, with the initial residual value being r0 = y.

[0143] Support set selection at step t:

[0144]

[0145] Updated support set:

[0146] Λ t =Λ t-1 ∪{i t} (18)

[0147] Updated estimates:

[0148]

[0149] Update residuals:

[0150]

[0151] Iterate to ||r t ||2<∈ or the maximum number of iterations is reached.

[0152] Using the methods described above, the system can achieve high-precision ranging and imaging at the subwavelength level. Compressed sensing extends the effective bandwidth to a virtual bandwidth of B.virt =N c Δf eff Distance resolution:

[0153]

[0154] In summary, this invention proposes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing (OFDM) system. First, the received signal undergoes time-frequency domain conversion and is decomposed into multiple orthogonal subcarriers using a fast Fourier transform (FFT) algorithm. Based on this, by analyzing the signal-to-noise ratio (SNR) and spectral characteristics of each subcarrier in real time, the system intelligently identifies and dynamically disables subcarriers contaminated by narrowband noise, thereby effectively suppressing the propagation of interference signals. Furthermore, a Bayesian estimation algorithm is used to optimally compensate the signal phase to eliminate phase shift caused by multipath effects and improve signal coherence. Through iterative optimization of the subcarrier allocation strategy and phase compensation parameters, signal and noise are efficiently separated, enhancing the accuracy of target information extraction. Finally, compressed sensing technology is combined to reconstruct the sparse signal, improving the spatial resolution of detection and imaging.

[0155] like Figure 2 As shown, compared with traditional methods, this invention can effectively suppress multipath interference, specifically in the following ways:

[0156] 1. The signal strength of the direct path is greater, approximately 24000uV, which is much greater than the 14000uV of the traditional method;

[0157] 2. The method proposed in this invention does not have the problem of direct path and multipath interference superposition, while the traditional method has multipath interference and direct signal superposition, resulting in the phenomenon of direct path peak broadening.

[0158] 3. The method proposed in this invention can effectively suppress the amplitude of multipath interference, with a maximum amplitude of 10000uV, while traditional methods cannot effectively suppress the influence of multipath interference, and the amplitude formed by multipath aliasing is 16500uV.

[0159] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the published pending claims.

Claims

1. A joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system, characterized in that, The method includes: Step 1: Receive the orthogonal frequency division multiplexing signal and perform time-frequency decomposition. Decompose the signal into multiple orthogonal subcarriers through fast Fourier transform. Step 2: Dynamically disable interfered subcarriers based on subcarrier signal-to-noise ratio, including: estimating the noise power of each subcarrier; calculating the instantaneous signal-to-noise ratio of the subcarrier, and disabling subcarriers with a signal-to-noise ratio lower than a preset threshold according to a preset threshold; Step 3: Perform Bayesian phase compensation on the retained subcarriers; Step 4: Iteratively optimize the subcarrier switching state and phase compensation parameters, and dynamically adjust the preset threshold to separate the signal from the noise; Step 5: Reconstruct the sparse signal based on compressed sensing technology, and improve ranging and imaging resolution using the orthogonal matching pursuit algorithm; The preset threshold in step 2 is based on the Neyman-Pearson criterion. According to the preset threshold, subcarriers with a signal-to-noise ratio lower than the preset threshold are turned off, including: in, Subcarrier switching state, The preset threshold; Step 3 includes: Construct a Bayesian posterior probability model, and obtain the maximized posterior probability based on the Bayesian posterior probability model: The optimal time delay is estimated by maximizing the posterior probability. in, The optimal time delay value to be estimated is... Let k be the normalized frequency of the k-th subcarrier. In order to receive signals, The variance of the phase noise. The prior variance of the time delay. Let be the prior mean of the time delay. The total number of subcarriers in the system; Phase alignment is performed on the compensated frequency domain signal: ； The alternating iterative optimization in step 4 includes: Fix the switching state of the kth subcarrier Optimize the phase of the k-th subcarrier Align the residual interference phase to the main lobe of the channel response: in, It indicates the first The channel frequency response of the subcarrier includes multipath phase offset. The estimated interference phase deviation of the k-th subcarrier during the nth iteration or symbol period; Fixed phase Update subcarrier switch status And through adaptive learning rate The preset threshold is dynamically adjusted.

2. The joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system according to claim 1, characterized in that, Step 1 includes: in, Let m be the frequency domain signal value on the m-th subcarrier. Let n be the discrete sampled values ​​of the received signal in the time domain, where n is the index of the time-domain sampling point and m is the index of the frequency-domain subcarrier. The base of the natural logarithm, It is the imaginary unit.

3. The joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system according to claim 1, characterized in that, Step 2, estimating the noise power of each subcarrier, includes: in, For subcarrier index, For window length, The frequency domain received signal for the i-th subcarrier. To estimate the noise power of each subcarrier.

4. The joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system according to claim 1, characterized in that, The reconstruction in step 5 includes: Constructing an observation model: in, For the measurement matrix, To observe the noise, For sparse signals, For the target echo signal; The reconstruction of the original signal is conceived as an optimization problem; The optimization problem is solved using l-norm relaxation. The support set and residuals are iteratively updated using the orthogonal matching pursuit algorithm until convergence.

5. A computer device, characterized in that: The system includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing system as described in any one of claims 1-4.

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