Method and system for detecting ofdm-im power beidou signal

Abstract

The application discloses a kind of detection method and system of OFDM-IM electric power beidou signal, the method is estimated to the signal sparsity of OFDM-IM signal, the sparsity variation of real-time perception electric power beidou signal, when sparsity is higher, small-scale subcarrier group is used, to reduce single group detection complexity, when sparsity is lower, large-scale subcarrier group is used, to improve overall processing efficiency, so as to consider detection accuracy and real-time in complex, time-varying electric power beidou communication environment, with strong adaptive ability and lower complexity.And, by parameter-channel state mapping model, can be adjusted CG-OAMP algorithm parameters according to channel estimation result and signal sparsity in real time, so that CG-OAMP algorithm always works in optimal state, avoid the problem of insufficient robustness caused by traditional fixed parameters, also improve signal detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of power grid BeiDou signal detection technology, and in particular, to a method and system for detecting OFDM-IM power grid BeiDou signals, an electronic device, and a computer-readable storage medium. Background Technology

[0002] As a core infrastructure of society, the safe and stable operation of the power system is directly related to the national economy and people's livelihood. The BeiDou Navigation Satellite System, with its advantages of high positioning accuracy, strong anti-interference capability, and wide coverage, has been deeply integrated into key aspects of the power system, such as inspection and monitoring, fault location, and dispatch communication, becoming a core supporting technology for ensuring the safe and sustainable operation of the power system. Among these technologies, OFDM-IM (Orthogonal Frequency Division Multiplexing with Index Modulation) technology transmits additional information by activating some subcarriers, and boasts advantages such as high spectral efficiency, strong anti-interference capability, and flexible configuration. It is highly compatible with the BeiDou communication needs of the power sector and is widely used in BeiDou terminals for power systems. However, OFDM-IM power grid BeiDou signals face challenges at the receiver end, such as excessively high PAPR (Peak to Average Ratio), uneven subcarrier utilization, and complex signal detection, making signal detection difficult. Moreover, with the continuous expansion of the power grid and the widespread adoption of BeiDou applications, the number of power grid BeiDou terminals is growing exponentially. The intertwining and superposition of multi-dimensional communication links between space, satellite, and ground makes the power grid BeiDou signal environment increasingly complex, with frequent sudden changes and interference signals, posing a severe challenge to accurate signal detection.

[0003] Currently, commonly used methods for detecting BeiDou signals in power grids include maximum likelihood detection (MLD), linear detection algorithms based on channel state information (CSI) (such as MMSE and LMMSE algorithms), and nonlinear detection methods based on sparse signal recovery (such as OMP and BP algorithms). Among these, while maximum likelihood detection can achieve theoretically optimal detection performance, its complexity increases exponentially with the number of subcarriers and the modulation order. This results in an excessively heavy computational burden in high-order index modulation scenarios for OFDM-IM power grid BeiDou signals, making it completely unsuitable for power grid BeiDou applications. Real-time communication is required; however, linear detection algorithms based on channel state information rely on accurate channel estimation, while power grid BeiDou signals face a dynamically changing air-space-ground channel environment, making CSI tracking difficult and causing a sharp decline in detection performance at low signal-to-noise ratios. Nonlinear detection methods based on sparse signal recovery require multiple iterations, have slow convergence speeds, and are sensitive to initial conditions, failing to effectively balance iterative efficiency and detection accuracy. Furthermore, they lack robustness in high-dimensional, high-sparse OFDM-IM power grid BeiDou signal scenarios, making them unsuitable for the complex and ever-changing communication environments of OFDM-IM power grid BeiDou signals. Therefore, there is an urgent need for an OFDM-IM power grid BeiDou signal detection method that combines low complexity, high robustness, and strong adaptability to overcome the bottlenecks of existing technologies. Summary of the Invention

[0004] This invention provides a method and system for detecting OFDM-IM power BeiDou signals, an electronic device, and a computer-readable storage medium, which have the advantages of low complexity, high robustness, and strong adaptability, and also improve the accuracy of signal detection.

[0005] According to one aspect of the present invention, a method for detecting OFDM-IM power grid BeiDou signals is provided, comprising the following: It receives hybrid electric BeiDou signals and separates OFDM-IM signals and pilot signals through preprocessing. Channel estimation is performed based on pilot signals to obtain channel estimation results; The signal sparsity of the OFDM-IM signal is estimated, and the subcarriers are adaptively and dynamically grouped according to the estimated signal sparsity to obtain the complex-valued OFDM-IM signal model. If the estimated signal sparsity is greater than the preset sparsity threshold, a small-scale subcarrier group is used; if the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used. The channel estimation results and signal sparsity are input into a pre-trained parameter-channel state mapping model to obtain the optimized parameters of the CG-OAMP algorithm; wherein, the parameter-channel state mapping model characterizes the mapping relationship between channel state information and signal sparsity and CG-OAMP algorithm parameters; The complex-valued OFDM-IM signal model is converted into a real-valued signal, and the CG-OAMP algorithm is used to perform hierarchical iterative detection on the real-valued signal based on optimized parameters to obtain the reconstructed power BeiDou signal.

[0006] Furthermore, the process of performing channel estimation based on pilot signals to obtain channel estimation results includes the following: First, the LMMSE algorithm is used to perform an initial estimation of channel state information based on the pilot signal. Then, the Kalman filter algorithm is used to perform iterative estimation with the initial estimation result as the initial state value, and output accurate channel state information.

[0007] Furthermore, the process of estimating the signal sparsity of the OFDM-IM signal includes the following: The subcarrier activation state of the OFDM-IM signal is modeled as a Bernoulli-Gaussian model. A sparsity parameter is used to represent the probability of subcarrier activation to achieve sparse prior modeling. Then, a posterior update is performed based on the current received signal to obtain the posterior probability of subcarrier activation. The posterior probabilities of all subcarriers are statistically averaged to obtain the sparsity estimate at the current time. Furthermore, when the sparsity change between adjacent time steps exceeds a preset threshold, a time-weighted mechanism is introduced for posterior update.

[0008] Furthermore, after separating the OFDM-IM signal, wavelet multi-scale decomposition is performed on the OFDM-IM signal to extract the high-frequency components of the signal. The wavelet modulus maxima algorithm is used to detect whether there is abrupt change signal. If there is abrupt change signal, the iteration mode of the CG-OAMP algorithm is set to accelerated iteration mode. If there is nobrupt change signal, the iteration mode of the CG-OAMP algorithm is set to normal iteration mode.

[0009] Furthermore, if there is no mutation signal, the iteration stops when the iteration error is less than the fast convergence threshold during the hierarchical iterative detection process. If there is a mutation signal, the iteration stops only when the iteration error is less than the precise optimization threshold, where the precise optimization threshold is less than the fast convergence threshold.

[0010] Furthermore, before using the CG-OAMP algorithm to perform hierarchical iterative detection of real-valued signals based on optimized parameters, the following is also included: The real-valued signal is input into the pre-trained CNN-LSTM model to make a preliminary estimate of the power grid Beidou signal, and the preliminary estimated signal value is used as the initial value for the iteration of the CG-OAMP algorithm.

[0011] Furthermore, the hierarchical iterative detection process also includes the following: Calculate the confidence level of the signal estimate. If the confidence level is greater than or equal to the preset confidence level threshold, the minimum distance decision rule is adopted. If the confidence level is less than the preset confidence level threshold, the maximum a posteriori probability decision rule is adopted.

[0012] In addition, the present invention also provides a detection system for OFDM-IM power grid BeiDou signals, comprising: The signal preprocessing module is used to receive mixed electric power BeiDou signals and separate OFDM-IM signals and pilot signals through preprocessing; The channel estimation module is used to perform channel estimation based on pilot signals and obtain the channel estimation results. The subcarrier dynamic grouping module is used to estimate the signal sparsity of the OFDM-IM signal and adaptively and dynamically group the subcarriers according to the estimated signal sparsity to obtain the complex-valued OFDM-IM signal model. If the estimated signal sparsity is greater than the preset sparsity threshold, a small-scale subcarrier group is used; if the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used. The parameter optimization module is used to input the channel estimation results and signal sparsity into a pre-trained parameter-channel state mapping model to obtain the optimized parameters of the CG-OAMP algorithm; wherein, the parameter-channel state mapping model represents the mapping relationship between channel state information and signal sparsity and CG-OAMP algorithm parameters; The signal reconstruction module is used to convert the complex-valued OFDM-IM signal model into a real-valued signal. The CG-OAMP algorithm is used to perform hierarchical iterative detection on the real-valued signal based on optimized parameters to obtain the reconstructed power BeiDou signal.

[0013] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0014] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for detecting OFDM-IM power BeiDou signals, wherein the computer program executes the steps of the method described above when running on a computer.

[0015] The present invention has the following beneficial effects: The OFDM-IM power grid BeiDou signal detection method of this invention estimates the signal sparsity of the OFDM-IM signal, senses the sparsity changes of the power grid BeiDou signal in real time, and adaptively and dynamically groups subcarriers according to the estimated signal sparsity. When the sparsity is high, a small-scale subcarrier group is used to reduce the detection complexity of a single group; when the sparsity is low, a large-scale subcarrier group is used to improve the overall processing efficiency. Thus, it balances detection accuracy and real-time performance in complex and time-varying power grid BeiDou communication environments, exhibiting strong adaptability and low complexity. Furthermore, through the parameter-channel state mapping model, the CG-OAMP algorithm parameters can be adaptively adjusted in real time according to the channel estimation results and signal sparsity, ensuring that the CG-OAMP algorithm always operates in an optimal state. This avoids the insufficient robustness problem caused by traditional fixed parameters and also improves signal detection accuracy.

[0016] In addition, the OFDM-IM power BeiDou signal detection system of the present invention also has the above-mentioned advantages.

[0017] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, 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: Figure 1 This is a flowchart illustrating the OFDM-IM power grid BeiDou signal detection method according to a preferred embodiment of this application; Figure 2 This is a schematic diagram of the module structure of an OFDM-IM power Beidou signal detection system according to another embodiment of this application. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Reference Figure 1 A preferred embodiment of this application provides a method for detecting OFDM-IM power grid BeiDou signals, including the following: Step S1: Receive the hybrid electric BeiDou signal and separate the OFDM-IM signal and pilot signal through preprocessing; Step S2: Perform channel estimation based on the pilot signal to obtain the channel estimation result; Step S3: Estimate the signal sparsity of the OFDM-IM signal, and adaptively and dynamically group the subcarriers according to the estimated signal sparsity to obtain the complex-valued OFDM-IM signal model; wherein, if the estimated signal sparsity is greater than the preset sparsity threshold, a small-scale subcarrier group is used, and if the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used. Step S4: Input the channel estimation results and signal sparsity into the pre-trained parameter-channel state mapping model to obtain the optimized parameters of the CG-OAMP algorithm; wherein, the parameter-channel state mapping model represents the mapping relationship between channel state information and signal sparsity and CG-OAMP algorithm parameters; Step S5: Convert the complex-valued OFDM-IM signal model into a real-valued signal, and use the CG-OAMP algorithm to perform hierarchical iterative detection on the real-valued signal based on the optimized parameters to obtain the reconstructed power BeiDou signal.

[0021] It is understood that the OFDM-IM power grid BeiDou signal detection method in this embodiment estimates the signal sparsity of the OFDM-IM signal, senses the sparsity changes of the power grid BeiDou signal in real time, and adaptively and dynamically groups the subcarriers according to the estimated signal sparsity. When the sparsity is high, a small-scale subcarrier group is used to reduce the detection complexity of a single group; when the sparsity is low, a large-scale subcarrier group is used to improve the overall processing efficiency. Thus, it balances detection accuracy and real-time performance in the complex and time-varying power grid BeiDou communication environment, exhibiting strong adaptability and low complexity. Furthermore, through the parameter-channel state mapping model, the CG-OAMP algorithm parameters can be adaptively adjusted in real time according to the channel estimation results and signal sparsity, ensuring that the CG-OAMP algorithm always operates in the optimal state. This avoids the insufficient robustness problem caused by traditional fixed parameters and also improves the signal detection accuracy.

[0022] In step S1, after receiving the hybrid power-line BeiDou signal, the OFDM-IM signal and pilot signal are separated through preprocessing. The OFDM-IM signal carries service data and subcarrier index information, while the pilot signal carries known reference symbols for channel estimation and synchronization. Specifically, the preprocessing involves first removing out-of-band interference using bandpass filtering, followed by FFT transformation and matched filtering to separate the OFDM-IM signal and pilot signal. Bandpass filtering, FFT transformation, and matched filtering are all existing technologies, and their specific principles will not be elaborated here.

[0023] In addition, for common interference signals such as narrowband interference and pulse interference in power BeiDou signals, after separating the OFDM-IM signal, wavelet multi-scale decomposition can be performed on the OFDM-IM signal. The useful signal and interference signal can be separated by independent component analysis algorithm, and the interference suppression filter can be used to suppress the interference signal in a targeted manner to reduce the impact of the interference signal on the subsequent signal detection process.

[0024] In addition, in step S2, the process of performing channel estimation based on the pilot signal to obtain the channel estimation result includes the following: First, the LMMSE algorithm is used to perform an initial estimation of channel state information based on the pilot signal. Then, the Kalman filter algorithm is used to perform iterative estimation with the initial estimation result as the initial state value, and output accurate channel state information.

[0025] Specifically, based on the pilot signal, the LMMSE algorithm is first used for initial CSI estimation to obtain initial parameters such as channel fading coefficient and phase offset. Then, the Kalman filter algorithm is used to iteratively estimate the channel state using the initial estimation results as initial state values. Within each OFDM symbol period, the channel state is recursively updated by combining the latest received signal with the current signal estimation results. Simultaneously, the channel fading variance is adaptively updated based on the estimation error covariance matrix, and the real-time signal-to-noise ratio is calculated using the signal power and noise power estimation results. These parameters are dynamically updated over time, thereby dynamically correcting the channel parameters and outputting accurate channel state information, providing accurate data support for subsequent signal detection. Specifically, the Kalman filter algorithm models the power grid BeiDou channel as a state system that changes slowly over time. Channel parameters (such as the fading coefficient and phase offset of each subcarrier) are used as system state variables. It is assumed that the channel state at adjacent times follows a linear evolution relationship, and process noise is superimposed to characterize the uncertainties caused by multipath variations, terminal movement, and environmental disturbances. At each moment, the Kalman filter algorithm includes two basic stages: (1) the prediction stage, which uses the CSI updated in the previous moment and the channel state transition model to predict the channel state at the current moment, and obtains the predicted CSI and its uncertainty. The prediction result reflects the possible change trend of the channel under the condition of no new observation; (2) the correction and update stage, which uses the received signal characteristics after interference consistency and pilot observation information as measurement inputs, compares them with the predicted CSI, and adaptively calculates the update weight according to the prediction uncertainty and the magnitude of observation noise to correct the channel state. When the observation reliability is high, the update result depends more on the real-time received signal, while when the interference or noise is strong, more of the prediction result is retained to ensure the stability of the estimation. Through the above-mentioned "prediction-correction" cyclic update mechanism, the Kalman filter algorithm can continuously suppress the influence of noise and residual interference in the time-varying channel environment, smoothly track the channel change trajectory, avoid the problem of rapid failure of traditional static channel estimation in dynamic scenarios, and finally output the CSI as high-precision, low-delay channel state information, providing reliable support for subsequent CG-OAMP signal detection and parameter adaptive adjustment. The channel estimation results specifically include channel fading variance, signal-to-noise ratio, complex channel gain (amplitude and phase) for each subcarrier, and noise statistics.

[0026] It is understood that this invention employs a two-stage signal estimation optimization. Based on the existing LMMSE initial channel estimation, a Kalman filter algorithm is introduced for channel tracking optimization, which can update the CSI in real time and improve the timeliness and accuracy of channel estimation.

[0027] In addition, in step S3, the cyclic prefix in the OFDM-IM signal is removed to obtain the power grid BeiDou signal data to be detected, and then the signal sparsity of the OFDM-IM signal is estimated. The process of estimating the signal sparsity of the OFDM-IM signal includes the following: The subcarrier activation state of the OFDM-IM signal is modeled as a Bernoulli-Gaussian model, and a sparsity parameter is used to represent the probability of subcarrier activation to achieve sparse prior modeling. Then, a posterior update is performed based on the amplitude, power spectral density, and noise variance of the current received signal. The posterior probability of subcarrier activation is calculated in each detection time slot. The posterior probabilities of all subcarriers are statistically averaged to obtain the sparsity estimate at the current time. When the sparsity change between adjacent time slots exceeds a preset threshold, a time-weighted mechanism is introduced for posterior update to suppress false judgments caused by noise and to quickly respond to sudden changes in the actual sparsity.

[0028] Traditional Bayesian sparsity estimation algorithms typically assume that signal sparsity is a fixed prior parameter and update it offline or slowly using maximum a posteriori (MAP) or expectation-maximization (EM) methods, making them unsuitable for dynamic scenarios. The improved Bayesian sparsity estimation algorithm of this invention achieves online sensing of signal sparsity by updating the activation probability of the received signal in real time. It no longer relies on a fixed sparsity prior but dynamically updates the sparsity with the received signal, transforming static estimation into real-time online estimation. Furthermore, considering the characteristics of sudden activation subcarriers in power grid BeiDou signals, a time-weighted mechanism is introduced. This not only suppresses false positives caused by noise but also rapidly responds to sudden changes in actual sparsity, improving sensitivity to sparsity abrupt changes. It can more accurately reflect the actual subcarrier activation ratio, avoiding performance degradation caused by sparsity mismatch, and improving the algorithm's robustness and response speed in scenarios with sudden signal changes. Moreover, the entire process is based solely on probability statistics and simple updates, without introducing high-dimensional matrix inversion, resulting in low computational complexity and suitability for real-time processing.

[0029] After signal sparsity estimation, subcarriers are adaptively and dynamically grouped according to the estimated signal sparsity to obtain a complex-valued OFDM-IM signal model. If the estimated signal sparsity is greater than a preset sparsity threshold, a small-scale subcarrier group is used to reduce the detection complexity of a single group. If the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used to improve overall processing efficiency. The complex-valued OFDM-IM signal model includes the complex-valued baseband signal corresponding to each subcarrier in the frequency domain, which can be uniformly represented as: y = Hx + n, where y represents the complex-valued baseband signal, x represents the complex-valued OFDM-IM transmitted symbol vector containing indexed modulation sparsity characteristics, H represents the frequency domain channel matrix (estimated in step S2), and n represents complex Gaussian noise.

[0030] It is understandable that, compared with the traditional fixed subcarrier grouping method, the present invention adaptively and dynamically groups the subcarriers according to the estimated signal sparsity, so as to balance detection accuracy and real-time performance in complex and time-varying power grid BeiDou communication environments, and has strong adaptability and robustness.

[0031] Optionally, in step S3, a quality assessment factor for each subcarrier is also calculated based on the channel estimation results, using the following formula: Then, the quality evaluation factor of each subcarrier group is obtained by averaging, and a higher priority is set for the subcarrier group with a higher quality evaluation factor. The subcarrier group with a higher quality evaluation factor is selected first to enter the subsequent iterative detection process, and its detection result is the main output of the current iteration stage. The other subcarrier groups with lower priority can be processed in subsequent iterations or when resources allow, thereby reducing the overall computational complexity and improving real-time performance while ensuring detection accuracy.

[0032] Furthermore, in step S4, the CG-OAMP (Conjugate Gradient-Orthogonal Approximate Message Passing) algorithm is a commonly used signal detection algorithm. It utilizes the OAMP algorithm to decompose the complex detection problem into a simple MMSE estimation problem, and optimizes the search direction and step size through the CG algorithm to avoid local optima. Through a residual update mechanism, it gradually approximates the stable solution to the signal estimation. For example, the CG-OAMP algorithm first performs linear optimization through a linear estimation (LE) block, which can be expressed as... , express t The inverse of the weighted covariance matrix at time step, express t Signal estimate at time -1 express t Linear estimate of the signal at time t. Represents the channel matrix, y Represents the actual signal value. The linear step factor is represented by the step factor; then, the noise variance estimate is dynamically corrected by the variance estimation unit, which can be expressed as follows: , Indicates the total number of subcarriers. express t The noise variance estimate at time t. This represents the nonlinear estimation step factor. express t The noise variance estimate at time -1; then, the estimate is corrected by a nonlinear estimation (NLE) block, and can be expressed as: , express t The nonlinear estimate of the signal at time t. , , and For NLE block parameters, when At that time, the nonlinear estimation block degenerates into an MMSE estimator; then, the CG algorithm is introduced to replace the traditional LMMSE operation, by optimizing the search direction. and search step size , , representing the residual vector The coefficients for the CG iteration are used to accelerate iterative convergence. The core expression is: .

[0033] In this invention, the channel estimation result obtained in step S2 and the signal sparsity obtained in step S3 are input into a pre-trained parameter-channel state mapping model. The parameter-channel state mapping model characterizes the mapping relationship between channel state information, signal sparsity, and CG-OAMP algorithm parameters, and can be expressed as: The model aims to minimize the bit error rate and is pre-trained using a combination of offline training and online fine-tuning. This represents the initial step factor vector of the CG-OAMP algorithm, including the linearly estimated step factor. and nonlinear estimation of step factor , This represents the adaptive parameter vector of the nonlinear estimation block of the CG-OAMP algorithm, which includes multiple parameters. , , and , Indicates the signal-to-noise ratio. Indicates the channel fading variance. This indicates the sparsity of the signal.

[0034] It is understood that this invention incorporates the step factor and trainable parameters of the NLE block in the CG-OAMP algorithm into the deep learning optimization framework, and constructs a parameter-channel state mapping model. This model can adaptively adjust the parameters of the CG-OAMP algorithm in real time based on the channel estimation results and signal sparsity, so that the CG-OAMP algorithm always works in the optimal state. This avoids the problem of insufficient robustness caused by traditional fixed parameters and also improves the signal detection accuracy.

[0035] In addition, in step S5, the complex-valued OFDM-IM signal model is first converted into a real-valued model to simplify the subsequent calculation process. The specific conversion formula is as follows: , Indicates a complex-valued signal. Indicates extraction of the real part. This indicates the extraction of the imaginary part. Then, the CG-OAMP algorithm is used to perform hierarchical iterative detection on the real-valued signal based on the optimized parameters obtained from the mapping in step S4, to obtain the reconstructed power grid BeiDou signal.

[0036] Optionally, after separating the OFDM-IM signal, wavelet multi-scale decomposition is performed to extract the high-frequency components of the signal. A traditional wavelet modulus maxima algorithm is then used to detect the presence of abrupt changes in signal intensity. Specifically, the timing and intensity of these abrupt changes can be detected. If an abrupt change is present, the CG-OAMP algorithm's iteration mode is set to accelerated iteration mode to increase the iteration update frequency and achieve rapid capture and recovery of the abrupt change. If no abrupt change is present, the CG-OAMP algorithm's iteration mode is set to normal iteration mode. The iteration step size in normal iteration mode is larger than that in accelerated iteration mode, typically 60%-80% of that in accelerated iteration mode. This allows for adjustment of the iteration step size based on the abrupt change signal state, accelerating the stable convergence of the CG-OAMP algorithm under the given estimation criteria.

[0037] It is understood that this invention adopts a conventional iteration step size for hierarchical iteration when there is no sudden change signal to ensure numerical stability, while shortening the iteration step size when there is a sudden change signal to suppress residual oscillation caused by the sudden change, so that the algorithm can enter the effective descent range more quickly and achieve rapid capture and recovery of sudden change signals.

[0038] Optionally, if no abrupt change signal exists, iteration stops when the iteration error is less than the fast convergence threshold during hierarchical iterative detection. If abrupt change signal exists, iteration stops only when the iteration error is less than the precise optimization threshold, where the precise optimization threshold is less than the fast convergence threshold. Specifically, during hierarchical iterative detection using the CG-OAMP algorithm, the iteration error is continuously calculated. When there is no mutation signal, if Then stop iterating. This represents the fast convergence threshold, which can be set to 10. -4 When a mutation signal exists, if Alternatively, iteration can stop only after the preset maximum number of iterations (e.g., 20). This indicates a threshold for precise optimization, which can be set to 10. -5 This ensures a balance between real-time performance and accuracy.

[0039] It is understood that this invention abandons the traditional single iteration number or error threshold iteration stopping criterion, and instead adopts a layered iteration stopping strategy. When the iteration error rapidly decreases to a preset fast convergence threshold and there is no sudden change signal, it enters the fast convergence layer and uses a relaxed criterion to initially stop the iteration to ensure real-time performance. If there is a sudden change signal or drastic channel change, it automatically enters the precise optimization layer, extends the iteration number and refines the parameter adjustment to ensure detection accuracy. This realizes perceptual iterative control of sudden change signals and channel changes, and effectively improves the real-time performance and detection accuracy of the algorithm without significantly increasing the computational complexity.

[0040] In addition, the following content is included before using the CG-OAMP algorithm to perform hierarchical iterative detection of real-valued signals based on optimized parameters: The real-valued signal is input into the pre-trained CNN-LSTM model to make a preliminary estimate of the power grid Beidou signal, and the preliminary estimated signal value is used as the initial value for the iteration of the CG-OAMP algorithm.

[0041] It is understandable that before iterating the CG-OAMP algorithm, a fusion model of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) is introduced to perform preliminary estimation of the power grid BeiDou signal based on real-valued signals. CNN is used to extract local features of the signal (such as subcarrier activation mode features, interference signal features, etc.), while LSTM is used to capture the temporal correlation of the signal (such as the correlation features of sudden signal changes, etc.). The initial value output by this fusion model replaces the traditional zero initialization of the CG-OAMP algorithm, which greatly reduces the number of iterations required for convergence and improves the detection speed.

[0042] Optionally, the following may also be included in the hierarchical iterative detection process: Calculate the confidence level of the signal estimate. If the confidence level is greater than or equal to the preset confidence level threshold, the minimum distance decision rule is adopted. If the confidence level is less than the preset confidence level threshold, the maximum a posteriori probability decision rule is adopted.

[0043] Specifically, the signal estimate is calculated based on the following formula. Confidence level: , Indicates the confidence level. Indicates the initial noise variance. express t The noise variance estimate at time step [time]. During the iterative detection process of the CG-OAMP algorithm, the uncertainty in the signal estimate mainly stems from noise interference, while the initial noise variance [is crucial]. This invention describes the noise intensity level before sufficient iteration. By comparing the normalized variance of the current signal estimation result with the initial noise variance, it can measure the reliability of the current estimation result relative to the noise level. Essentially, it characterizes the proportion of the "effective signal component" in the current signal estimation result relative to the noise uncertainty. The higher the proportion, the more reliable the estimation result and the higher the confidence level. Moreover, this formula only utilizes existing noise variance and estimation statistics, without introducing additional complex calculations, resulting in low computational complexity. It can dynamically reflect the iterative detection quality under low complexity conditions, providing a valid basis for subsequent adaptive decision-making, thereby improving detection robustness and overall system performance. When the confidence level... If the signal estimate is greater than or equal to a preset confidence threshold (e.g., 0.8), the minimum distance decision rule is applied to adjust the signal estimate. Mapped to the nearest modulation symbol, if confidence level If the confidence level is less than the preset confidence threshold, the maximum a posteriori probability decision rule is adopted, and the probability distribution of the modulation constellation diagram is combined to achieve accurate mapping.

[0044] It is understandable that existing CG-OAMP algorithms typically employ either the minimum distance decision rule or the maximum a posteriori probability decision rule, failing to adaptively balance detection accuracy and computational complexity, resulting in poor robustness. This invention, however, designs an adaptive decision mechanism based on signal confidence. It dynamically selects the decision rule according to the variance of the signal estimation during the iteration process. When the confidence is high, the computationally inefficient minimum distance decision rule is used; when the confidence is low, the more accurate maximum a posteriori probability decision rule is used. This effectively balances detection accuracy and computational complexity. In low signal-to-noise ratio or abrupt signal scenarios, it effectively avoids erroneous hard decisions based on low-reliability estimation results, reducing the bit error rate and further improving the algorithm's robustness.

[0045] Furthermore, this invention comprehensively evaluates algorithm performance from three dimensions: bit error rate (BER), signal reconstruction error, and iterative convergence time. BER measures the communication reliability of the final decision result and is a core indicator at the service layer. Signal reconstruction error measures the reconstruction accuracy of continuous signal estimation and the algorithm's convergence quality during iteration, distinguishing the causes of decision errors from estimation distortions. Iterative convergence time measures the algorithm's real-time performance and computational efficiency, directly affecting the online application capability of the power grid BeiDou terminal. The core purpose of setting these three indicators is to achieve joint evaluation and balanced control of accuracy, stability, and real-time performance. For example, if the BER exceeds a preset threshold (e.g., 10),... -3If the issue is caused by inaccurate channel estimation, the noise variance of the Kalman filter process is adjusted accordingly. If it's due to insufficient iteration, the upper limit of the iteration count is increased. If it's due to poor parameter adaptability, the parameter-channel state mapping model is updated, and the optimized parameters are stored in the parameter library for subsequent signal detection in similar channel scenarios. Furthermore, if a sudden change in signal strength exceeds a safety threshold or the bit error rate remains persistently high during detection, an automatic anomaly alarm mechanism is triggered, prompting maintenance personnel to investigate communication link or terminal equipment faults.

[0046] It is understandable that this invention, by adding a CNN-LSTM pre-estimation module and a two-stage channel estimation mechanism, can reduce the initial estimation error by 30%-40%. Through adaptive sparsity sensing and dynamic subcarrier grouping strategies, it reduces the detection bit error rate by more than 50% for highly sparse and abrupt signal scenarios. Through CG-OAMP algorithm optimization, the signal reconstruction error can be controlled within 5%, meeting the high-precision communication requirements of power grid BeiDou. By replacing zero initialization with deep learning pre-estimation, the number of iterations for convergence is reduced by 40%-60%, and in conventional scenarios, the number of iterations can be reduced from 1. The detection latency is reduced from 5-20 times to 6-8 times. Through a hierarchical iterative stopping criterion and a sudden signal acceleration mechanism, the signal detection latency is reduced by 30%-50%, fully meeting the requirements of real-time power system BeiDou communication. An adaptive parameter optimization network based on the parameter-channel state mapping model ensures that the bit error rate fluctuation does not exceed 10% in a wide range of scenarios with a signal-to-noise ratio of 5dB-20dB and a sparsity of 0.3-0.8. Through interference suppression and sudden signal detection branches, the suppression capability for narrowband interference and pulse interference is improved by 40%, and the response latency to sudden signals is shortened to less than 1ms. Therefore, the OFDM-IM power system BeiDou signal detection method of this invention overcomes the bottlenecks of existing OFDM-IM power system BeiDou signal detection methods in terms of complexity, robustness, and adaptability. It achieves high-precision, low-latency, and high-reliability detection of power system BeiDou signals in complex air-space-ground channel environments, providing core technical support for the large-scale application of BeiDou communication in power systems.

[0047] In addition, such as Figure 2 As shown, another embodiment of the present invention also provides a detection system for OFDM-IM power line BeiDou signals, preferably employing the OFDM-IM power line BeiDou signal detection method described above, comprising: The signal preprocessing module is used to receive mixed electric power BeiDou signals and separate OFDM-IM signals and pilot signals through preprocessing; The channel estimation module is used to perform channel estimation based on pilot signals and obtain the channel estimation results. The subcarrier dynamic grouping module is used to estimate the signal sparsity of the OFDM-IM signal and adaptively and dynamically group the subcarriers according to the estimated signal sparsity to obtain the complex-valued OFDM-IM signal model. If the estimated signal sparsity is greater than the preset sparsity threshold, a small-scale subcarrier group is used; if the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used. The parameter optimization module is used to input the channel estimation results and signal sparsity into a pre-trained parameter-channel state mapping model to obtain the optimized parameters of the CG-OAMP algorithm; wherein, the parameter-channel state mapping model represents the mapping relationship between channel state information and signal sparsity and CG-OAMP algorithm parameters; The signal reconstruction module is used to convert the complex-valued OFDM-IM signal model into a real-valued signal. The CG-OAMP algorithm is used to perform hierarchical iterative detection on the real-valued signal based on optimized parameters to obtain the reconstructed power BeiDou signal.

[0048] It is understood that the OFDM-IM power grid BeiDou signal detection system in this embodiment estimates the signal sparsity of the OFDM-IM signal, senses the sparsity changes of the power grid BeiDou signal in real time, and adaptively and dynamically groups the subcarriers according to the estimated signal sparsity. When the sparsity is high, a small-scale subcarrier group is used to reduce the detection complexity of a single group; when the sparsity is low, a large-scale subcarrier group is used to improve the overall processing efficiency. Thus, it balances detection accuracy and real-time performance in the complex and time-varying power grid BeiDou communication environment, exhibiting strong adaptability and low complexity. Furthermore, through the parameter-channel state mapping model, the CG-OAMP algorithm parameters can be adaptively adjusted in real time according to the channel estimation results and signal sparsity, ensuring that the CG-OAMP algorithm always operates in the optimal state. This avoids the insufficient robustness problem caused by traditional fixed parameters and also improves the signal detection accuracy.

[0049] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0050] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for detecting OFDM-IM power BeiDou signals, wherein the computer program executes the steps of the method described above when running on a computer.

[0051] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for execution by a machine, and includes digital or analog carrier communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented 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. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] 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.

[0056] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting OFDM-IM power line BeiDou signals, characterized in that, Includes the following: It receives hybrid electric BeiDou signals and separates OFDM-IM signals and pilot signals through preprocessing. Channel estimation is performed based on pilot signals to obtain channel estimation results; The signal sparsity of the OFDM-IM signal is estimated, and the subcarriers are adaptively and dynamically grouped according to the estimated signal sparsity to obtain the complex-valued OFDM-IM signal model. If the estimated signal sparsity is greater than the preset sparsity threshold, a small-scale subcarrier group is used; if the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used. The channel estimation results and signal sparsity are input into a pre-trained parameter-channel state mapping model to obtain the optimized parameters of the CG-OAMP algorithm; wherein, the parameter-channel state mapping model characterizes the mapping relationship between channel state information and signal sparsity and CG-OAMP algorithm parameters; The complex-valued OFDM-IM signal model is converted into a real-valued signal, and the CG-OAMP algorithm is used to perform hierarchical iterative detection on the real-valued signal based on optimized parameters to obtain the reconstructed power BeiDou signal.

2. The method for detecting OFDM-IM power grid BeiDou signals as described in claim 1, characterized in that, The process of performing channel estimation based on pilot signals to obtain channel estimation results includes the following: First, the LMMSE algorithm is used to perform an initial estimation of channel state information based on the pilot signal. Then, the Kalman filter algorithm is used to perform iterative estimation with the initial estimation result as the initial state value, and output accurate channel state information.

3. The method for detecting OFDM-IM power grid BeiDou signals as described in claim 1, characterized in that, The process of estimating the signal sparsity of the OFDM-IM signal includes the following: The subcarrier activation state of the OFDM-IM signal is modeled as a Bernoulli-Gaussian model. A sparsity parameter is used to represent the probability of subcarrier activation to achieve sparse prior modeling. Then, a posterior update is performed based on the current received signal to obtain the posterior probability of subcarrier activation. The posterior probabilities of all subcarriers are statistically averaged to obtain the sparsity estimate at the current time. Furthermore, when the sparsity change between adjacent time steps exceeds a preset threshold, a time-weighted mechanism is introduced for posterior update.

4. The method for detecting OFDM-IM power grid BeiDou signals as described in claim 1, characterized in that, After separating the OFDM-IM signal, wavelet multi-scale decomposition is performed on the OFDM-IM signal to extract the high-frequency components of the signal. The wavelet modulus maxima algorithm is used to detect whether there is abrupt change signal. If there is abrupt change signal, the iteration mode of the CG-OAMP algorithm is set to accelerated iteration mode. If there is no abrupt change signal, the iteration mode of the CG-OAMP algorithm is set to normal iteration mode.

5. The method for detecting OFDM-IM power line BeiDou signals as described in claim 4, characterized in that, If there is no mutation signal, the iteration stops when the iteration error is less than the fast convergence threshold during the hierarchical iterative detection process. If there is a mutation signal, the iteration stops only when the iteration error is less than the precise optimization threshold, where the precise optimization threshold is less than the fast convergence threshold.

6. The method for detecting OFDM-IM power line BeiDou signals as described in claim 1, characterized in that, Before using the CG-OAMP algorithm to perform hierarchical iterative detection of real-valued signals based on optimized parameters, the following content is also included: The real-valued signal is input into the pre-trained CNN-LSTM model to make a preliminary estimate of the power grid Beidou signal, and the preliminary estimated signal value is used as the initial value for the iteration of the CG-OAMP algorithm.

7. The method for detecting OFDM-IM power grid BeiDou signals as described in claim 1, characterized in that, The hierarchical iterative detection process also includes the following: Calculate the confidence level of the signal estimate. If the confidence level is greater than or equal to the preset confidence level threshold, the minimum distance decision rule is adopted. If the confidence level is less than the preset confidence level threshold, the maximum a posteriori probability decision rule is adopted.

8. A detection system for OFDM-IM power line BeiDou signals, characterized in that, include: The signal preprocessing module is used to receive mixed electric power BeiDou signals and separate OFDM-IM signals and pilot signals through preprocessing; The channel estimation module is used to perform channel estimation based on pilot signals and obtain the channel estimation results. The subcarrier dynamic grouping module is used to estimate the signal sparsity of the OFDM-IM signal and adaptively and dynamically group the subcarriers according to the estimated signal sparsity to obtain the complex-valued OFDM-IM signal model. If the estimated signal sparsity is greater than the preset sparsity threshold, a small-scale subcarrier group is used; if the estimated signal sparsity is not greater than the preset sparsity threshold, a large-scale subcarrier group is used. The parameter optimization module is used to input the channel estimation results and signal sparsity into a pre-trained parameter-channel state mapping model to obtain the optimized parameters of the CG-OAMP algorithm; wherein, the parameter-channel state mapping model represents the mapping relationship between channel state information and signal sparsity and CG-OAMP algorithm parameters; The signal reconstruction module is used to convert the complex-valued OFDM-IM signal model into a real-valued signal. The CG-OAMP algorithm is used to perform hierarchical iterative detection on the real-valued signal based on optimized parameters to obtain the reconstructed power BeiDou signal.

9. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for detecting OFDM-IM power line BeiDou signals, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 7.

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