Signal emission bandwidth estimation method based on adaptive filtering algorithm and related equipment

Abstract

The invention belongs to the field of wireless communication systems, and discloses a signal emission bandwidth estimation method based on an adaptive filtering algorithm and related equipment, and the method comprises the steps: generating an ideal simulation signal corresponding to an actual measurement signal, and obtaining a reference signal through adaptive filtering processing; and calculating the residual error of the actually measured signal and the reference signal, and continuously optimizing the response function of the adaptive filtering algorithm by adopting a stochastic gradient descent method so as to minimize the residual error mean square error. According to the method, the filtering parameters are continuously adjusted, so that the reference signal is closer to the bandwidth characteristic of the actually measured signal. And finally, calculating a signal emission bandwidth based on the optimized reference signal. By adopting the method, the problem that a traditional method is difficult to adapt to a complex and changeable communication environment is effectively solved, the accuracy and reliability of bandwidth estimation are improved, and an accurate emission bandwidth reference is provided for synchronous demodulation of a ground receiving end and adjacent channel interference evaluation of a frequency spectrum monitoring system.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication systems, and particularly relates to a signal transmission bandwidth estimation method based on an adaptive filtering algorithm and related devices. Background Art

[0002] In a wireless communication system, the transmission bandwidth, as a key parameter characterizing the signal spectrum occupancy range, directly affects the signal transmission quality, the system anti-interference ability, and the spectrum resource utilization efficiency. Traditional transmission bandwidth measurement techniques mainly rely on an offline test system composed of a swept-frequency signal source and a self-transmitting and self-receiving device, and estimate the bandwidth boundary by scanning the frequency band point by point and detecting the signal amplitude attenuation characteristics. However, this method has significant limitations: on the one hand, the sweeping process takes a long time and it is difficult to meet the online measurement requirements in scenarios of dynamic spectrum allocation or real-time link optimization; on the other hand, the offline test mode cannot adapt to the operating state of the communication system, resulting in difficulty in accurately evaluating the actual transmission bandwidth when it is affected by modulation methods, power amplifier non-linear distortion, and channel time-varying characteristics.

[0003] With the evolution of communication and navigation technologies, the system performance optimization in a complex electromagnetic environment poses higher requirements for real-time perception of the transmission bandwidth. Taking the navigation satellite system as an example, in order to suppress out-of-band interference and spurious radiation, the signal generation end needs to use a high-precision narrowband filter to perform frequency-domain shaping on the transmitted signal. However, due to the inconsistency of the transmission source channel model parameters, there are topological structure differences, non-standard passband ripple characteristics, and stopband attenuation indexes in the filter designs of different batches of satellite payloads, resulting in the dynamic change characteristics of the transmission bandwidth in the inter-satellite link and the satellite-ground link. This uncertainty not only increases the synchronization and demodulation difficulty at the ground receiving end, but also causes the spectrum monitoring system to lack an accurate transmission bandwidth benchmark when evaluating adjacent channel interference, becoming a key technical bottleneck restricting the performance improvement of the satellite communication system.

[0004] It can be seen that the traditional bandwidth estimation method is difficult to adapt to a complex and changeable communication environment, resulting in the inability to accurately estimate the signal transmission bandwidth. Summary of the Invention

[0005] The present invention provides a signal transmission bandwidth estimation method based on an adaptive filtering algorithm and related devices to solve the technical problem that the traditional bandwidth estimation method is difficult to adapt to a complex and changeable communication environment, resulting in the inability to accurately estimate the signal transmission bandwidth.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A signal transmission bandwidth estimation method based on an adaptive filtering algorithm, comprising: Based on the measured signal of the satellite, an ideal simulation signal corresponding to the measured signal information is obtained; The ideal simulation signal is adaptively filtered using an adaptive filtering algorithm to output a reference signal; Based on the residual obtained from the measured signal and the reference signal, the response function of the adaptive filtering algorithm is iteratively updated using the stochastic gradient descent method until the minimum residual mean square error is obtained; Based on the reference signal output from the last iterative update, the signal transmission bandwidth is calculated.

[0007] Further, the generation of the ideal simulation signal corresponding to the information of the measured signal based on the collected measured signal includes: According to the Doppler information of the collected measured signal, an ideal distortionless signal is simulated; the Doppler information includes modulation mode, modulation parameters, multiplexing mode, code rate, pseudo-code period, and carrier frequency; The ideal distortionless signal is resampled according to the measured satellite A / D sampling frequency to obtain an ideal simulation signal.

[0008] Further, before the ideal simulation signal is adaptively filtered using the adaptive filtering algorithm to output a reference signal, it further includes: Initializing the parameter category, parameter range, and sliding step of the adaptive filtering algorithm; wherein, the parameter category includes passband cut-off frequency, stopband start frequency, minimum stopband attenuation, and maximum stopband attenuation; the parameter range is determined by the residual between the measured signal and the reference signal; the sliding step is determined by the pre-estimated transmission bandwidth.

[0009] Further, the ideal simulation signal is adaptively filtered using the adaptive filtering algorithm to output a reference signal, including: The ideal simulation signal is decomposed into upper and lower sidebands to obtain the upper sideband and lower sideband of the ideal simulation signal; The upper and lower sidebands of the ideal simulation signal are respectively adaptively filtered using the adaptive filtering algorithm to output an upper sideband reference signal and a lower sideband reference signal.

[0010] Further, before the response function of the adaptive filtering algorithm is iteratively updated using the stochastic gradient descent method based on the residual obtained from the measured signal and the reference signal until the minimum residual mean square error is obtained, it includes: Performing domain spectrum analysis on the measured signal and the reference signal respectively according to the same spectrum estimation method, spectrum analysis parameters, and smoothing times to obtain the actual power spectrum and the ideal power spectrum; wherein, the spectrum estimation method uses Doppler estimation to compensate for the theoretical center frequency information; Interpolating and translating the reference signal according to the actual power spectrum and the ideal power spectrum; Calculating the residual between the measured signal and the reference signal, and the specific formula is as follows:

[0011] Wherein, represents the residual between the measured signal and the reference signal; represents the measured signal; represents the ideal simulation signal; represents the response function of the adaptive filtering algorithm.

[0012] Furthermore, for the residual calculated based on the measured signal and the reference signal, the response function of the adaptive filtering algorithm is iteratively updated using the stochastic gradient descent method until the minimum residual mean square error is obtained, including: For the residual calculated based on the measured signal and the reference signal, randomly select any data point to calculate the gradient, and then realize the iterative update of the response function of the adaptive filtering algorithm; wherein, the parameter update for each iterative calculation is in the direction of gradient descent; The specific iterative formula is as follows:

[0013] Wherein, represents the response function of the adaptive filtering algorithm; T represents the current moment, and T + 1 represents the next moment; represents the response function of the adaptive filtering algorithm at the current moment; represents the response function of the adaptive filtering algorithm at the next moment; μ represents the step size factor; represents the gradient vector of the residual mean square error between the measured signal and the reference signal; T represents the residual between the measured signal and the reference signal at the current moment; represents the ideal simulation signal.

[0014] Furthermore, in the calculation of the signal transmission bandwidth based on the reference signal output by the last iterative update, the specific formula for the signal transmission bandwidth is as follows:

[0015]

[0016] Wherein, is the minimum order of the filter, represents the passband edge frequency, represents the passband edge frequency, represents the maximum passband attenuation, represents the passband edge frequency, represents the cut-off frequency.

[0017] A signal transmission bandwidth estimation system based on an adaptive filtering algorithm, comprising: A simulation module, configured to obtain an ideal simulation signal corresponding to the measured signal information based on the measured signal of the satellite; An adaptive filtering module, configured to perform adaptive filtering processing on the ideal simulation signal by using an adaptive filtering algorithm and output a reference signal; An iterative update module, configured to perform iterative update on the response function of the adaptive filtering algorithm by using the stochastic gradient descent method based on the residual obtained by calculating the measured signal and the reference signal until the minimum residual mean square error is obtained; A calculation module, configured to calculate the signal transmission bandwidth based on the reference signal output by the last iterative update.

[0018] An electronic device, comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the above-mentioned signal transmission bandwidth estimation method based on the adaptive filtering algorithm when executing the computer program.

[0019] A computer-readable storage medium, storing a computer program, where the computer program is used to implement the steps of the above-mentioned signal transmission bandwidth estimation method based on the adaptive filtering algorithm when executed by a processor.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a signal transmission bandwidth estimation method based on an adaptive filtering algorithm. By generating an ideal simulation signal corresponding to the measured signal and using adaptive filtering processing to obtain a reference signal; then by calculating the residual between the measured signal and the reference signal, the response function of the adaptive filtering algorithm is continuously optimized by using the stochastic gradient descent method to minimize the residual mean square error. This method continuously adjusts the filtering parameters to make the reference signal closer to the bandwidth characteristics of the measured signal. Finally, the signal transmission bandwidth is calculated based on the optimized reference signal. Using this method can adapt to the differences and dynamic change characteristics of satellite payload filters in different batches, and accurately estimate the transmission bandwidth through adaptive learning and optimization, effectively solving the problem that traditional methods are difficult to adapt to complex and changeable communication environments, improving the accuracy and reliability of bandwidth estimation, and providing an accurate transmission bandwidth benchmark for synchronous demodulation at the ground receiving end and adjacent channel interference evaluation of the spectrum monitoring system.

[0021] In the present invention, preferably, an ideal distortionless signal is simulated and generated according to the Doppler information of the measured signal, and the ideal simulation signal is resampled to ensure the close correspondence between the simulation signal and the measured signal. This helps to improve the accuracy and effectiveness of subsequent adaptive filtering processing, and further improves the accuracy of bandwidth estimation.

[0022] In the present invention, preferably, before the adaptive filtering process, the parameter categories, ranges, and sliding steps of the filtering algorithm are initialized, providing a clear search direction and range for the algorithm. This helps to accelerate the iterative convergence process, improve the algorithm efficiency, and ensure the stability and accuracy of the bandwidth estimation result.

[0023] In the present invention, preferably, by decomposing the ideal simulation signal into upper and lower sidebands and performing adaptive filtering processing on the upper and lower sidebands respectively, the bandwidth characteristics of the signal can be captured more precisely. This processing method helps to improve the fineness and accuracy of bandwidth estimation, especially when the signal bandwidth is wide or there is a complex spectral structure.

[0024] In the present invention, preferably, the measured signal and the reference signal are subjected to spectral analysis using the same spectral estimation method and parameters, and the reference signal is interpolated and translated to ensure the consistency and accuracy of spectral comparison. This helps to calculate the residual more accurately, thereby improving the effect of iterative update of the response function and the accuracy of bandwidth estimation.

[0025] In the present invention, preferably, the random gradient descent method is used to iteratively update the response function of the adaptive filtering algorithm, and the gradient is obtained by randomly selecting data points to accelerate the convergence process. This iterative method has the characteristics of high computational efficiency and fast convergence speed, which helps to improve the real-time performance and accuracy of bandwidth estimation. Description of the Drawings

[0026] Figure 1 It is the system model framework of the transmission bandwidth estimation provided by the embodiment of the present invention; Figure 2 It is the schematic diagram of the adaptive filter provided by the embodiment of the present invention; Figure 3 It is the power spectrum comparison diagram of a certain low-earth orbit satellite provided by the embodiment of the present invention; among them, (a) is the B06 model; (b) is the A01 model; (c) is the A04 model; (d) is the B01 model; (e) is the B03 model; (f) is the B05 model; Figure 4 It is the comparison diagram of the filtered spectrum matching situation of a certain low-earth orbit satellite provided by the present invention; among them, (a) is the B06 model; (b) is the A01 model; (c) is the A04 model; (d) is the B01 model; (e) is the B03 model; (f) is the B05 model; Figure 5 It is the flow chart of a signal transmission bandwidth estimation method based on an adaptive filtering algorithm provided by the embodiment of the present invention; Figure 6 It is the structural schematic diagram of a signal transmission bandwidth estimation system based on an adaptive filtering algorithm provided by the embodiment of the present invention. Detailed Embodiments

[0027] To further understand the content of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0028] The following explains the technical terms related to the present invention: A / D sampling: The full name of A / D sampling is Analog-to-Digital Conversion.

[0029] The meaning of QPSK is Quadrature Phase Shift Keying. It is a digital modulation method used to convert digital signals into analog signals for transmission.

[0030] As described in the background art, due to the complexity of satellite payloads, there are differences in the types and parameter designs of satellite filters in different batches. For example, for a certain low-earth orbit satellite, its payload generation is complex, the single-side width of the filter is less than 20 MHz, and the inconsistency of satellite filters in each batch brings great challenges to the estimation of the emission bandwidth. Traditional methods are difficult to adapt to this complex and changeable situation and cannot estimate the signal emission bandwidth intelligently and accurately. There is an urgent need for a new and effective method. Thus, this embodiment can solve the following technical problems of the prior art: First, aiming at the requirement for evaluating the signal emission bandwidth index in the on-line working state of the device, it solves the technical problem of measuring the signal emission bandwidth in the non-sweeping mode.

[0031] Second, since a narrowband filter is used in the system to suppress out-of-band interference, out-of-band spurs and other problems, it solves the uncertainty problem brought by factors such as the types and parameter designs of satellite filters in each batch to the emission bandwidth estimation.

[0032] To solve the above problems, this embodiment provides a method for estimating the signal emission bandwidth based on an adaptive filtering algorithm, which can dynamically adjust the parameters of the adaptive filter according to the spectral characteristics of the measured navigation signal, and calculate the emission bandwidth under the filter response function with the highest matching degree, solving the problems of inaccurate and difficult estimation of the emission bandwidth generated by complex payloads. This method first simulates an ideal distortion-free signal according to the modulation mode of the measured signal, filters it through the LMS adaptive filtering algorithm, and adjusts the parameters of the adaptive filter according to the spectral characteristics of the measured signal. Perform digital domain spectral analysis on the reference signals obtained after filtering the measured signal and the ideal simulation signal, and for the measured signal and the reference signal of the satellite, calculate the spectral residual to obtain the emission bandwidth under the condition of the filter with the best matching degree.

[0033] Exemplarily, the ideal simulation signal is a radio frequency carrier modulation signal obtained according to software simulation under ideal distortion - free conditions. The generation principle and modulation parameters of the ideal simulation signal are consistent with those of the measured signal of the satellite being collected.

[0034] The least - mean - square (LMS) adaptive algorithm is a method for changing the parameters and structure of a filter. The coefficients of the adaptive filter are time - varying coefficients updated by the adaptive algorithm. That is, its coefficients automatically and continuously adapt to a given signal to obtain the desired response. The input of the LMS adaptive algorithm is the ideal simulation signal, and the output is the reference signal after adaptive filtering. For each sample value of the input ideal simulation signal sequence, according to a specific algorithm, the filter weighting coefficients are updated and adjusted. The reference signal after adaptive filtering and the measured signal should satisfy the criterion of minimum mean - square error, that is, the output signal sequence approximates the measured signal sequence.

[0035] Make a decision on the output sequence. If the obtained result does not satisfy the criterion of minimum error, at this time, an adaptive optimization algorithm is used to adjust the filter coefficients. After continuous iterative calculations, find such a system that minimizes the error between the estimated sequence and the expected measured sequence.

[0036] The optimization algorithm adopted is stochastic gradient descent, that is, the gradient of the mean - square error is used to implement the estimation of the coefficient vector at the next moment from the coefficient vector of the filter at the current moment in an iterative manner. The coefficient update for each iterative calculation is in the direction of the gradient descent. In order to find the direction of the maximum descent, a data point can be randomly selected to calculate the gradient. For spectral analysis, pwelch divides a data sequence of length N into L segments, each segment has a length of M. The power spectrum of each segment is calculated separately, and the average of the power spectra of all segments is taken. Allowing partial overlap of each segment of data reduces the variance of the spectral estimate through averaging. By choosing an appropriate window function, the leakage of the spectrum can be reduced and the frequency - estimation resolution can be improved. Among them, pwelch is a commonly used power - spectral - density (PSD) estimation method in the field of signal processing.

[0037] Exemplarily, this embodiment provides a method for estimating the signal transmission bandwidth based on an adaptive filtering algorithm, including: Based on the measured signal of the satellite, obtain an ideal simulation signal corresponding to the information of the measured signal; Use the adaptive filtering algorithm to perform adaptive filtering on the ideal simulation signal and output a reference signal; Based on the residual obtained from the measured signal and the reference signal, use the stochastic gradient descent method to iteratively update the response function of the adaptive filtering algorithm until the minimum residual mean - square error is obtained; Based on the reference signal output by the last iterative update, calculate the signal transmission bandwidth.

[0038] The signal transmission bandwidth estimation method provided in this embodiment will be further explained below with reference to the accompanying drawings: As Figure 1 shown, a system model framework for estimating the transmission bandwidth is constructed, that is, a system model for estimating the transmission bandwidth of QPSK modulated satellite signals based on an adaptive filtering algorithm is constructed. First, an ideal distortion-free signal is simulated according to the modulation mode of the measured satellite QPSK signal, covering parameters such as the multiplexing mode, code rate, pseudo-code period, and carrier frequency. Then, the simulated signal is resampled according to the measured satellite A / D sampling frequency to ensure that the generation principle and modulation parameters of the ideal signal and the actually collected signal are the same. The ideal simulated signal is sent to the LMS adaptive filtering algorithm for filtering, and at the same time, the parameters of the adaptive filter are dynamically adjusted according to the spectral characteristics of the measured signal. Digital domain spectral analysis is performed on the measured signal and the filtered reference signal, and the transmission bandwidth is obtained under the condition of the filter with the best matching degree by calculating the spectral residual between the measured satellite signal and the reference signal.

[0039] As Figure 2 shown, is the measured signal of the satellite; as the desired signal of the adaptive filter, is the ideal simulated signal, is the reference signal that can be used for estimating the transmission bandwidth after the ideal simulated signal passes through the adaptive filter. Among them, the least mean square (LMS) adaptive algorithm is a method for changing the parameters and structure of the filter. The coefficients of the adaptive filter are time-varying coefficients updated by the adaptive algorithm. That is, its coefficients automatically and continuously adapt to the given signal to obtain the desired response.

[0040] Exemplarily, the working principle of the specific LMS adaptive filtering algorithm is as follows: The input of the LMS adaptive algorithm is the ideal simulated signal , and the output is the reference signal after adaptive filtering. For each sample value of the ideal simulated signal sequence , the filter weighting coefficients are updated and adjusted according to a specific algorithm. The reference signal and the measured signal should satisfy the criterion of the minimum mean square error, that is, the reference signal sequence of the satellite approximates the measured signal sequence .

[0041] For the output reference signal sequence , if the obtained result does not satisfy the criterion of the minimum residual mean square error, at this time, the adaptive optimization algorithm is used to adjust the filter coefficients. After continuous iterative calculations, an updated system is obtained, so that the estimated and the desired have the minimum error. The specific formula for residual calculation is as follows:

[0042] In the formula, represents the residual between the measured signal and the reference signal; represents the measured signal; represents the ideal simulation signal; represents the response function of the adaptive filtering algorithm.

[0043] The optimization algorithm adopted is the stochastic gradient descent, that is, the coefficient vector of the next moment is iteratively calculated from the gradient estimation of the mean square error of the current moment filter coefficient vector. The coefficient update of each iterative calculation is in the direction of the gradient descent. In order to find the direction of the maximum descent, a data point can be randomly selected to calculate the gradient, or all data participate in the operation. If multiple data participate in the operation, the calculation amount will increase, but the solution effect can tend to the global solution. At this time, the filter response function at the next moment T + 1 can be calculated by the following formula, where represents the gradient vector of the mean square error of the residual between the reference signal and the measured satellite signal. μ is the step factor, which is used to control the stability and convergence speed. The adjustment of the filter parameters can be solved according to the following formula:

[0044] In this embodiment, after collecting the measured signal of the satellite, it is necessary to estimate the carrier frequency at the current moment, and then obtain the Doppler information to calculate the center frequency of the actual signal. The spectral analysis uses pwelch to divide a data with a length of N into L segments, each segment has a length of M, and calculate the power spectrum of each segment respectively. The power spectra of all segments are averaged. Allowing partial overlap of each segment of data can reduce the variance of the spectral estimation through averaging processing. By selecting an appropriate window function, the leakage of the spectrum can be reduced and the frequency estimation resolution can be improved.

[0045] Exemplarily, based on constructing the system model framework of the transmission bandwidth estimation and the LMS adaptive filtering algorithm, this embodiment provides a method for estimating the signal transmission bandwidth based on the adaptive filtering algorithm. The specific implementation steps are as follows: S1. Acquisition of measured signal: The satellite signal is sampled using a sampling frequency lower than the highest frequency of the signal to ensure that a digital signal without information loss is obtained. The selection of this sampling frequency needs to consider both the signal integrity and the convenience of subsequent processing, and avoid introducing too much noise or losing key information due to improper sampling.

[0046] S2. Simulation of Ideal Signal Generation: Sample the ideal signal based on the signal modulation method, modulation parameters, and multiplexing method of the system, combined with the Doppler information of the actual signal. By introducing the Doppler information, the frequency characteristics of the ideal signal are closely corresponding to those of the actual signal, ensuring the comparability of the two in subsequent processing. For example, calculate the Doppler frequency shift based on the measured satellite motion state, and then adjust the carrier frequency of the ideal signal to achieve precise matching.

[0047] S3. Adaptive Sliding Parameter Setting of Filter: Determine the parameter categories of the adaptive filter, including the passband cut-off frequency, stopband start frequency, minimum stopband attenuation, and maximum stopband attenuation. The parameter range is determined by the residuals between the actual signal and the ideal signal. By analyzing the spectral characteristics of the residuals between the two, find the frequency bands where signal distortion or mismatch is relatively serious, and set the parameter range reasonably based on this to ensure that the filter can optimize the signal targeted. The sliding step is determined by the bandwidth estimation accuracy. If a high bandwidth estimation accuracy is required, set a smaller sliding step to finely adjust the filter parameters, but this will increase the calculation amount and processing time; on the contrary, the sliding step can be appropriately increased to improve the processing efficiency.

[0048] S4. Adaptive Filtering for Upper and Lower Sidebands Separately: Considering the poor left-right symmetry of the actual satellite filter, to significantly improve the accuracy of the transmit bandwidth estimation, perform adaptive filtering matching calculations on the decomposition of the upper and lower sidebands. Independently adjust the parameters of the adaptive filter according to the signal characteristics of the upper and lower sidebands respectively, so that the signals of the upper and lower sidebands after filtering are in the best compliance with the measured signal. For example, for the high-frequency noise in the upper sideband signal, effectively suppress it by adjusting the parameters in the high-frequency band of the filter; for the low-frequency interference in the lower sideband signal, optimize the parameters of the low-frequency band filter to eliminate it.

[0049] S5. Spectrum Analysis and Processing: Use the pwelch method to read and analyze the actual signal segment by segment. Divide a data with a length of N into L segments, with each segment having a length of M, calculate the power spectrum of each segment respectively, and then take the average of the power spectra of all segments. Through this averaging processing method, effectively reduce the noise error of the actual signal and improve the accuracy of the spectrum estimation. At the same time, select appropriate window functions, such as the Hanning window, Hamming window, etc., to reduce the spectrum leakage and improve the frequency estimation resolution, ensuring that the actual signal and the ideal signal are processed under the same spectrum estimation method, spectrum analysis parameters, and smoothing times for subsequent precise comparative analysis.

[0050] S6. Center frequency determination: Perform Doppler estimation on the actual signal and compensate the theoretical center frequency information according to the estimation result. Since there is relative motion during the satellite's orbital operation, the received signal generates Doppler frequency shift. Through accurate Doppler estimation, the center frequency of the signal can be accurately restored, improving the accuracy of the center frequency point and providing a reliable benchmark for subsequent bandwidth estimation. For example, use relevant Doppler estimation algorithms, combine information such as satellite orbital parameters and the location of the ground receiving station, calculate the Doppler frequency shift in real time, and correct the center frequency.

[0051] S7. Interpolation and translation processing: To accurately calculate the residual between the actual signal and the filtered simulation signal information, interpolate the ideal signal frequency in the frequency domain to match the frequency resolution of the ideal signal with that of the actual signal, facilitating comparative analysis on the same frequency scale. In terms of amplitude, translate the ideal power spectrum according to the amplitude value at the center frequency point to ensure that the ideal power spectrum and the actual power spectrum are consistent in amplitude characteristics, reducing the impact of amplitude differences on residual calculation.

[0052] S8. Residual calculation: Calculate the spectrum residuals between the measured signals in the upper sideband and the lower sideband and the filtered simulation signals respectively. Determine the frequency range for calculating the minimum mean square error of the residuals according to the bandwidth boundary characteristics of the actual signal. For example, if the approximate bandwidth range of a certain satellite signal is known to be [10 MHz, 20 MHz], then calculate the residuals precisely within this range to avoid invalid calculations in irrelevant frequency bands, improving calculation efficiency and accuracy.

[0053] S9. Residual gradient calculation: According to the stochastic gradient descent algorithm, randomly select a data point to calculate the gradient, so that the coefficient update in each iterative calculation moves in the direction of gradient descent, gradually approaching the optimal filter coefficients to minimize the residuals.

[0054] S10. Transmit bandwidth calculation: Through continuous iterative optimization, find the filter coefficients when the error function is minimized. In the case of optimal residuals, calculate the cut-off frequency according to the current filter parameters, and this cut-off frequency is the transmit bandwidth. At this time, the filter parameters have been optimally adapted to the measured signal, and the obtained transmit bandwidth estimation result has high accuracy; in this embodiment, the transmit bandwidth calculation formula is as follows:

[0055]

[0056] Wherein, is the minimum order of the filter, represents the passband edge frequency, represents the passband edge frequency, represents the maximum attenuation in the passband, represents the passband edge frequency, represents the cut-off frequency.

[0057] As Figure 3 shown, specifically as Figure 3 in (a), (b), (c), (d), (e), (f) of; By comparing and analyzing the satellite signal power spectra of different batches of a certain low-earth orbit satellite, it can be seen that there are obvious differences in the actual signal spectra of different satellites, and after narrowband filtering, the single-sided width of the filter is less than 20 MHz. Using the transmit bandwidth estimation method proposed in this embodiment, the parameters of the adaptive filter are dynamically adjusted according to the measured navigation signal spectrum characteristics to process the satellite signal; As Figure 4 shown, specifically as Figure 4 in (a), (b), (c), (d), (e), (f) of, the upper sideband and the lower sideband are in good agreement with the measured signal after adaptive filtering, which proves the effectiveness of this method in processing complex satellite signals. Through the tests of 6 satellites, the estimated results of their transmit bandwidths are distributed between 10 MHz and 19 MHz on a single side. The specific data are shown in Table 1, which further verifies that the method of the present invention can accurately estimate the transmit bandwidth of satellite signals under the conditions of complex satellite payloads and unstable hardware parameters.

[0058] Table 1 shows the estimated results of the transmit bandwidth of a certain low-earth orbit satellite. The unit of the transmit bandwidth: MHz

[0059] Exemplarily, as Figure 5 shown, this embodiment provides a signal transmit bandwidth estimation method based on an adaptive filtering algorithm, including the following steps: A signal transmit bandwidth estimation method based on an adaptive filtering algorithm, including: Based on the measured signal of the satellite, obtaining an ideal simulation signal corresponding to the information of the measured signal; Using the adaptive filtering algorithm to perform adaptive filtering processing on the ideal simulation signal and output a reference signal; Based on the residual calculated from the measured signal and the reference signal, using the stochastic gradient descent method to iteratively update the response function of the adaptive filtering algorithm until the minimum residual mean square error is obtained; Based on the reference signal output by the last iterative update, calculating the signal transmit bandwidth.

[0060] In this embodiment, the simulating and generating an ideal simulation signal corresponding to the information of the measured signal based on the collected measured signal includes: According to the Doppler information of the collected measured signal, simulating and generating an ideal distortionless signal; The Doppler information includes modulation mode, modulation parameters, multiplexing mode, code rate, pseudo-code period, and carrier frequency; Resample the ideal distortion - free signal according to the measured satellite A / D sampling frequency to obtain an ideal simulation signal.

[0061] In this embodiment, before using the adaptive filtering algorithm to perform adaptive filtering on the ideal simulation signal and output a reference signal, it further includes: Initialize the parameter categories, parameter ranges, and sliding steps of the adaptive filtering algorithm; among them, the parameter categories include the passband cut - off frequency, stopband start frequency, minimum stopband attenuation, and maximum stopband attenuation; the parameter range is determined by the residual between the measured signal and the reference signal; the sliding step is determined by the pre - estimated transmission bandwidth.

[0062] In this embodiment, using the adaptive filtering algorithm to perform adaptive filtering on the ideal simulation signal and output a reference signal includes: Decompose the ideal simulation signal into upper and lower sidebands to obtain the upper sideband and lower sideband of the ideal simulation signal; Use the adaptive filtering algorithm to perform adaptive filtering on the upper and lower sidebands of the ideal simulation signal respectively, and output the upper sideband reference signal and the lower sideband reference signal.

[0063] In this embodiment, before using the random gradient descent method to iteratively update the response function of the adaptive filtering algorithm based on the residual calculated from the measured signal and the reference signal until the minimum residual mean - square error is obtained, it includes: Perform domain - spectrum analysis on the measured signal and the reference signal respectively according to the same spectrum estimation method, spectrum analysis parameters, and smoothing times to obtain the actual power spectrum and the ideal power spectrum; among them, the spectrum estimation method uses Doppler estimation to compensate for the theoretical center - frequency information; Perform interpolation and translation processing on the reference signal according to the actual power spectrum and the ideal power spectrum; Calculate the residual between the measured signal and the reference signal, and the specific formula is as follows:

[0064] In the formula, represents the residual between the measured signal and the reference signal; represents the measured signal; represents the ideal simulation signal; represents the response function of the adaptive filtering algorithm.

[0065] In this embodiment, using the random gradient descent method to iteratively update the response function of the adaptive filtering algorithm based on the residual calculated from the measured signal and the reference signal until the minimum residual mean - square error is obtained includes: Based on the residual calculated from the measured signal and the reference signal, randomly select any data point to obtain the gradient, and then iteratively update the response function of the adaptive filtering algorithm; among them, the parameter update in each iterative calculation is in the direction of gradient descent. The specific iterative formula is as follows:

[0066] In the formula, represents the response function of the adaptive filtering algorithm; T represents the current moment, and T + 1 represents the next moment; represents the response function of the adaptive filtering algorithm at the current moment; represents the response function of the adaptive filtering algorithm at the next moment; μ represents the step size factor; represents the gradient vector of the mean square error of the residual between the measured signal and the reference signal; T represents the residual between the measured signal and the reference signal at the current moment; represents the ideal simulation signal.

[0067] In this embodiment, the formula for calculating the transmission bandwidth is as follows:

[0068]

[0069] Among them, is the minimum order of the filter, represents the passband edge frequency, represents the passband edge frequency, represents the maximum attenuation in the passband, represents the passband edge frequency, represents the cut-off frequency.

[0070] As Figure 6 shown, this embodiment also provides a signal transmission bandwidth estimation system based on the adaptive filtering algorithm, including: a simulation module for obtaining an ideal simulation signal corresponding to the measured signal information based on the measured signal of the satellite; an adaptive filtering module for adaptively filtering the ideal simulation signal using the adaptive filtering algorithm and outputting a reference signal; an iterative update module for iteratively updating the response function of the adaptive filtering algorithm using the random gradient descent method based on the residual calculated from the measured signal and the reference signal until the minimum mean square error of the residual is obtained; a calculation module for calculating the signal transmission bandwidth based on the reference signal output by the last iterative update.

[0071] The present invention also provides an electronic device, including: a memory for storing a computer program; and a processor for implementing the steps of the signal transmission bandwidth estimation method based on the adaptive filtering algorithm when executing the computer program.

[0072] When the processor executes the computer program, it implements the steps of the above-mentioned signal transmission bandwidth estimation based on the adaptive filtering algorithm. For example: based on the measured signal of the satellite, an ideal simulation signal corresponding to the measured signal information is obtained; the ideal simulation signal is adaptively filtered by using the adaptive filtering algorithm to output a reference signal; based on the residual obtained by calculating the measured signal and the reference signal, the response function of the adaptive filtering algorithm is iteratively updated by using the stochastic gradient descent method until the minimum residual mean square error is obtained; based on the reference signal output by the last iterative update, the signal transmission bandwidth is calculated.

[0073] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-mentioned system. For example: a simulation module for obtaining an ideal simulation signal corresponding to the measured signal information based on the measured signal of the satellite; an adaptive filtering module for adaptively filtering the ideal simulation signal by using the adaptive filtering algorithm to output a reference signal; an iterative update module for iteratively updating the response function of the adaptive filtering algorithm by using the stochastic gradient descent method based on the residual obtained by calculating the measured signal and the reference signal until the minimum residual mean square error is obtained; a calculation module for calculating the signal transmission bandwidth based on the reference signal output by the last iterative update.

[0074] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the signal transmission bandwidth estimation device based on the adaptive filtering algorithm. For example, the computer program can be divided into a simulation module, an adaptive filtering module, an iterative update module, and a calculation module; the simulation module is used to obtain an ideal simulation signal corresponding to the measured signal information based on the measured signal of the satellite; the adaptive filtering module is used to adaptively filter the ideal simulation signal by using the adaptive filtering algorithm to output a reference signal; the iterative update module is used to iteratively update the response function of the adaptive filtering algorithm by using the stochastic gradient descent method based on the residual obtained by calculating the measured signal and the reference signal until the minimum residual mean square error is obtained; the calculation module is used to calculate the signal transmission bandwidth based on the reference signal output by the last iterative update.

[0075] The signal emission bandwidth estimation device based on the adaptive filtering algorithm may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The signal emission bandwidth estimation device based on the adaptive filtering algorithm may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above are examples of the signal emission bandwidth estimation device based on the adaptive filtering algorithm, and do not constitute a limitation on the signal emission bandwidth estimation device based on the adaptive filtering algorithm. It may include more components than the above, or combine some components, or different components. For example, the signal emission bandwidth estimation device based on the adaptive filtering algorithm may also include input and output devices, network access devices, a bus, etc.

[0076] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center of the signal emission bandwidth estimation based on the adaptive filtering algorithm, and uses various interfaces and lines to connect all parts of the signal emission bandwidth estimation device based on the adaptive filtering algorithm.

[0077] The memory may be used to store the computer programs and / or modules. The processor realizes various functions of the signal emission bandwidth estimation device based on the adaptive filtering algorithm by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory.

[0078] The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0079] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the signal transmission bandwidth estimation method based on an adaptive filtering algorithm are implemented.

[0080] If the modules / units integrated in the signal transmission bandwidth estimation system based on the adaptive filtering algorithm are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0081] Based on such an understanding, all or part of the processes in the signal transmission bandwidth estimation method based on the adaptive filtering algorithm of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the signal transmission bandwidth estimation method based on the adaptive filtering algorithm can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or preset intermediate form, etc.

[0082] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0083] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0084] The present invention provides a signal transmission bandwidth estimation method based on an adaptive filtering algorithm, having the following advantages: By adopting the signal transmission bandwidth estimation method, the technical problem of measuring the signal transmission bandwidth in the non-scanning mode is solved, and the evaluation of the signal transmission bandwidth index in the online working state of the device is realized; this method can dynamically adjust the parameters of the adaptive filter according to the measured spectrum characteristics of the navigation signal, and calculate the transmission bandwidth under the filter response function with the highest matching degree, solving the problems of inaccurate and difficult estimation of the transmission bandwidth generated by complex loads.

[0085] The above embodiments are merely one of the implementation manners capable of implementing the technical solution of the present invention. The scope of protection required by the present invention is not limited solely by this embodiment, but also includes any changes, substitutions, and other implementation manners that are easily conceivable by any person skilled in the art within the technical scope disclosed by the present invention.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that it is still possible to make modifications or equivalent substitutions to the specific implementation manners of the present invention. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating the signal transmission bandwidth based on an adaptive filtering algorithm, characterized in that including: Based on the measured satellite signals, obtaining ideal simulation signals corresponding to the information of the measured signals; Using an adaptive filtering algorithm to perform adaptive filtering on the ideal simulation signals and outputting reference signals; Based on the residuals calculated from the measured signals and the reference signals, using the stochastic gradient descent method to iteratively update the response function of the adaptive filtering algorithm until the minimum residual mean square error is obtained; Based on the reference signals output from the last iterative update, calculating the signal transmission bandwidth.

2. The method for estimating the signal transmission bandwidth based on the adaptive filtering algorithm according to claim 1, wherein The simulating and generating of ideal simulation signals corresponding to the information of the measured signals based on the collected measured signals includes: According to the Doppler information of the collected measured signals, simulating and generating ideal distortionless signals; the Doppler information includes modulation mode, modulation parameters, multiplexing mode, code rate, pseudo-code period, and carrier frequency; Resampling the ideal distortionless signals according to the measured satellite A / D sampling frequency to obtain ideal simulation signals.

3. A signal transmission bandwidth estimation method based on an adaptive filtering algorithm according to claim 1, wherein Before using the adaptive filtering algorithm to perform adaptive filtering on the ideal simulation signals and outputting reference signals, it further includes: Initializing the parameter category, parameter range, and sliding step of the adaptive filtering algorithm; where the parameter category includes passband cut-off frequency, stopband start frequency, minimum stopband attenuation, and maximum stopband attenuation; the parameter range is determined by the residuals between the measured signals and the reference signals; the sliding step is determined by the pre-estimated transmission bandwidth.

4. A method for estimating the signal transmission bandwidth based on an adaptive filtering algorithm according to claim 1, characterized in that, The using of the adaptive filtering algorithm to perform adaptive filtering on the ideal simulation signals and outputting reference signals includes: Performing upper and lower sideband decomposition on the ideal simulation signals to obtain the upper sideband and lower sideband of the ideal simulation signals; Using the adaptive filtering algorithm to perform adaptive filtering on the upper sideband and lower sideband of the ideal simulation signals respectively, and outputting the upper sideband reference signal and the lower sideband reference signal.

5. A method for estimating the signal transmission bandwidth based on an adaptive filtering algorithm according to claim 1, characterized in that Before using the stochastic gradient descent method to iteratively update the response function of the adaptive filtering algorithm based on the residuals calculated from the measured signals and the reference signals until the minimum residual mean square error is obtained, it includes: Performing domain spectrum analysis on the measured signals and the reference signals respectively according to the same spectrum estimation method, spectrum analysis parameters, and smoothing times to obtain the actual power spectrum and the ideal power spectrum; where the spectrum estimation method uses Doppler estimation to compensate for the theoretical center frequency information; Performing interpolation and translation processing on the reference signals according to the actual power spectrum and the ideal power spectrum; Calculating the residuals between the measured signals and the reference signals, and the specific formula is as follows: In the formula, represents the residual between the measured signal and the reference signal; represents the measured signal; represents the ideal simulation signal; represents the response function of the adaptive filtering algorithm.

6. A signal transmission bandwidth estimation method based on an adaptive filtering algorithm according to claim 1, characterized in that The using of the stochastic gradient descent method to iteratively update the response function of the adaptive filtering algorithm based on the residuals calculated from the measured signals and the reference signals until the minimum residual mean square error is obtained includes: Based on the residuals calculated from the measured signals and the reference signals, randomly selecting any data point to calculate the gradient, and then realizing the iterative update of the response function of the adaptive filtering algorithm; where the parameter update calculated in each iteration is in the direction of gradient descent; The specific iterative formula is as follows: In the formula, represents the response function of the adaptive filtering algorithm; T represents the current moment, and T + 1 represents the next moment; represents the response function of the adaptive filtering algorithm at the current moment; represents the response function of the adaptive filtering algorithm at the next moment; μ represents the step size factor; represents the gradient vector of the mean square error of the residual between the measured signal and the reference signal; T represents the residual between the measured signal and the reference signal at the current moment; represents the ideal simulation signal.

7. A method for estimating the signal transmission bandwidth based on an adaptive filtering algorithm according to claim 1, characterized in that, In the calculating of the signal transmission bandwidth based on the reference signals output from the last iterative update, the specific formula of the signal transmission bandwidth is as follows: Among them, is the minimum order of the filter, represents the passband edge frequency, represents the passband edge frequency, represents the maximum passband attenuation, represents the passband edge frequency, represents the cut-off frequency.

8. A signal transmission bandwidth estimation system based on an adaptive filtering algorithm, characterized in that, including: A simulation module, configured to obtain an ideal simulation signal corresponding to the measured signal information based on the measured signal of the satellite; An adaptive filtering module, configured to perform adaptive filtering processing on the ideal simulation signal by using an adaptive filtering algorithm and output a reference signal; An iterative update module, configured to perform iterative update on the response function of the adaptive filtering algorithm by using the stochastic gradient descent method based on the residual obtained by calculating the measured signal and the reference signal until the minimum residual mean square error is obtained; A calculation module, configured to calculate the signal transmission bandwidth based on the reference signal output by the last iterative update.

9. An electronic device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the signal transmission bandwidth estimation method based on the adaptive filtering algorithm according to any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the signal transmission bandwidth estimation method based on the adaptive filtering algorithm according to any one of claims 1-7.

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