Low-orbit satellite combined frequency offset estimation and channel equalization method in high-speed moving scene

Abstract

The invention discloses a low-orbit satellite joint frequency offset estimation and channel equalization method in a high-speed moving scene, which comprises the following steps of: constructing a dynamic coupling state space model to realize joint estimation of frequency offset and channel parameters; associating a frequency offset change rate with the dynamic change of multipath time delay through a state transition equation; the association is based on relative motion parameters and radio wave propagation characteristics of the satellite and the mobile terminal; according to the method, state estimation is carried out by adopting a filtering algorithm, channel multipath sparsity constraint is introduced into the filtering algorithm, and satellite orbit parameters and navigation data of a mobile terminal are fused to serve as priori information, so that joint estimation of frequency offset and channel parameters is realized through a dynamic coupling state space model; the problem of parameter error accumulation in traditional separated processing is solved, and the estimation precision is improved; a pilot frequency-data dual-drive mechanism is combined with time-frequency orthogonal pilot frequency and data feedback, and the estimation real-time performance and the dynamic adjustment capability of the equalizer are both considered.

Description

Technical Field

[0001] This invention belongs to the field of low-Earth orbit satellite communication technology, and particularly relates to a method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios. Background Technology

[0002] Low-Earth orbit (LEO) satellite communication systems have become one of the core technologies supporting high-speed mobile scenarios (such as high-speed rail, aviation, and ocean shipping) due to their advantages such as wide coverage and low transmission latency. In existing technologies, frequency offset estimation and channel equalization methods for high-speed mobile scenarios have formed a certain technical system: in terms of frequency offset estimation, methods such as pilot-based FFT frequency offset detection and Kalman filter tracking are often used to achieve coarse estimation and fine correction of Doppler frequency offset through time-domain or frequency-domain pilots; in terms of channel equalization, adaptive algorithms such as minimum mean square error (LMS) and recursive least squares (RLS) are often used, combined with frequency-domain equalization or time-domain equalizers to compensate for multipath fading.

[0003] Meanwhile, existing technologies improve synchronization and channel estimation performance through pilot design optimization (such as comb pilots and block pilots), utilize satellite orbit parameters and mobile terminal positioning data to assist in motion trajectory prediction, and some schemes introduce sparse reconstruction algorithms to handle the sparsity characteristics of multipath channels, thereby reducing equalization complexity. These technologies have been applied in low-to-medium speed mobile scenarios (such as vehicle speeds ≤200km / h), supporting the basic communication needs between low-Earth orbit satellites and ground terminals.

[0004] Existing technologies have significant limitations in high-speed mobile scenarios (e.g., vehicle speeds ≥ 300 km / h): First, frequency offset estimation and channel equalization are often processed separately, failing to consider their dynamic coupling relationship. This leads to accumulated frequency offset errors affecting equalization performance, or equalization residuals interfering with frequency offset tracking accuracy. Second, pilot design fails to achieve efficient coordination of time and frequency resources, and the orthogonal multiplexing capability of frequency offset-sensitive and channel-sensitive pilots is insufficient, making it difficult to balance estimation accuracy and real-time performance. Third, filtering algorithms have limited adaptability to non-Gaussian noise and multipath sparsity; traditional Kalman filtering significantly increases estimation errors in strongly nonlinear scenarios. Fourth, the lack of a parameter smoothing transition mechanism during beam switching increases communication interruption duration. Fifth, the linkage between anti-interference processing and frequency offset estimation and channel equalization is insufficient, making it difficult to cope with the impact of neighboring satellite interference or ground clutter on communication stability in high-speed scenarios. These problems restrict the application effectiveness of low-Earth orbit satellite communication in high-speed mobile scenarios. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios, which solves the problems of separate processing of frequency offset and channel equalization, insufficient pilot coordination, poor filter adaptation, lack of smooth beam switching, and weak anti-interference linkage in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] 1. A method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios, comprising the following steps:

[0008] (1) Constructing a dynamic coupled state-space model to achieve joint estimation of frequency offset and channel parameters: The frequency offset change rate is correlated with the dynamic change of multipath delay through the state transition equation. The correlation is based on the relative motion parameters of the satellite and the mobile terminal and the radio wave propagation characteristics. The state transition equation adopts a parameter-feedback dynamic coupling mechanism, specifically:

[0009] in, Let k be the rate of change of frequency offset. This represents the mean change in multipath delay. For the i-th multipath delay, , , , The coupling coefficient is used; this mechanism breaks through the limitation of independent parameter updates in traditional separate modeling, and achieves real-time adaptation of parameter estimation in high dynamic scenarios through bidirectional feedback of frequency offset change rate and multipath delay.

[0010] A filtering algorithm is used for state estimation. This algorithm introduces sparsity constraints for channel multipath and integrates satellite orbit parameters and navigation data from the mobile terminal as prior information. The sparsity constraints are dynamically adjusted through time-varying thresholds, i.e., the weights of non-dominant paths are automatically reduced based on the instantaneous energy of the multipath components, thus solving the adaptation failure problem of the traditional fixed sparsity assumption in channel abrupt change scenarios.

[0011] (2) Frequency offset estimation and equalizer linkage optimization through pilot-data dual-drive: A time-frequency orthogonal pilot structure is designed, comprising a time-domain pilot for frequency offset estimation and a frequency-domain pilot for channel detection. The time-domain and frequency-domain pilots are transmitted simultaneously through orthogonal multiplexing. The pilot structure supports dynamic resource scheduling; when the mobile terminal speed exceeds 500 km / h, the power ratio of the time-domain pilot is automatically increased to 60%-70%, prioritizing the accuracy of frequency offset estimation. During the data transmission phase, the frequency offset residual is calculated based on the equalized decision data. The frequency offset change rate is updated according to the frequency offset residual, and the update step size of the channel equalizer tap coefficients is dynamically adjusted. The update step size adopts a residual-speed dual-factor adaptive mechanism, with the formula: in, For the instantaneous speed of the terminal, To maximize design speed, this mechanism addresses the convergence oscillation problem caused by fixed step sizes in high-speed scenarios by jointly weighting the residuals and speed. The iteration process is terminated in real-time based on a preset equilibrium quality index.

[0012] During the data transmission phase, the frequency offset residual is calculated based on the equalized decision data. The frequency offset change rate is updated according to the frequency offset residual, and the update step size of the channel equalizer tap coefficients is dynamically adjusted to form a real-time linkage between frequency offset estimation and channel equalization.

[0013] The iteration process is terminated in real time based on the preset balanced quality index.

[0014] (3) Achieve dual-domain coordinated compensation of frequency offset and channel through Doppler-multipath joint frequency domain processing:

[0015] Establish a correlation model that reflects the frequency offset of multipath components and the channel frequency domain response;

[0016] By using frequency domain analysis with a sliding window to track the spectral offset trend, coarse compensation for frequency offset can be achieved.

[0017] Based on the coarsely compensated signal, a sparse reconstruction algorithm is used to recover the channel matrix, and time-domain processing is combined to correct the residual effects of non-integer multiple frequency offset, forming a dual-domain synergy of frequency domain compensation and time-domain equalization.

[0018] (4) Combining motion trajectory prediction mapping to provide joint frequency offset-channel prior constraints:

[0019] Input satellite orbit parameters and navigation data from the mobile terminal, calculate the projection of the relative motion velocity between the satellite and the mobile terminal in the direction of radio wave propagation, and predict the instantaneous frequency deviation based on the projection result;

[0020] The predicted instantaneous frequency offset is fed back to the dynamic coupled state-space model in step (1) as the initial constraint for the joint estimation of frequency offset and channel parameters.

[0021] Preferably, the filtering algorithm is a variational Bayesian Kalman filter, and the parameters of the state transition equation are dynamically adjusted according to satellite orbit prediction data. When the mobile terminal speed is ≥500km / h and the Doppler frequency offset is ≥30kHz, the variational Bayesian Kalman filter processes the non-Gaussian noise of the multipath component through probabilistic modeling. Compared with the traditional extended Kalman filter, the frequency offset estimation error is reduced by more than 20%, and the adaptability to the sparsity of channel multipath is improved by 30%.

[0022] Preferably, the time-domain pilot uses a constant envelope sequence, the frequency-domain pilot uses comb-distributed subcarriers, and the power of the time-domain pilot and the frequency-domain pilot is allocated according to a preset ratio; the orthogonal multiplexing is achieved through the time-frequency resource allocation of OFDM symbols to ensure that the frequency offset estimation and channel detection signals do not interfere with each other.

[0023] Preferably, the Doppler-multipath joint frequency domain processing in step (3) further includes: separating each multipath signal by matched filtering to eliminate the coupling effect of Doppler frequency offset and multipath delay; the channel equalizer adopts a linear minimum mean square error equalization structure and combines the channel prior covariance matrix to optimize the equalization performance, and the prior covariance matrix is ​​dynamically updated based on the motion trajectory prediction results in step (4).

[0024] Preferably, the motion trajectory prediction mapping in step (4) further includes: calculating the angle of arrival based on the location data of the satellite and the mobile terminal, and correcting the angle of arrival in real time through a filtering algorithm; and dynamically selecting the corresponding channel parameter prior distribution to initialize the equalizer based on the environmental perception data of the mobile terminal (including terrain features or occlusion information).

[0025] Preferably, it also includes a joint processing step during beam switching:

[0026] Before switching, the relative velocity difference is calculated based on the orbital angle between the satellite to be switched and the current satellite, the initial frequency offset and channel parameters of the satellite to be switched are estimated, and these are used as the initial values ​​of the state model in step (1) before switching.

[0027] During the handover process, the frequency offset and channel parameters of the current satellite and the satellite to be handed over are weighted and fused during the beam overlap period. The weighting coefficients are dynamically adjusted according to the received signal strength of both satellites to ensure the continuity of the joint estimation before and after the handover.

[0028] Preferably, it also includes a residual-based correction step: the theoretical values ​​of frequency offset estimation and channel equalization are corrected by a preset correction network. The input of the correction network is the frequency offset estimation residual and the channel equalization error, and the output is the corrected frequency offset compensation value and the equalizer coefficient adjustment amount. The correction result is fed back to the dynamic adjustment process of step (2) in real time.

[0029] Preferably, the low-orbit satellite communication system is suitable for meeting the requirements of high-speed mobile scenarios, such as beam switching period, Doppler frequency offset range, and channel coherence time. The system supports mobile terminal speeds ≤1000km / h and satellite orbital altitudes of 500-1500km.

[0030] Preferably, the pilot design in step (2) further includes:

[0031] A repetitive pilot sequence is inserted before each data segment. The pilot sequence includes a first pilot segment and a second pilot segment. The first pilot segment is used for phase synchronization, and the second pilot segment is used for timing synchronization.

[0032] Frequency offset estimation is performed by combining Hilbert transform with frequency domain analysis. Carrier acquisition is achieved by analyzing the complex vector of the signal. The acquisition result is directly used for the initialization of frequency offset residual calculation in step (2).

[0033] Preferably, it also includes an auxiliary anti-interference processing step:

[0034] Interference subcarriers are identified and suppressed by frequency domain energy detection to reduce the impact of neighboring satellite signals or ground clutter on the frequency offset estimation accuracy. The suppression results are fed back to the frequency domain joint processing in step (3).

[0035] Based on the time-domain distribution characteristics of normal communication signals, effective signals are screened to filter out sudden interference and ensure the stability of channel balance. The screening results are used to verify the validity of the decision data in step (2).

[0036] The technical effects and advantages of the low-Earth orbit satellite joint frequency offset estimation and channel equalization method for high-speed mobile scenarios proposed in this invention are as follows:

[0037] 1. This invention achieves joint estimation of frequency offset and channel parameters through a dynamically coupled state-space model, overcoming the problem of parameter error accumulation in traditional separate processing and improving estimation accuracy; the pilot-data dual-drive mechanism combines time-frequency orthogonal pilots and data feedback, taking into account both the real-time estimation and the dynamic adjustment capability of the equalizer; the Doppler-multipath joint frequency domain processing utilizes sparse reconstruction and dual-domain collaborative compensation to reduce the processing complexity in complex scenarios; motion trajectory prediction mapping provides prior constraints for joint estimation, enhancing robustness in highly dynamic scenarios.

[0038] 2. This invention features an optimized filtering algorithm that adapts to non-Gaussian noise and multipath sparsity, improving performance stability under extreme conditions; an improved pilot design that accelerates synchronization; smooth beam switching that reduces communication interruptions; and an anti-interference mechanism that enhances communication reliability in complex electromagnetic environments. The overall solution effectively meets the real-time, accuracy, and stability requirements of low-Earth orbit satellite communication in high-speed mobile scenarios, and is suitable for satellite communication scenarios of various high-speed mobile terminals such as high-speed rail and aviation. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios proposed in this invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] Example 1

[0043] refer to Figure 1 This embodiment provides a method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios. Specifically, it provides a method for joint frequency offset estimation and channel equalization in high-speed rail scenarios based on a dynamic coupling model. The specific implementation includes:

[0044] Objective: To verify the effectiveness of the core solution in a 350km / h high-speed rail scenario, and to solve the problems of rapid frequency offset and fast channel fading caused by high-speed movement through the synergistic effect of dynamic coupling modeling, dual-drive optimization, joint frequency domain processing and trajectory prediction.

[0045] Implementation steps:

[0046] (1) Construct a dynamically coupled state-space model:

[0047] Define state vector ,in For instantaneous frequency deviation, The frequency deviation rate, It is a complex gain with 3 multipath paths.

[0048] The state transition equations are based on parametric feedback: in The coupling coefficient, representing the mean change in multipath delay, is determined through simulation optimization. A variational Bayesian Kalman filter is employed, incorporating a sparsity constraint on the time-varying threshold: for Set dynamic threshold ,when The system forces zeroing out the data to address the adaptation failure issue caused by the fixed sparsity assumption during channel abrupt changes. It also integrates high-speed rail GPS data (positioning error ≤ 5m) with satellite orbit prediction data (orbit error ≤ 100m).

[0049] (2) Pilot-data dual-drive optimization

[0050] Pilot structure: 128-point ZC sequence in the time domain (2 OFDM symbols), QPSK pilots inserted every 16 points in 1024 subcarriers in the frequency domain, orthogonally multiplexed through time-frequency resource allocation.

[0051] Data segment processing: Calculate the frequency offset residual (threshold 50Hz) based on the equalization decision data, and adjust the equalizer taps using the 16th order variable step size RLS algorithm. The iteration terminates when the constellation diagram clustering error is <0.05.

[0052] (3) Doppler-multipath joint frequency domain processing

[0053] Frequency domain model: Coarse frequency offset compensation (accuracy ±2Hz) is achieved through a 512-point sliding window FFT.

[0054] Sparse reconstruction: The channel matrix is ​​recovered using the SBL algorithm, and a 16th-order FIR filter is used to correct the residual non-integer multiple frequency offset (accuracy ±0.1Hz).

[0055] (4) Motion trajectory prediction mapping

[0056] Calculate relative velocity projection ,predict The feedback is sent to step (1) as the initial constraint of the state model.

[0057] Implementation results:

[0058] Frequency offset estimation error ≤ 5Hz, BER after channel equalization ≤ 1e-5, convergence time ≤ 20ms, meeting the real-time communication requirements of high-speed rail scenarios at 350km / h.

[0059] Example 2

[0060] This embodiment provides a method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios, and a non-Gaussian noise suppression scheme for variational Bayesian Kalman filtering. Specific implementation details include:

[0061] Objective: To verify the advantages of variational Bayesian Kalman filtering (VBKF) over traditional extended Kalman filtering (EKF) and to solve the problem of insufficient estimation accuracy caused by multipath non-Gaussian noise in high-speed moving scenarios.

[0062] Implementation steps:

[0063] (1) Optimization of filtering algorithm:

[0064] The state vector is the same as in Example 1. The VBKF state transition matrix is ​​updated every 10ms based on satellite orbit prediction data, and a non-Gaussian noise probability model is introduced (the variance is dynamically adjusted with the multipath intensity).

[0065] Sparsity constraint: The channel estimation dimension is reduced from 5 dimensions to 3 dimensions by using Laplace prior, thus reducing computational complexity.

[0066] Steps (2)-(4) are the same as steps (2)-(4) in Example 1.

[0067] Implementation results:

[0068] In a scenario with a speed of 500 km / h and a Doppler frequency offset of 30 kHz, the VBKF frequency offset estimation error is 3.2 Hz (EKF is 16.5 Hz), the channel MSE is -28 dB (EKF is -20 dB), the error is reduced by more than 20%, and the sparsity adaptability is improved by 30%.

[0069] Example 3

[0070] This embodiment provides a method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios, based on a synchronization scheme for high-speed mobile scenarios using dual-segment pilots. Specific implementation details include:

[0071] Objective: To optimize pilot design, improve phase synchronization and timing synchronization accuracy in high-speed mobile scenarios, and shorten frequency offset acquisition time.

[0072] Implementation steps: (1)-(4) are the same as in Example 1, except that step (2) is detailed as follows:

[0073] Step (2): Pilot structure optimization

[0074] Repeating pilot sequence: Two pilot symbols are inserted before each data segment, including a 64-point m-sequence (phase synchronization, accuracy ±0.1 rad) and a 64-point Barker code (timing synchronization, error ≤1 sampling point).

[0075] Frequency offset estimation: An analytical signal is generated by Hilbert transform, and the frequency offset is captured by 1024-point FFT (range ±50kHz, accuracy ±1Hz). The result is directly used for residual calculation initialization.

[0076] Implementation results:

[0077] Pilot synchronization success rate ≥99.9%, frequency offset acquisition time ≤5ms (7ms in Example 1), synchronization speed improved by 30%, adapting to the rapidly changing needs of high-speed mobile scenarios.

[0078] Example 4

[0079] This embodiment provides a method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios, based on a parameter smoothing transition scheme in low-Earth orbit satellite beam switching. Specific implementation details include:

[0080] Purpose of implementation:

[0081] Verify the effectiveness of beam switching processing and resolve communication interruption issues caused by frequency offset and sudden changes in channel parameters during the switching process.

[0082] Implementation steps: (1)-(4) The basic steps are the same as in Example 1, with the following additional switching process:

[0083] Pre-switch estimation:

[0084] 500ms before the switch, based on the angle θ between the two satellite orbits 12 Calculate the relative velocity difference Δv at 30° to pre-estimate the initial frequency offset of the target satellite. , as the initial value of the state model in step (1).

[0085] Fusion during switching:

[0086] During the overlap period, according to the received power ratio The current frequency offset / channel parameters of the target satellite are weighted and fused, and the results are used for equalizer adjustment in steps (2) and (3).

[0087] Implementation effect

[0088] During the handover process, the BER fluctuation is ≤3e-6 with no obvious interruption, the convergence time is ≤20ms (50ms for the traditional method), and the handover latency is reduced by 60%.

[0089] Example 5

[0090] This embodiment provides a method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios, based on a frequency offset estimation and equalization anti-interference scheme under neighboring satellite interference scenarios. Specific implementation details include:

[0091] Purpose of implementation: To verify the effectiveness of anti-interference processing and solve the problem of decreased estimation accuracy caused by interference from neighboring satellites or ground clutter.

[0092] Implementation steps: Steps (1)-(4) are the same as in Example 1, except that steps (3) and (2) are added as follows:

[0093] Step (3): Frequency domain interference suppression. Interference subcarriers (200-300kHz band) are identified by frequency domain energy detection, and zero suppression is performed (depth ≥20dB). The results are fed back to frequency domain joint processing.

[0094] Step (2): Time domain signal screening. Based on the normal signal time domain distribution (1-2ms pulse width, 10ms period), sudden interference is filtered out. The screening results are used for judgment data verification.

[0095] Implementation results: In the case of neighboring satellite interference (interference power ratio -10dB), the frequency offset estimation error is ≤8Hz (25Hz without anti-interference), and the BER is ≤5e-5 (1e-3 without anti-interference), with a performance improvement of 2 orders of magnitude.

[0096] Comparative Example 1

[0097] This comparison provides a traditional scheme for separating the estimation of frequency offset and channel parameters.

[0098] Purpose of implementation: To compare the performance differences between the "joint processing" of this invention and the traditional "separate processing".

[0099] Implementation steps:

[0100] Frequency offset estimation: Based solely on time-domain pilot signals, using the FFT algorithm (without motion trajectory prediction).

[0101] Channel equalization: Based on frequency domain pilot signals, a fixed step size LMS algorithm is used (step size 0.01, no sparse reconstruction).

[0102] There is no inter-step feedback mechanism; frequency offset and channel parameters are processed independently.

[0103] Implementation results:

[0104] In the 350km / h scenario, the frequency offset estimation error is ≥30Hz (5Hz in Example 1), BER=5e-4 (1e-5 in Example 1), and the convergence time is ≥100ms (20ms in Example 1), demonstrating the superiority of the joint processing of the present invention.

[0105] Compared to Examples 1-5 and Comparative Example 1, the combined frequency offset estimation and channel equalization method of this invention has significant advantages in high-speed mobile scenarios. Comparative Example 1 uses traditional separate processing, with a frequency offset estimation error ≥30Hz, a BER of 5e-4 after channel equalization, and a convergence time ≥100ms, which cannot meet the requirements of high-speed communication.

[0106] Example 1, as the basic scheme, reduces the frequency offset error to ≤5Hz and the BER to ≤1e-5 through dynamic coupling modeling and dual-drive optimization, compressing the convergence time to 20ms, and outperforming the comparative example in all aspects. Example 2 optimizes the filtering algorithm, achieving a frequency offset error of 3.2Hz and an 8dB improvement in channel MSE in a 500km / h, 30kHz Doppler scenario, with significant non-Gaussian noise suppression. Example 3 improves the pilot design, achieving a synchronization success rate of 99.9%, reducing the acquisition time to 5ms, and increasing the synchronization speed by 30%. Example 4 reduces the latency by 60% for beam switching, achieving a BER fluctuation of ≤3e-6 and resolving the switching interruption problem. Example 5 enhances anti-interference capabilities, achieving a frequency offset error of ≤8Hz and a BER improvement of two orders of magnitude under adjacent satellite interference, ensuring communication stability in complex environments.

[0107] In summary, this invention, through a joint processing mechanism and multi-dimensional optimization, comprehensively surpasses traditional methods in terms of estimation accuracy, real-time performance, and scenario adaptability, thus meeting the needs of high-speed mobile low-orbit satellite communication.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0109] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0112] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for joint frequency offset estimation and channel equalization of low-Earth orbit satellites in high-speed mobile scenarios, characterized in that, Includes the following steps: (1) Constructing a dynamic coupled state-space model to achieve joint estimation of frequency offset and channel parameters: The frequency offset change rate is correlated with the dynamic change of multipath delay through the state transition equation. The correlation is based on the relative motion parameters of the satellite and the mobile terminal and the radio wave propagation characteristics. The state transition equation adopts a parameter-feedback dynamic coupling mechanism, specifically: ; in, Let k be the rate of change of frequency offset. This represents the mean change in multipath delay. For the i-th multipath delay, , , , The coupling coefficient is used; this mechanism breaks through the limitation of independent parameter updates in traditional separate modeling, and achieves real-time adaptation of parameter estimation in high dynamic scenarios through bidirectional feedback of frequency offset change rate and multipath delay. A filtering algorithm is used for state estimation. This algorithm introduces sparsity constraints for channel multipath and integrates satellite orbit parameters and navigation data from the mobile terminal as prior information. The sparsity constraints are dynamically adjusted through time-varying thresholds, i.e., the weights of non-dominant paths are automatically reduced based on the instantaneous energy of the multipath components, thus solving the adaptation failure problem of the traditional fixed sparsity assumption in channel abrupt change scenarios. (2) Frequency offset estimation and equalizer linkage optimization through pilot-data dual-drive: A time-frequency orthogonal pilot structure is designed, comprising a time-domain pilot for frequency offset estimation and a frequency-domain pilot for channel detection. The time-domain and frequency-domain pilots are transmitted simultaneously through orthogonal multiplexing. The pilot structure supports dynamic resource scheduling; when the mobile terminal speed exceeds 500 km / h, the power ratio of the time-domain pilot is automatically increased to 60%-70%, prioritizing the accuracy of frequency offset estimation. During the data transmission phase, the frequency offset residual is calculated based on the equalized decision data. The frequency offset change rate is updated according to the frequency offset residual, and the update step size of the channel equalizer tap coefficients is dynamically adjusted. The update step size adopts a residual-speed dual-factor adaptive mechanism, with the formula: in, For the instantaneous speed of the terminal, To maximize design speed, this mechanism addresses the convergence oscillation problem caused by fixed step sizes in high-speed scenarios by jointly weighting the residuals and speed. The iteration process is terminated in real-time based on a preset equilibrium quality index. During the data transmission phase, the frequency offset residual is calculated based on the equalized decision data. The frequency offset change rate is updated according to the frequency offset residual, and the update step size of the channel equalizer tap coefficients is dynamically adjusted to form a real-time linkage between frequency offset estimation and channel equalization. The iteration process is terminated in real time based on the preset balanced quality index. (3) Achieve dual-domain coordinated compensation of frequency offset and channel through Doppler-multipath joint frequency domain processing: Establish a correlation model that reflects the frequency offset of multipath components and the channel frequency domain response; By using frequency domain analysis with a sliding window to track the spectral offset trend, coarse compensation for frequency offset can be achieved. Based on the coarsely compensated signal, a sparse reconstruction algorithm is used to recover the channel matrix, and time-domain processing is combined to correct the residual effects of non-integer multiple frequency offset, forming a dual-domain synergy of frequency domain compensation and time-domain equalization. (4) Combining motion trajectory prediction mapping to provide joint frequency offset-channel prior constraints: Input satellite orbit parameters and navigation data from the mobile terminal, calculate the projection of the relative motion velocity between the satellite and the mobile terminal in the direction of radio wave propagation, and predict the instantaneous frequency deviation based on the projection result; The predicted instantaneous frequency offset is fed back to the dynamic coupled state-space model in step (1) as the initial constraint for the joint estimation of frequency offset and channel parameters.

2. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, The filtering algorithm is a variational Bayesian Kalman filter, and the parameters of the state transition equation are dynamically adjusted according to satellite orbit prediction data. When the mobile terminal speed is ≥500km / h and the Doppler frequency offset is ≥30kHz, the variational Bayesian Kalman filter processes the non-Gaussian noise of the multipath component through probabilistic modeling. Compared with the traditional extended Kalman filter, the frequency offset estimation error is reduced by more than 20%, and the adaptability to the sparsity of channel multipath is improved by 30%.

3. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, The time-domain pilot uses a constant envelope sequence, the frequency-domain pilot uses comb-distributed subcarriers, and the power of the time-domain pilot and the frequency-domain pilot is allocated according to a preset ratio; the orthogonal multiplexing is achieved through the time-frequency resource allocation of OFDM symbols to ensure that the frequency offset estimation and channel detection signals do not interfere with each other.

4. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, The Doppler-multipath joint frequency domain processing in step (3) also includes: separating each multipath signal by matched filtering to eliminate the coupling effect of Doppler frequency offset and multipath delay; the channel equalizer adopts a linear minimum mean square error equalization structure and combines the channel prior covariance matrix to optimize the equalization performance, and the prior covariance matrix is ​​dynamically updated based on the motion trajectory prediction results in step (4).

5. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, The motion trajectory prediction mapping in step (4) also includes: calculating the angle of arrival based on the location data of the satellite and the mobile terminal, and correcting the angle of arrival in real time through a filtering algorithm; and dynamically selecting the corresponding channel parameter prior distribution to initialize the equalizer based on the environmental perception data of the mobile terminal.

6. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, It also includes joint processing steps during beam switching: Before switching, the relative velocity difference is calculated based on the orbital angle between the satellite to be switched and the current satellite, the initial frequency offset and channel parameters of the satellite to be switched are estimated, and these are used as the initial values ​​of the state model in step (1) before switching. During the handover process, the frequency offset and channel parameters of the current satellite and the satellite to be handed over are weighted and fused during the beam overlap period. The weighting coefficients are dynamically adjusted according to the received signal strength of both satellites to ensure the continuity of the joint estimation before and after the handover.

7. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, It also includes a residual-based correction step: the theoretical values ​​of frequency offset estimation and channel equalization are corrected by a preset correction network. The input of the correction network is the frequency offset estimation residual and the channel equalization error, and the output is the corrected frequency offset compensation value and the equalizer coefficient adjustment amount. The correction result is fed back to the dynamic adjustment process of step (2) in real time.

8. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, This is a low-orbit satellite communication system suitable for high-speed mobile scenarios, with requirements for beam switching period, Doppler frequency offset range, and channel coherence time. The system supports mobile terminal speeds ≤1000km / h and satellite orbital altitudes of 500-1500km.

9. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, The pilot design in step (2) also includes: A repetitive pilot sequence is inserted before each data segment. The pilot sequence includes a first pilot segment and a second pilot segment. The first pilot segment is used for phase synchronization, and the second pilot segment is used for timing synchronization. Frequency offset estimation is performed by combining Hilbert transform with frequency domain analysis. Carrier acquisition is achieved by analyzing the complex vector of the signal. The acquisition result is directly used for the initialization of frequency offset residual calculation in step (2).

10. The method for joint frequency offset estimation and channel equalization of low-orbit satellites in high-speed mobile scenarios as described in claim 1, characterized in that, It also includes auxiliary anti-interference processing steps: Interference subcarriers are identified and suppressed by frequency domain energy detection to reduce the impact of neighboring satellite signals or ground clutter on the frequency offset estimation accuracy. The suppression results are fed back to the frequency domain joint processing in step (3). Based on the time-domain distribution characteristics of normal communication signals, effective signals are screened to filter out sudden interference and ensure the stability of channel balance. The screening results are used to verify the validity of the decision data in step (2).

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