Multi-satellite fusion narrowband communication method and device

Abstract

The invention relates to the technical field of narrowband communication, and discloses a multi-satellite fusion narrowband communication method and device, and the method comprises the steps: calculating a transmission time matching value of each satellite based on an LEO satellite orbit parameter, an MEO satellite orbit parameter and a GEO satellite orbit parameter; constructing a multi-satellite time delay difference measurement matrix according to the transmission time matching value, and performing time delay compensation on the narrowband communication receiving signal to obtain a time delay compensation receiving signal; performing multi-satellite time delay state estimation on the time delay compensation receiving signal to obtain a real-time time delay estimation value; narrowband carrier phase compensation is carried out on the time delay compensation receiving signal according to the real-time time delay estimation value, and a fused narrowband communication signal is obtained.The method can adapt to time delay changes caused by satellite orbital motion in real time, maximization of signal gains is achieved through coherent superposition, and the receiving performance of a narrowband communication system is improved.

Description

Technical Field

[0001] This invention relates to the field of narrowband communication technology, and in particular to a narrowband communication method and apparatus that integrates multiple satellites. Background Technology

[0002] Satellite networks at different orbital altitudes, such as LEO, MEO, and GEO, can provide global coverage and highly reliable communication services. However, due to the significant differences in the distances between satellites and ground stations—LEO satellites orbit at altitudes of approximately 500-2000 km, MEO satellites at approximately 2000-35786 km, and GEO satellites at approximately 35786 km—this substantial difference in orbital altitude leads to significant variations in signal transmission delays. Traditional methods often use a uniform delay calculation model to process all satellite signals, failing to optimize for the specific propagation characteristics of satellites at different orbital altitudes. This is particularly problematic in narrowband communication scenarios, where even small deviations in carrier phase can severely impact signal coherence combining. Furthermore, current technologies lack the ability to dynamically track the delay status of multi-satellite systems, making it impossible to adapt to real-time delay changes caused by satellite orbital motion. Summary of the Invention

[0003] This invention provides a narrowband communication method and apparatus for multi-satellite fusion. This invention can adapt to the time delay changes caused by satellite orbital motion in real time, maximize signal gain through coherent superposition, and improve the receiving performance of narrowband communication systems.

[0004] In a first aspect, the present invention provides a narrowband communication method based on multi-satellite fusion, the narrowband communication method based on multi-satellite fusion comprising: The transmission time matching value of each satellite is calculated based on the orbital parameters of LEO, MEO and GEO satellites. Based on the transmission time matching value, a delay difference measurement matrix for multiple satellites is constructed, and delay compensation is performed on the narrowband communication received signal to obtain a delay-compensated received signal. Multi-satellite delay state estimation is performed on the delay-compensated received signal to obtain a real-time delay estimate; Based on the real-time delay estimate, narrowband carrier phase compensation is performed on the delay-compensated received signal to obtain a fused narrowband communication signal.

[0005] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the calculation of the transmission time matching value of each satellite based on the LEO satellite orbit parameters, MEO satellite orbit parameters, and GEO satellite orbit parameters includes: The orbital geometry parameter sets of LEO satellite orbital parameters, MEO satellite orbital parameters, and GEO satellite orbital parameters are analyzed separately to obtain the sets of orbital geometric parameters. Geometric propagation path calculation is performed on the set of orbital geometric parameters to obtain the basic propagation time; Based on the satellite elevation angle and narrowband carrier frequency in the orbital geometry parameter set, ionospheric delay correction and tropospheric delay correction are performed to obtain the atmospheric propagation correction time; The transmission time matching value of each satellite is obtained by superimposing the base propagation time with the atmospheric propagation correction time.

[0006] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the step of performing ionospheric delay correction and tropospheric delay correction based on the satellite elevation angle and narrowband carrier frequency in the orbital geometry parameter set to obtain the atmospheric propagation correction time includes: The ionospheric electron density is obtained by querying the vertical total electron content database based on the satellite elevation angle in the orbital geometry parameter set. The ionospheric propagation delay is obtained by performing ionospheric delay calculations on the ionospheric electron density, the narrowband carrier frequency and the satellite elevation angle in the orbital geometry parameter set. Based on the satellite elevation angle, the surface atmospheric pressure and geographical latitude are obtained, and tropospheric delay calculation is performed to obtain the tropospheric propagation delay. The atmospheric propagation correction time is obtained by summing the ionospheric propagation delay and the tropospheric propagation delay.

[0007] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the step of constructing a multi-satellite delay difference metric matrix based on the transmission time matching value and performing delay compensation on the narrowband communication received signal to obtain a delay-compensated received signal includes: The number of visible satellites is counted based on the transmission time matching values ​​of each satellite to obtain the matrix dimension parameters; Calculate the absolute time delay difference between any two satellites based on the transmission time matching value of each satellite to obtain the set of satellite pair time delay differences; The time delay difference matrix is ​​obtained by filling the set of satellite pair time delay differences with matrix elements according to the matrix dimension parameters. A symmetry check and diagonal zero setting are performed on the time delay difference matrix to obtain a time delay difference measurement matrix for multiple satellites; Based on the aforementioned delay difference metric matrix, delay compensation is performed on the narrowband communication received signal to obtain a delay-compensated received signal.

[0008] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present invention, the step of performing time delay compensation on the narrowband communication received signal based on the time delay difference metric matrix to obtain a time delay compensated received signal includes: Generate a filter parameter set based on the maximum delay difference value and the signal-to-noise ratio parameter in the delay difference metric matrix; The filter parameter group is configured according to the narrowband communication characteristics to construct a delay compensation filter group; The narrowband communication received signal is input into the time delay compensation filter bank for channel-by-channel time delay correction processing to obtain the corrected signal; The time delay alignment verification and residual error elimination operations are performed on the corrected signal to obtain the time delay compensated received signal.

[0009] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, the step of performing multi-satellite delay state estimation on the delay-compensated received signal to obtain a real-time delay estimate includes: The delay observation data is extracted from the delay-compensated received signal to obtain a multi-satellite delay observation vector; Construct a time delay state vector containing the absolute time delay and the rate of change of time delay for each satellite based on the multi-satellite time delay observation vector; The time delay state vector is input into the Kalman filter tracker for state prediction and observation update calculation to obtain the filtered state vector. The time delay component is extracted from the filtered state vector to obtain the real-time time delay estimate.

[0010] In conjunction with the first aspect, in the sixth implementation of the first aspect of the present invention, the step of inputting the time-delay state vector into a Kalman filter tracker for state prediction and observation update calculation to obtain a filtered state vector, and performing time-delay component extraction on the filtered state vector to obtain a real-time time-delay estimate, includes: The time-delay state vector is input into the Kalman filter tracker and matrix multiplication is performed between the time-delay state vector and the state transition matrix to obtain a one-step prediction vector. Calculate the Kalman gain factor based on the covariance matrix and observation matrix of the predicted vector in the first step; Based on the Kalman gain factor, an observation correction calculation is performed on the one-step prediction vector to obtain the filtered state vector; The real-time delay estimates of each satellite are extracted from the filtered state vector.

[0011] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, the step of performing narrowband carrier phase compensation on the delay-compensated received signal based on the real-time delay estimate to obtain a fused narrowband communication signal includes: The real-time delay estimate is multiplied by the narrowband carrier frequency and converted into the phase deviation of each satellite carrier. A phase compensation processor, comprising a digital phase compensator and a phase tracking loop, is constructed based on the phase deviation of each satellite carrier. The delay compensation received signal is input into the phase compensation processor for carrier phase correction to obtain a carrier phase correction signal; The carrier phase correction signal is subjected to residual phase error elimination and signal alignment verification to obtain a phase-aligned signal; Based on the phase alignment signal, multi-satellite signals are coherently combined to obtain a fused narrowband communication signal.

[0012] In conjunction with the first aspect, in the eighth implementation of the first aspect of the present invention, the step of coherently combining multiple satellite signals based on the phase alignment signal to obtain a fused narrowband communication signal includes: Calculate the channel weight coefficient set based on the signal-to-noise ratio parameters of each satellite channel; The phase alignment signal is multiplied by the channel weight coefficient group to obtain the weighted phase alignment signal. Perform a multi-channel in-phase superposition operation on the weighted phase-aligned signal to obtain a merged signal; The merged signal is input into a narrowband filter for spectrum shaping and noise suppression to obtain a fused narrowband communication signal.

[0013] Secondly, the present invention provides a narrowband communication device for multi-satellite fusion, the narrowband communication device for multi-satellite fusion comprising: The calculation module is used to calculate the transmission time matching value of each satellite based on the LEO satellite orbit parameters, MEO satellite orbit parameters and GEO satellite orbit parameters; The delay compensation module is used to construct a delay difference measurement matrix for multiple satellites based on the transmission time matching value, and to perform delay compensation on the narrowband communication received signal to obtain a delay-compensated received signal. A multi-satellite delay state estimation module is used to perform multi-satellite delay state estimation on the delay compensation received signal to obtain a real-time delay estimate. The narrowband carrier phase compensation module is used to perform narrowband carrier phase compensation on the delay-compensated received signal based on the real-time delay estimate to obtain a fused narrowband communication signal.

[0014] The technical solution provided by this invention establishes dedicated transmission time matching functions for satellites in different orbits (LEO, MEO, and GEO), comprehensively considering the combined effects of geometric propagation delay, ionospheric delay, and tropospheric delay. Compared with existing unified delay models, it can more accurately calculate the actual transmission delay of each satellite, construct a multi-satellite delay difference metric matrix, and comprehensively quantify the delay difference between any pair of satellites through matrix processing. Compared with traditional simple delay difference calculation methods, it can systematically describe the overall delay distribution characteristics of the multi-satellite system. The adaptive filter bank designed based on the delay difference metric matrix can dynamically adjust the compensation parameters according to real-time channel conditions, and has stronger environmental adaptability than fixed compensation algorithms. The Kalman filter algorithm is used to dynamically estimate the delay state of multiple satellites, transforming the delay tracking problem into a multi-dimensional state estimation problem. Compared with simple filtering methods, it has stronger noise resistance and tracking accuracy, and can adapt to delay changes caused by satellite orbital motion in real time. By using real-time delay estimation for narrowband carrier phase compensation, combined with a digital phase compensator and phase tracking loop, carrier phase deviation caused by transmission delay can be eliminated, ensuring that multiple satellite signals have the same carrier phase at the receiver. Employing a maximum ratio combining algorithm and dynamically allocating weight coefficients based on the channel quality of each satellite, compared to selective combining or equal gain combining methods, it can make fuller use of the energy of multiple satellite signals. By coherently superimposing, it maximizes the signal gain and significantly improves the receiving performance of narrowband communication systems. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating the steps of a narrowband communication method for multi-satellite fusion in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a narrowband communication device with multi-satellite fusion in an embodiment of the present invention. Detailed Implementation

[0017] This invention provides a narrowband communication method and apparatus for multi-satellite fusion. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the narrowband communication method for multi-satellite fusion in this invention includes: Step S1: Calculate the transmission time matching value of each satellite based on the LEO satellite orbit parameters, MEO satellite orbit parameters, and GEO satellite orbit parameters; Specifically, the orbital parameters of satellites from different orbital types are standardized and analyzed to extract a set of orbital geometric parameters, including orbital altitude, geocentric angle, satellite elevation angle, pole position, and ground observation point coordinates. This set of parameters is then mapped to the corresponding orbital models for each satellite type, obtaining the instantaneous geometric propagation path between each satellite and the ground observation point. Based on these geometric parameters, the straight-line propagation distance from the transmitted signal of each satellite to the ground receiving point is calculated. A division operation is then performed using the speed of light constant to obtain the corresponding fundamental propagation time. This fundamental propagation time only considers the geometric distance and does not include electromagnetic wave propagation disturbances in the atmosphere. Based on the satellite elevation angle and the preset narrowband carrier frequency from the orbital geometric parameter set, the ionospheric delay and tropospheric delay are calculated using the Klobuchar and Saastamoinen models, respectively. The sum of these two atmospheric propagation delays is defined as the atmospheric propagation correction time. The atmospheric propagation correction time differs among LEO, MEO, and GEO satellites, therefore requiring independent modeling for each satellite. The basic propagation time is added to the corresponding atmospheric propagation correction time to obtain the transmission time matching value of each satellite at the current observation time. The transmission time matching value takes into account the satellite orbital position, electromagnetic propagation path and multi-source delay factors of the ionosphere and troposphere, and has the ability to dynamically track and correct with high precision.

[0019] Step S2: Construct a delay difference measurement matrix for multiple satellites based on the transmission time matching value, and perform delay compensation on the narrowband communication received signal to obtain the delay-compensated received signal; Specifically, all visible satellites currently participating in the fusion calculation are traversed and statistically analyzed to determine the actual number N available for reception. This number is then used as the dimension parameter of the delay difference matrix, initializing an N×N matrix space. Based on the calculated transmission time matching value for each satellite, all possible satellite pair combinations are constructed using a double-loop approach. For each pair of satellites, the delay difference is calculated separately, using the absolute value of the difference between the transmission time matching values ​​of the two satellites as the delay difference index for the current combination, forming a matrix containing N (N... The set of satellite pair delay differences with 1 / 2 elements. The set of satellite pair delay differences is then used to fill the matrix elements according to the row and column position mapping method defined by the matrix dimension parameters. Each matrix element d... ij This represents the delay difference between the i-th and j-th satellites, while performing a symmetry check to ensure that the matrix satisfies d. ij = d ji The properties, and all diagonal position elements d ii A unified value of zero is set to indicate that there is no delay difference, forming a multi-satellite delay difference measurement matrix that meets the mathematical definition requirements. Based on the precise delay difference between each channel reflected in the matrix, a preset adaptive compensation function is used to digitally adjust the narrowband communication received signals from each satellite. The compensation process includes dynamic adjustment of delay lines, interpolation processing, and channel alignment operations to ensure that all signals achieve coherent superposition under a unified time reference, resulting in a delay-compensated received signal.

[0020] Step S3: Perform multi-satellite delay state estimation on the delay compensation received signal to obtain the real-time delay estimate; Specifically, a structured time-domain analysis is performed on the received signal after delay compensation. Synchronization pilots, reference symbols, or zero-crossing points in the signal frame structure are used as extraction anchors. The delay observation values ​​of each satellite at the current moment are extracted from multiple channels using a sliding window function and correlation calculation methods. These are combined to form a delay observation vector containing all visible satellite channels. The elements of the delay observation vector correspond to the actual arrival delay of the signal from each satellite. To simultaneously capture the static delay level and the dynamic change trend caused by orbital motion, a two-dimensional extended delay state vector is constructed, containing the absolute transmission delay of each satellite and the corresponding delay change rate. This is represented as a first-order state vector of length 2N, describing the transmission delay state evolution information of the multi-satellite system at a given moment. The delay state vector is used as the input variable to the Kalman filter tracker. Combining the established state transition equation and observation equation, state prediction and observation update operations are sequentially performed within continuous time steps. The state prediction part calculates the current value based on the previous state vector and the theoretical orbit change rate. The observation update part uses the current observation vector to correct the deviation of the predicted value and generate the optimal estimated filtered state vector. The absolute time delay component in the filtered state vector is extracted by vector slicing to obtain the real-time time delay estimate corresponding to the current moment.

[0021] Step S4: Perform narrowband carrier phase compensation on the delay-compensated received signal based on the real-time delay estimate to obtain the fused narrowband communication signal.

[0022] Specifically, the real-time delay estimates of each satellite are element-wise multiplied with the set narrowband carrier frequency, and then multiplied by a constant factor of 2π to map the propagation time deviation into the corresponding carrier phase deviation, resulting in a set of phase deviation vectors describing the current carrier phase offset of each satellite. A phase compensation processor is constructed based on these phase deviation vectors. The phase compensation processor includes a digital phase compensation module for performing static phase correction and a digital phase-locked loop (PLL) for dynamically adjusting and eliminating phase drift. The PLL includes submodules such as a phase error detector, a proportional-integral loop filter, and a numerically controlled oscillator, used for continuous estimation and dynamic feedback compensation of minute phase disturbances in the signal. The received signals, after delay compensation, are input into the phase compensation processor. The received signals are rotated in the complex signal domain, causing the complex signals in each channel to undergo reverse phase rotation according to the corresponding phase deviation, resulting in the carrier phase correction signal. Residual phase error elimination is performed on the carrier phase correction signal, and fine-tuning is carried out in conjunction with the compensated phase output from the phase-locked loop. In-frequency alignment verification is performed on each channel signal, including symbol boundary alignment, phase jump detection, and stability testing, ensuring that all satellite signals are fully aligned in both the time and phase domains, outputting a phase-aligned signal with a unified phase reference. The phase-aligned signals from each channel are then weighted and merged according to the maximum ratio merging criterion. The merging weight is determined based on the real-time signal-to-noise ratio of each satellite signal; a higher weight indicates stronger channel reliability. Optimal signal fusion is achieved under heterogeneous channel quality conditions, outputting a fused narrowband communication signal.

[0023] In one specific embodiment, the process of performing step S1 may specifically include the following steps: The orbital geometry parameter sets of LEO satellite orbital parameters, MEO satellite orbital parameters, and GEO satellite orbital parameters are analyzed separately to obtain the sets of orbital geometric parameters. Geometric propagation path calculation is performed on the orbital geometry parameter set to obtain the basic propagation time; Ionospheric delay correction and tropospheric delay correction are performed based on the satellite elevation angle and narrowband carrier frequency in the orbital geometry parameter set to obtain the atmospheric propagation correction time; The transmission time matching value for each satellite is obtained by superimposing the base propagation time with the atmospheric propagation correction time.

[0024] Specifically, a standardized solution process is performed on the received orbital parameter data of LEO, MEO, and GEO satellites. This includes structured parsing of TLE orbital elements or high-precision orbital epoch data, extracting orbital geometric elements including orbital altitude, orbital inclination, right ascension of the ascending node, argument of perigee, mean perigee, and time stamps. Combined with the geographic coordinates of the ground station, the instantaneous geometric positional relationship between the satellite and the ground at the current moment is calculated. Parameters such as satellite elevation angle, geocentric angle, and surface projection distance, which describe the direction and distance between the satellite position and the observation point, are derived to form a set of orbital geometric parameters. Using the spatial distance data already calculated from the orbital geometry parameter set, vector geometric analysis is performed in conjunction with the Earth's radius and the satellite's orbital radius. The actual propagation path length from the satellite to the ground observation point is obtained using the cosine theorem or a vector space model. Based on the speed of light in free space, i.e., the constant speed of light in a vacuum, the path length is divided by the speed of light to obtain the basic propagation time of the signal under the condition of not considering atmospheric effects. This time serves as the first part of the theoretical transmission delay. To ensure modeling accuracy, the geometric propagation path of each satellite is calculated independently. In particular, for LEO satellites, the path change rate is greater than that of MEO and GEO satellites, and the propagation delay varies more drastically. Therefore, high-frequency refresh calculation is required. Based on this, according to the satellite elevation angle and carrier operating frequency in the orbital geometry parameter set, the Klobuchar model and the Saastamoinen model are introduced to quantitatively correct the ionospheric delay and tropospheric delay, respectively. The ionospheric delay is mainly affected by the inverse relationship between the total electron content of the ionosphere and the square of the carrier frequency; the delay is greater at lower carrier elevation angles. The correction calculation needs to consider the location of the ionospheric penetration point and the magnetic field correlation coefficient. The tropospheric delay is related to ground pressure, temperature, humidity, and the observation elevation angle. The Saastamoinen model converts the influence of actual tropospheric refraction on the propagation path into time delay units through elevation angle projection and latitude-related correction factors. The sum of these two constitutes the atmospheric propagation correction time, which is the non-geometric delay disturbance generated during the signal's passage through the Earth's atmosphere. The basic propagation time and the atmospheric propagation correction time are numerically superimposed to form a complete transmission time estimate that comprehensively considers the orbital spatial path and atmospheric propagation behavior; this is the transmission time matching value for each satellite at the current moment.

[0025] The process includes, after obtaining the transmission time matching values ​​for each satellite, the following steps: performing error analysis between the transmission time matching values ​​and actual observation delays for each satellite, establishing a transmission time matching accuracy assessment model, and obtaining matching accuracy assessment results; constructing an adaptive learning algorithm based on the matching accuracy assessment results and historical atmospheric parameter change data, and optimizing and adjusting the ionospheric delay correction coefficients and tropospheric delay correction coefficients online to obtain optimized correction coefficients; inputting the optimized correction coefficients into a neural network prediction model to calculate atmospheric delay predictions for future periods, and obtaining predicted atmospheric delay values; performing forward-looking adjustments and compensations on the parameters of the transmission time matching function based on the predicted atmospheric delay values ​​to obtain an adaptive transmission time matching function; recalculating the transmission time matching values ​​for each satellite using the adaptive transmission time matching function, and fusing them with the original calculation results to obtain accurate transmission time matching values.

[0026] In one specific embodiment, the process of performing ionospheric delay correction and tropospheric delay correction based on the satellite elevation angle and narrowband carrier frequency in the orbital geometry parameter set to obtain the atmospheric propagation correction time can specifically include the following steps: The ionospheric electron density is obtained by querying the vertical total electron content database based on the satellite elevation angle in the orbital geometry parameter set; The ionospheric propagation delay is obtained by performing ionospheric delay calculations using the narrowband carrier frequency and satellite elevation angle of the orbital geometry parameter set, based on the ionospheric electron density. The tropospheric propagation delay is obtained by acquiring the surface atmospheric pressure and geographic latitude based on the satellite elevation angle and performing tropospheric delay calculation. The atmospheric propagation correction time is obtained by summing the ionospheric propagation delay and the tropospheric propagation delay.

[0027] Specifically, the observation elevation angle parameters corresponding to each satellite are extracted from the orbital geometry parameter set, and the observation elevation angle values ​​are used as the primary key to input the ionospheric model's pre-query process. This involves accessing a grid-structured Total Vertical Electron Content (TEC) database. This database is built based on real-time GNSS observation data, ground-based stations, or static models such as the International Reference Ionospheric Model (IRI), and includes a three-dimensional index structure of latitude, longitude, and time. Interpolation at the ionospheric penetration point determines the vertical TEC value corresponding to each observation elevation angle, thus obtaining the ionospheric electron density. Based on the ionospheric electron density, combined with the carrier frequency parameters from the orbital geometry parameter set, the frequency values ​​are substituted into the inverse square structure calculation formula in the Klobuchar ionospheric model or its extended models. This is combined with the projection path coefficient of the satellite signal during ionospheric traversal. The projection path coefficient is inversely proportional to the satellite elevation angle and is represented using 1 / cos(θ) or its improved function. The vertical TEC value is then projected as the total electron content along the actual traversal path, completing the calculation of the ionospheric propagation delay. Based on the same satellite elevation angle parameters, the current surface atmospheric pressure value of the observation point's area is queried through a meteorological data interface or atmospheric model library, provided in Pa. Combined with the geographical latitude data of the observation point, the tropospheric propagation delay is calculated by calling the Saastamoinen tropospheric model. The Saastamoinen tropospheric model considers the superposition effect of the dry and wet components of the troposphere. The dry component mainly depends on atmospheric pressure and the observation elevation angle, while the wet component is limited by temperature and water vapor pressure. In the simplified calculation, standardized empirical values ​​are used to process the two delays, and the tropospheric propagation delay under the current conditions is summed to obtain the tropospheric propagation delay. The ionospheric propagation delay and the tropospheric propagation delay are summed to form the atmospheric propagation correction time.

[0028] In one specific embodiment, the process of performing step S2 may specifically include the following steps: The number of visible satellites is counted based on the transmission time matching values ​​of each satellite to obtain the matrix dimension parameters; The absolute time delay difference between any two satellites is calculated based on the transmission time matching value of each satellite, thus obtaining the set of satellite pair time delay differences; The time delay difference matrix is ​​obtained by filling the set of satellite pair time delay differences with matrix elements according to the matrix dimension parameters. A symmetry check and diagonal zeroing are performed on the delay difference matrix to obtain a delay difference metric matrix for multiple satellites; Delay compensation is performed on the narrowband communication received signal based on the delay difference metric matrix to obtain the delay-compensated received signal.

[0029] Specifically, the reception status of all visible satellites at the current observation time is statistically identified. This identification is based on indicators such as signal-to-noise ratio threshold, reception channel effectiveness, and zenith angle obstruction. Satellite channels in effective communication status are filtered and counted, and the total number of visible satellites N is used as the dimension parameter of the time delay matrix. A list of transmission time matching values ​​obtained through previous modeling is invoked, and a double-loop iterative operation is performed. For any two visible satellites numbered i and j, the absolute time delay difference between them is calculated, expressed as |T|. i T j The time propagation difference between each pair of satellites is obtained in the form of |. This operation requires eliminating redundant combinations of i = j and symmetrically preserving a copy of the difference for each pair of combinations. All the time delay differences between the formed satellite pairs are aggregated into a set of size N (N... 1) / 2, Based on the statistically obtained dimension parameter N, an N×N two-dimensional matrix structure is initialized in memory. The difference data from the above set is mapped and filled into the matrix elements according to the corresponding satellite index positions. The element value in the i-th row and j-th column is completely consistent with that in the j-th row and i-th column to maintain symmetry. After the matrix is ​​filled, the main diagonal elements are explicitly set to zero to indicate that there is no delay difference between themselves. After the matrix construction is completed, a delay difference metric matrix is ​​formed to characterize the consistency of the propagation time distribution of the multi-satellite system at the current moment, reflecting the propagation time structure of multi-source heterogeneous signals before reaching the receiver. Delay compensation for narrowband communication received signals is performed based on a delay difference metric matrix. Each difference value in the matrix is ​​aligned and matched with the received signal of each channel. According to a defined primary reference satellite or average time base, a delay compensation structure is configured for each received signal branch. This delay structure is constructed using digital delay lines, FIR filters, or programmable buffer loops, and the delay offset is dynamically set based on the matrix values. By performing precise shift operations on each signal path at the sampling period level, all satellite signals achieve consistent alignment on the time axis. All compensated received signals have a synchronized time reference.

[0030] The process includes, after obtaining the delay difference metric matrix for multiple satellites, the following steps: Electromagnetic environment adaptability assessment of the current number of visible satellites and signal quality; when the number of visible satellites is below a preset threshold, an auxiliary information fusion mode is activated to obtain the environmental adaptability assessment results; based on the environmental adaptability assessment results, local clock reference, channel quality monitoring data, and historical delay statistics are used as virtual satellite signal sources to obtain auxiliary delay reference data; the auxiliary delay reference data is jointly processed with the delay difference metric matrix for multiple satellites, and the problem of insufficient observability of delay observation is solved through atomic clock synchronization correction and delay reference calibration to obtain an enhanced delay difference metric matrix; based on the enhanced delay difference metric matrix, delay estimation observability and filter stability evaluation indicators are calculated to obtain delay estimation quality evaluation parameters; based on the delay estimation quality evaluation parameters, adaptive weight adjustment and outlier data removal are performed on the enhanced delay difference metric matrix to finally obtain the delay difference metric matrix for multiple satellites.

[0031] In one specific embodiment, the process of performing time delay compensation on the narrowband communication received signal based on the time delay difference metric matrix to obtain the time delay compensated received signal may specifically include the following steps: Generate a filter parameter set based on the maximum delay difference value and the signal-to-noise ratio parameter in the delay difference metric matrix; Configure the filter parameter group according to the narrowband communication characteristics to construct a delay compensation filter group; The narrowband communication received signal is input into a time delay compensation filter bank and subjected to channel-by-channel time delay correction processing to obtain the corrected signal; The time delay alignment verification and residual error elimination operations are performed on the corrected signal to obtain the time delay compensated received signal.

[0032] Specifically, all off-diagonal elements are extracted from the delay difference metric matrix and a maximum value search operation is performed to obtain the maximum absolute delay difference between currently visible satellites, representing the most severe propagation delay inconsistency level in the entire system, which serves as the delay boundary basis for the design of the delay compensation filter bank. At the same time, the signal-to-noise ratio (SNR) of each satellite's receiving channel at the current moment is calculated, and a channel SNR vector is constructed as a reference for calculating the filter weight attenuation coefficient, adaptive learning rate, and forgetting factor. Based on the maximum absolute time delay difference, the maximum delay step count of the digital filter is determined. This maximum delay step count is quantized into an integer delay index in units of sampling periods. Combined with the bandwidth characteristics and sampling rate parameters of the narrowband communication system, the filter order, window function structure, sampling accuracy, and phase response are configured to form a filter parameter set suitable for dynamic time delay compensation. This parameter set includes the quantization delay index for each filter order, as well as variable weight functions and adaptive adjustment factors for each channel. During the initialization phase, the filter's memory length and stability weights are dynamically allocated based on the signal-to-noise ratio (SNR) of each channel. This ensures that high SNR channels maintain strong time delay tracking inertia, while assigning higher sensitivity to low SNR channels for faster convergence. The received signals from the multi-channel narrowband communication are input to their respective filter branches. Each filter performs sampling-level delay shifting, weighted superposition, and state feedback operations on the signal according to the time delay compensation requirements of its satellite. The filter output is the intermediate signal that completes the initial time delay correction. The error correction module inputs the processed signal to the error correction module. By cross-comparing it with the global time reference or main reference channel signal, it analyzes the alignment deviation in the time domain and calculates the residual delay error. Through error feedback, it iteratively updates the filter weights, delay index, or state memory unit. While ensuring delay stability, it gradually converges the residual error, eliminates secondary delay disturbances caused by quantization jitter, irregular sampling, or sudden changes in elevation angle, and outputs the received signal group after all delay compensations are completed.

[0033] The process involves inputting the narrowband communication received signal into a delay compensation filter bank for channel-by-channel delay correction to obtain a corrected signal. This includes: constructing an adaptive sample-and-hold compensation unit based on the signal-to-interference-to-noise ratio and signal interruption probability of each satellite channel to obtain an anti-interruption delay compensator; grouping the narrowband communication received signal according to satellite orbit type and inputting it into the anti-interruption delay compensator for differentiated sampling to obtain grouped sampled signals; generating virtual delay correction data for signal interruption times based on an alternating prediction compensation algorithm to obtain a prediction compensation signal; performing alternating fusion and iterative correction operations on the grouped sampled signals and the prediction compensation signals to obtain a continuous correction signal; and performing integrity verification and abnormal signal removal processing on the continuous correction signal to obtain the corrected signal.

[0034] The process, after obtaining the delay-compensated received signal, includes: quantifying the compensation effect of the delay-compensated received signal, constructing a comprehensive evaluation function that includes delay consistency index, signal-to-noise ratio improvement degree, and phase stability, and obtaining the compensation effect evaluation result; establishing a deep Q-network model based on the compensation effect evaluation result, using the delay difference matrix, signal-to-noise ratio vector, and Doppler frequency shift vector as state space inputs to obtain a reinforcement learning state vector; calculating the optimal delay compensation action strategy through the deep Q-network based on the reinforcement learning state vector, outputting the adjustment amount of compensation parameters for each satellite channel, and obtaining an intelligent compensation strategy; applying the intelligent compensation strategy to the real-time adjustment of the delay compensation filter bank parameters, and establishing a reward function for strategy effect feedback to obtain optimized compensation parameters; and using the optimized compensation parameters to perform secondary fine-tuning compensation processing on the delay-compensated received signal to obtain the reinforcement learning-optimized delay-compensated received signal.

[0035] In one specific embodiment, the process of performing step S3 may specifically include the following steps: Time delay observation data is extracted from the time delay compensated received signal to obtain multi-satellite time delay observation vectors; Construct a time delay state vector containing the absolute time delay and the rate of change of time delay for each satellite based on the multi-satellite time delay observation vector; The time delay state vector is input into the Kalman filter tracker for state prediction and observation update calculation to obtain the filtered state vector. The time delay component is extracted from the filtered state vector to obtain the real-time time delay estimate.

[0036] Specifically, using the time-delay-compensated multiple received signals as the input data source, feature point structures for identifying signal propagation characteristics are selected in the time domain, such as periodic pilot symbols, preset synchronization codes, or cyclic prefix boundaries. Propagation delay observations at the current moment are extracted from each satellite channel using methods such as sliding correlation, autocorrelation peak detection, or cross-correlation matching. These propagation delay observations characterize the residual arrival time offset of each signal with the sampling period as the smallest granularity, and are ensured to be independent of the compensated delay value throughout the extraction process to avoid error propagation or quantization mismatch. All extracted observations are organized into a multi-dimensional observation vector according to their corresponding satellite numbers, where each element represents the instantaneous propagation delay observation result of a satellite at the current moment—i.e., a multi-satellite time delay observation vector—and used as the observation input for the state estimation model. To construct the state vector X(t) for time-delay dynamic modeling, the current time delay value and the rate of change of time delay between two consecutive time steps are tracked simultaneously for each visible satellite. The current time delay value represents the absolute propagation delay, and the rate of change of time delay reflects the time delay derivative effect caused by orbital changes or propagation disturbances. The two together constitute a first-order state vector structure of length 2N, where N is the number of visible satellites at the current time. The time-delay state vector is used as the input variable of the Kalman filter tracker and enters the iterative process of state prediction and observation update. In the state prediction stage, the estimated state vector of the previous time step is propagated and calculated according to the defined state transition matrix. Combined with the orbital prediction introduction term and process noise covariance in the system model, the system dynamic simulation is performed to predict the prior state estimation result at the current time step. In the observation update stage, the time-delay observation vector obtained in the current step is correlated and compared with the predicted state through the observation matrix to generate the observation residual vector. The update gain is calculated according to the minimum mean square error criterion. The predicted state vector is weighted and corrected by gain adjustment to complete the optimization of the posterior state estimation. The optimization result is the filtered state vector at the current moment, where the first half is the absolute propagation delay value of each satellite after filtering, and the second half is the estimated value of its corresponding delay change rate. After completing the internal state update of the filter, a structural component extraction operation is performed on the output filtered state vector, retaining only the absolute propagation delay part corresponding to the first N dimensions, and organizing it into a real-time delay estimation vector according to the satellite number order.

[0037] In one specific embodiment, the process of inputting the time-delay state vector into the Kalman filter tracker for state prediction and observation update calculation to obtain the filtered state vector, and extracting the time-delay component from the filtered state vector to obtain the real-time time-delay estimate, can specifically include the following steps: The time-delay state vector is input into the Kalman filter tracker, and matrix multiplication is performed between the time-delay state vector and the state transition matrix to obtain the one-step prediction vector. The Kalman gain factor is calculated based on the covariance matrix of the one-step prediction vector and the observation matrix. The filtered state vector is obtained by performing observation correction calculation on the one-step prediction vector based on the Kalman gain factor. The real-time delay estimates of each satellite are extracted from the filtered state vector.

[0038] Specifically, the posterior state vector is used as input, combined with the state transition matrix defined in the system modeling phase, to perform forward prediction of the current system's dynamic process. The state transition matrix is ​​designed based on the assumption of linear variation in satellite propagation delay. Its structure consists of an upper half representing the product of an identity matrix and a time step, while the lower half represents the product of the delay change rate and its correlation coefficient. This form effectively captures the continuous evolution trend of propagation delay during orbital motion. By performing matrix multiplication on the previous moment's state vector and the state transition matrix, the current one-step prediction vector is obtained, representing a theoretical estimate of the system state without observational data updates. Simultaneously, prediction covariance is calculated based on the previous moment's state covariance matrix, the current state transition matrix, and the process noise covariance matrix, i.e., the propagation of prediction error is modeled, yielding the covariance matrix corresponding to the current one-step prediction state vector. Subsequently, the observation update phase begins, introducing the current multi-satellite delay observation vector, which consists of propagation time observations extracted from the actual received signals. The predicted state vector is linearly correlated with the observed vector using the observation matrix. The product of the predicted state covariance matrix and the observation matrix, along with the summation term of the observation noise covariance matrix, are calculated. This summation term, multiplied by the product of the predicted state covariance matrix and the observation matrix, forms the numerator, thus constructing the expression for the Kalman gain factor. The physical meaning of the Kalman gain lies in measuring the relative weight of observational information and model prediction information in state updates. Its calculation result is influenced by factors such as observation noise, process noise, and model structure. Based on the Kalman gain factor, an observation correction process is performed on the predicted state vector. The observation residual is obtained by calculating the difference between the current observation vector and the projection of the predicted state into the observation space. This residual is then multiplied by the Kalman gain for weighted updates, allowing the original predicted state vector to incorporate actual observation information while maintaining dynamic continuity, resulting in a filtered state vector. The first half of the filtered state vector, corresponding to the N-dimensional absolute time delay component, is extracted, representing the real-time time delay estimates of all visible satellites at the current moment.

[0039] In one specific embodiment, the process of performing step S4 may specifically include the following steps: The real-time delay estimate is multiplied by the narrowband carrier frequency and converted into the phase deviation of each satellite carrier. A phase compensation processor, including a digital phase compensator and a phase tracking loop, is constructed based on the phase deviation of each satellite carrier. The delay-compensated received signal is input into the phase compensation processor for carrier phase correction to obtain the carrier phase correction signal. The carrier phase correction signal is subjected to residual phase error elimination and signal alignment verification to obtain a phase-aligned signal; Multi-satellite signals are coherently combined based on phase-aligned signals to obtain fused narrowband communication signals.

[0040] Specifically, the real-time delay estimate of each satellite is used as an input variable, and element-wise multiplication is performed in conjunction with the preset narrowband carrier frequency parameters in the system. Simultaneously, the physical constant 2π is introduced to map the time-domain offset to the frequency-domain phase offset, resulting in a set of carrier phase deviations in radians, representing the rotation angle of the complex signal due to differences in the propagation paths of each satellite. A digital phase compensator structure is constructed based on the phase deviation of each satellite. This structure includes a phase modulation factor generation module, a complex exponential calculation module, and a feedback control interface, connected in parallel with a high-precision phase tracking loop to handle residual disturbances on the time scale. The phase tracking loop internally consists of a phase error detector, a proportional-integral filter, and a numerically controlled oscillator, providing real-time closed-loop control capability. All delay-compensated received signals are input to the phase compensation processor, where a sample-by-sample multiplication operation is performed between the complex signal and the corresponding complex exponential compensation factor in each channel, rotating the carrier phase from its offset state back to the theoretical center position, resulting in a pre-corrected carrier phase correction signal. A residual phase error detection mechanism is introduced based on the phase-corrected signal. The phase difference between the current corrected complex signal and the previous frame or reference carrier within its symbol period is used as the error input. A phase detector calculates the imaginary part of the difference, and a micro-amplitude compensation phase is formed through proportional-integral adjustment. This micro-compensation phase is fed back to the numerically controlled oscillator in the phase compensation channel in real time, enabling the system to have continuous self-correction capabilities. After the residual error is continuously suppressed to below the coherent combining threshold, alignment verification is performed on each channel signal. This involves checking whether symbol boundary synchronization, frequency offset residuals, and instantaneous phase jitter meet the combining conditions. If they are met, the signal is determined to have achieved phase alignment, resulting in a highly consistent phase-aligned signal. Multi-channel coherent combining is then performed on all phase-aligned signals. The combining method adopts the maximum ratio combining criterion. The combining weight coefficient is dynamically calculated based on the signal-to-noise ratio of each signal channel. Each satellite signal is multiplied by its corresponding weight and then superimposed in the complex domain to form a fused narrowband communication signal.

[0041] In one specific embodiment, the process of performing coherent combining of multi-satellite signals based on phase alignment signals to obtain fused narrowband communication signals can specifically include the following steps: Calculate the channel weight coefficient set based on the signal-to-noise ratio parameters of each satellite channel; The phase-aligned signal is multiplied by the channel weight coefficient group to obtain the weighted phase-aligned signal; Perform a multi-channel in-phase superposition operation on the weighted phase-aligned signal to obtain the merged signal; The merged signal is input into a narrowband filter for spectrum shaping and noise suppression to obtain a fused narrowband communication signal.

[0042] Specifically, the signal-to-noise ratio (SNR) parameter of each signal channel is input into the weight generation module, and a normalized weight coefficient set is constructed according to the maximum ratio combining criterion. The weight value of high SNR channels tends to be maximum, and the weight value of low SNR channels tends to be minimum, thereby effectively suppressing the negative impact of low-quality channels on the combining result, while avoiding nonlinear overreaction caused by drastic fluctuations in SNR values. The aligned satellite received signals and their corresponding weight coefficients are multiplied point-to-point using complex multiplication to form a weighted phase-aligned signal set. The weighted phase-aligned signals of all channels are superimposed in phase, and the complex weighted signals of each channel are summed at each sampling point on the time axis to obtain a new complex-form combined signal. The combined signal integrates the effective information of all channels in both complex amplitude and phase, and its overall power spectrum characteristics have a higher main lobe intensity and a lower noise floor compared to any single-channel signal. The merged signal is input into a narrowband filter, which is constructed based on low-pass or band-pass characteristics. Its center frequency should be aligned with the carrier frequency, and its bandwidth should match the actual bandwidth occupied by the narrowband system. A linear-phase FIR or minimum-phase IIR structure is employed to ensure the signal waveform remains undistorted. The narrowband filter suppresses the spectral spread caused by multi-channel amplitude mismatch or frequency drift in the merged signal and effectively isolates broadband noise energy outside the system's receiving bandwidth, thus achieving the dual goals of spectrum shaping and noise suppression. The output fused narrowband communication signal is a high-quality complex signal stream that is time-aligned, phase-consistent, amplitude-enhanced, spectrally concentrated, and sufficiently noise-suppressed.

[0043] The process, after obtaining the fused narrowband communication signal, includes: performing spectrum analysis and interference detection on the fused narrowband communication signal to identify the location of narrowband interference sources and the distribution of interference intensity, and obtaining interference feature vectors; constructing a dynamic power allocation matrix based on the interference feature vectors and the instantaneous signal-to-noise ratio of each satellite channel, calculating the optimal signal power allocation weights, and obtaining intelligent power allocation coefficients; performing adaptive power adjustment on the phase-aligned signal based on the intelligent power allocation coefficients, while applying a blind source separation algorithm to suppress co-channel interference, and obtaining an anti-interference processed signal; inputting the anti-interference processed signal into an adaptive notch filter bank for precise narrowband interference suppression, and eliminating residual noise through spectral subtraction, and obtaining a cleaned and merged signal; performing signal quality assessment and dynamic gain control on the cleaned and merged signal, and performing final power adjustment according to the target signal-to-noise ratio requirements, to obtain an intelligently optimized fused narrowband communication signal.

[0044] The narrowband communication method of multi-satellite fusion in the embodiments of the present invention has been described above. The narrowband communication device of multi-satellite fusion in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the narrowband communication device for multi-satellite fusion in this invention includes: The calculation module is used to calculate the transmission time matching value of each satellite based on the LEO satellite orbit parameters, MEO satellite orbit parameters and GEO satellite orbit parameters; The delay compensation module is used to construct a delay difference measurement matrix for multiple satellites based on the transmission time matching value, and to perform delay compensation on the narrowband communication received signal to obtain the delay-compensated received signal. The multi-satellite delay state estimation module is used to perform multi-satellite delay state estimation on the delay-compensated received signal to obtain a real-time delay estimate. The narrowband carrier phase compensation module is used to perform narrowband carrier phase compensation on the delay-compensated received signal based on the real-time delay estimate, so as to obtain the fused narrowband communication signal.

[0045] Through the collaborative efforts of the aforementioned components, and by establishing dedicated transmission time matching functions for satellites in different orbits (LEO, MEO, and GEO), this approach comprehensively considers the combined effects of geometric propagation delay, ionospheric delay, and tropospheric delay. Compared to existing unified delay models, it can more accurately calculate the actual transmission delay of each satellite. A multi-satellite delay difference metric matrix is ​​constructed, and the delay differences between any pair of satellites are comprehensively quantified through matrix processing. Compared to traditional simple delay difference calculation methods, this approach can systematically describe the overall delay distribution characteristics of a multi-satellite system. The adaptive filter bank designed based on the delay difference metric matrix can dynamically adjust compensation parameters according to real-time channel conditions, exhibiting stronger environmental adaptability compared to fixed compensation algorithms. The Kalman filter algorithm is used to dynamically estimate the delay state of multiple satellites, transforming the delay tracking problem into a multi-dimensional state estimation problem. Compared to simple filtering methods, this approach has stronger noise resistance and tracking accuracy, and can adapt in real-time to delay changes caused by satellite orbital motion. By using real-time delay estimation for narrowband carrier phase compensation, combined with a digital phase compensator and phase tracking loop, carrier phase deviation caused by transmission delay can be eliminated, ensuring that multiple satellite signals have the same carrier phase at the receiver. Employing a maximum ratio combining algorithm and dynamically allocating weight coefficients based on the channel quality of each satellite, compared to selective combining or equal gain combining methods, it can make fuller use of the energy of multiple satellite signals. By coherently superimposing, it maximizes the signal gain and significantly improves the receiving performance of narrowband communication systems.

[0046] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A narrowband communication method using multi-satellite fusion, characterized in that, include: The transmission time matching value of each satellite is calculated based on the orbital parameters of LEO, MEO and GEO satellites. Based on the transmission time matching value, a delay difference measurement matrix for multiple satellites is constructed, and delay compensation is performed on the narrowband communication received signal to obtain a delay-compensated received signal. Multi-satellite delay state estimation is performed on the delay-compensated received signal to obtain a real-time delay estimate; Based on the real-time delay estimate, narrowband carrier phase compensation is performed on the delay-compensated received signal to obtain a fused narrowband communication signal.

2. The narrowband communication method for multi-satellite fusion according to claim 1, characterized in that, The calculation of the transmission time matching value for each satellite based on LEO, MEO, and GEO satellite orbit parameters includes: The orbital geometry parameter sets of LEO satellite orbital parameters, MEO satellite orbital parameters, and GEO satellite orbital parameters are analyzed separately to obtain the sets of orbital geometric parameters. Geometric propagation path calculation is performed on the set of orbital geometric parameters to obtain the basic propagation time; Based on the satellite elevation angle and narrowband carrier frequency in the orbital geometry parameter set, ionospheric delay correction and tropospheric delay correction are performed to obtain the atmospheric propagation correction time; The transmission time matching value of each satellite is obtained by superimposing the base propagation time with the atmospheric propagation correction time.

3. The narrowband communication method for multi-satellite fusion according to claim 2, characterized in that, The atmospheric propagation correction time is obtained by performing ionospheric delay correction and tropospheric delay correction based on the satellite elevation angle and narrowband carrier frequency in the orbital geometry parameter set, including: The ionospheric electron density is obtained by querying the vertical total electron content database based on the satellite elevation angle in the orbital geometry parameter set. The ionospheric propagation delay is obtained by performing ionospheric delay calculations on the ionospheric electron density, the narrowband carrier frequency and the satellite elevation angle in the orbital geometry parameter set. Based on the satellite elevation angle, the surface atmospheric pressure and geographical latitude are obtained, and tropospheric delay calculation is performed to obtain the tropospheric propagation delay. The atmospheric propagation correction time is obtained by summing the ionospheric propagation delay and the tropospheric propagation delay.

4. The narrowband communication method for multi-satellite fusion according to claim 1, characterized in that, The step of constructing a multi-satellite delay difference metric matrix based on the transmission time matching value and performing delay compensation on the narrowband communication received signal to obtain a delay-compensated received signal includes: The number of visible satellites is counted based on the transmission time matching values ​​of each satellite to obtain the matrix dimension parameters; Calculate the absolute time delay difference between any two satellites based on the transmission time matching value of each satellite to obtain the set of satellite pair time delay differences; The time delay difference matrix is ​​obtained by filling the set of satellite pair time delay differences with matrix elements according to the matrix dimension parameters. A symmetry check and diagonal zero setting are performed on the time delay difference matrix to obtain a time delay difference measurement matrix for multiple satellites; Based on the aforementioned delay difference metric matrix, delay compensation is performed on the narrowband communication received signal to obtain a delay-compensated received signal.

5. The narrowband communication method for multi-satellite fusion according to claim 4, characterized in that, The step of performing time delay compensation on the narrowband communication received signal based on the time delay difference metric matrix to obtain a time delay compensated received signal includes: Generate a filter parameter set based on the maximum delay difference value and the signal-to-noise ratio parameter in the delay difference metric matrix; The filter parameter group is configured according to the narrowband communication characteristics to construct a delay compensation filter group; The narrowband communication received signal is input into the time delay compensation filter bank for channel-by-channel time delay correction processing to obtain the corrected signal; The time delay alignment verification and residual error elimination operations are performed on the corrected signal to obtain the time delay compensated received signal.

6. The narrowband communication method for multi-satellite fusion according to claim 1, characterized in that, The step of performing multi-satellite delay state estimation on the delay-compensated received signal to obtain a real-time delay estimate includes: The delay observation data is extracted from the delay-compensated received signal to obtain a multi-satellite delay observation vector; Construct a time delay state vector containing the absolute time delay and the rate of change of time delay for each satellite based on the multi-satellite time delay observation vector; The time delay state vector is input into the Kalman filter tracker for state prediction and observation update calculation to obtain the filtered state vector. The time delay component is extracted from the filtered state vector to obtain the real-time time delay estimate.

7. The narrowband communication method for multi-satellite fusion according to claim 6, characterized in that, The step of inputting the time-delay state vector into a Kalman filter tracker for state prediction and observation update calculations to obtain a filtered state vector, and then extracting the time-delay component from the filtered state vector to obtain a real-time time-delay estimate, includes: The time-delay state vector is input into the Kalman filter tracker and matrix multiplication is performed between the time-delay state vector and the state transition matrix to obtain a one-step prediction vector. Calculate the Kalman gain factor based on the covariance matrix and observation matrix of the predicted vector in the first step; Based on the Kalman gain factor, an observation correction calculation is performed on the one-step prediction vector to obtain the filtered state vector; The real-time delay estimates of each satellite are extracted from the filtered state vector.

8. The narrowband communication method for multi-satellite fusion according to claim 1, characterized in that, The step of performing narrowband carrier phase compensation on the delay-compensated received signal based on the real-time delay estimate to obtain a fused narrowband communication signal includes: The real-time delay estimate is multiplied by the narrowband carrier frequency and converted into the phase deviation of each satellite carrier. A phase compensation processor, comprising a digital phase compensator and a phase tracking loop, is constructed based on the phase deviation of each satellite carrier. The delay compensation received signal is input into the phase compensation processor for carrier phase correction to obtain a carrier phase correction signal; The carrier phase correction signal is subjected to residual phase error elimination and signal alignment verification to obtain a phase-aligned signal; Based on the phase alignment signal, multi-satellite signals are coherently combined to obtain a fused narrowband communication signal.

9. The narrowband communication method for multi-satellite fusion according to claim 8, characterized in that, The process of coherently combining multiple satellite signals based on the phase alignment signal to obtain a fused narrowband communication signal includes: Calculate the channel weight coefficient set based on the signal-to-noise ratio parameters of each satellite channel; The phase alignment signal is multiplied by the channel weight coefficient group to obtain the weighted phase alignment signal. Perform a multi-channel in-phase superposition operation on the weighted phase-aligned signal to obtain a merged signal; The merged signal is input into a narrowband filter for spectrum shaping and noise suppression to obtain a fused narrowband communication signal.

10. A narrowband communication device with multi-satellite fusion, characterized in that, A narrowband communication method for performing multi-satellite fusion as described in any one of claims 1-9, comprising: The calculation module is used to calculate the transmission time matching value of each satellite based on the LEO satellite orbit parameters, MEO satellite orbit parameters and GEO satellite orbit parameters; The delay compensation module is used to construct a delay difference measurement matrix for multiple satellites based on the transmission time matching value, and to perform delay compensation on the narrowband communication received signal to obtain a delay-compensated received signal. A multi-satellite delay state estimation module is used to perform multi-satellite delay state estimation on the delay compensation received signal to obtain a real-time delay estimate. The narrowband carrier phase compensation module is used to perform narrowband carrier phase compensation on the delay-compensated received signal based on the real-time delay estimate to obtain a fused narrowband communication signal.

Images