Multi-satellite network converged communication link optimization and anti-interference method

Abstract

The invention discloses a multi-satellite network converged communication link optimization and anti-interference method, which belongs to the technical field of wireless communication networks, and comprises the following steps: acquiring Doppler frequency shift parameters of satellite nodes, adjacent satellite radiation quantity parameters and preset geographical shielding coefficients, and generating a link dynamic quality coefficient; constructing a dynamic topological graph according to the link dynamic quality coefficient; performing radio frequency fingerprint extraction and frequency domain waveform phase reversal processing on the detected interference signal to generate an anti-interference compensation signal; and when the link dynamic quality coefficient is greater than or equal to a preset quality threshold value, selecting a target transmission path according to the dynamic topological graph, and injecting the anti-interference compensation signal into the target transmission path. According to the invention, a mechanism of cooperation of dynamic link quality evaluation, interference fingerprint feature matching and multi-path fragmentation transmission is adopted, and real-time optimization selection of communication paths, accurate suppression of interference signals and efficient distribution of network resources can be realized in a complex air and space environment.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication networks, and in particular to a multi-satellite network fusion communication link optimization and anti-interference method. Background Art

[0002] Satellite network communication technology achieves wide-area coverage through the coordinated networking of multiple satellites. However, its link performance is significantly affected by orbital dynamics, electromagnetic interference, and the atmospheric environment. Existing technologies primarily select transmission paths using a preset link priority table, combined with frequency hopping and spread spectrum technology to mitigate interference, while also relying on redundant coding to improve transmission reliability.

[0003] Current satellite network link optimization methods typically construct routing models based on static parameters, failing to fully account for time-varying factors such as real-time changes in Doppler frequency shift and the dynamic accumulation of interference from neighboring satellites. This results in delayed link quality assessment. Countermeasures against interference scenarios often employ passive frequency avoidance strategies, which waste channel resources and inadequate interference suppression accuracy. Data retransmission mechanisms in complex transmission environments are prone to triggering frequent fragment reassembly, increasing the risk of network congestion.

[0004] However, existing solutions in multi-satellite networks generally face technical deficiencies such as insufficient dynamic link adaptability, limited interference fingerprint recognition accuracy, and inefficient multipath coordination. Fixed-weight interference suppression algorithms struggle to adapt to new interference waveforms, while static topology models prevent path selection from adapting to network structure changes caused by high-speed satellite motion. Furthermore, resource allocation lacks the ability to jointly optimize node real-time load and energy status. Summary of the Invention

[0005] To solve the above problems, the present invention provides a multi-satellite network integrated communication link optimization and anti-interference method, which adopts a dynamic link quality assessment, interference fingerprint feature matching and multi-path fragmentation transmission coordination mechanism, and can realize real-time optimization selection of communication paths, precise suppression of interference signals and efficient allocation of network resources in complex aerospace environments.

[0006] The above objectives can be achieved through the following solutions:

[0007] A multi-satellite network converged communication link optimization and anti-interference method comprises obtaining Doppler frequency shift parameters of satellite nodes, radiation parameters of adjacent satellites, and a preset geographic shielding coefficient to generate a link dynamic quality coefficient; constructing a dynamic topology map based on the link dynamic quality coefficient; performing radio frequency fingerprint extraction and frequency domain waveform inversion processing on detected interference signals to generate an anti-interference compensation signal; and when the link dynamic quality coefficient is greater than or equal to a preset quality threshold, selecting a target transmission path based on the dynamic topology map and injecting the anti-interference compensation signal into the target transmission path.

[0008] Optionally, generating the dynamic quality coefficient of the link includes: obtaining the Doppler frequency shift parameter of the satellite node, the radiation parameter of the adjacent satellite and a preset geographic shielding coefficient; fusing the Doppler frequency shift parameter and the radiation parameter of the adjacent satellite to generate an initial link evaluation value; superimposing the geographic shielding coefficient and the preset rain attenuation correction coefficient to generate a link environment correction factor; and calculating the dynamic quality coefficient of the link based on the initial link evaluation value and the link environment correction factor.

[0009] Optionally, the method further includes: obtaining rainfall intensity parameters in real-time meteorological data; calculating a rain attenuation correction coefficient using the rainfall intensity parameters and a preset attenuation empirical coefficient; wherein the attenuation empirical coefficient dynamically switches with the satellite operating frequency band.

[0010] Optionally, the method also includes: when the link dynamic quality coefficient is less than the quality threshold, splitting the data to be transmitted into multiple data fragments; assigning a transmission timestamp containing a path delay difference to each data fragment; and selecting at least two paths for fragment transmission based on the dynamic topology map.

[0011] Optionally, the selection of at least two paths for fragmented transmission based on the dynamic topology map includes: obtaining satellite motion vector parameters and the actual arrival time of each data fragment at the receiving end; using the transmission timestamp of each data fragment and the actual arrival time to calculate the transmission time difference; using the transmission time difference and the satellite motion vector parameters to calculate the compensation value; correcting the actual arrival time according to the compensation value to obtain the theoretical arrival timing; and restoring the data to be transmitted according to the theoretical arrival timing.

[0012] Optionally, the radio frequency fingerprint extraction and frequency domain waveform inversion processing of the detected interference signal to generate an anti-interference compensation signal includes: performing radio frequency fingerprint extraction on the detected interference signal to obtain the interference center frequency; obtaining the frequency of the satellite operating frequency band to obtain the standard frequency; calculating the difference between the interference center frequency and the standard frequency to obtain a frequency error value; adjusting the phase offset of the inverted waveform according to the frequency error value and a preset adjustment weight matrix to generate an anti-interference compensation signal.

[0013] Optionally, the method also includes: obtaining a set of waveform characteristic parameters of historical interference signals and establishing a dynamic interference library; performing feature extraction on the detected interference signal to obtain a set of waveform characteristic parameters; performing similarity matching on the waveform characteristic parameter set with the waveform characteristic parameter sets of each historical interference signal in the preset dynamic interference library to calculate a similarity value set; when there is no similarity value in the similarity value set that is less than a preset similarity threshold, storing the waveform characteristic parameter set of the detected interference signal in the dynamic interference library; using the waveform characteristic parameter set of the detected interference signal to calculate a frequency-frequency fingerprint feature vector; using the frequency-frequency fingerprint feature vector to calculate an adjustment weight matrix; wherein, the adjustment weight matrix is ​​bound to the waveform characteristic parameter set of the corresponding interference signal and stored in the dynamic interference library; when there is a similarity value in the similarity value set that is less than a preset similarity threshold, selecting the corresponding adjustment weight matrix according to the size of the similarity.

[0014] Optionally, the method also includes: collecting computing load parameters of satellite nodes; when the computing load parameters exceed a preset threshold, obtaining the residual energy of adjacent satellite nodes; calculating the node priority of adjacent satellite nodes based on the link dynamic quality coefficient and the residual energy; and selecting a relay satellite node based on the size of the node priority.

[0015] Optionally, the method further includes: obtaining a relay forwarding path based on the dynamic topology map and the relay satellite nodes; calculating a delay change gradient based on the relay forwarding path and the satellite motion vector parameters; and correcting the compensation value using the delay change gradient.

[0016] Based on the same inventive concept, the present invention also provides a multi-satellite network converged communication link optimization and anti-interference system, which includes: a link perception module, which is used to obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellite and the preset geographic shielding coefficient, and generate a link dynamic quality coefficient; a spectrum generation module, which is used to construct a dynamic topology spectrum according to the link dynamic quality coefficient; a compensation calculation module, which is used to extract the radio frequency fingerprint and perform frequency domain waveform inversion processing on the detected interference signal to generate an anti-interference compensation signal; a compensation execution module, which is used to select a target transmission path according to the dynamic topology spectrum when the link dynamic quality coefficient is greater than or equal to a preset quality threshold, and inject the anti-interference compensation signal into the target transmission path.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention builds a dynamic link quality coefficient evaluation system by integrating multi-dimensional parameters such as Doppler frequency shift, adjacent interference radiation, and environmental shielding effects. Combined with the real-time update mechanism of the dynamic topology map, it significantly improves the accuracy and response speed of link screening in complex aerospace environments, ensuring the long-term stability of the communication path and its ability to resist environmental mutations.

[0019] 2. The present invention uses interference signal RF fingerprint feature extraction and frequency domain waveform inversion processing technology, combined with the adaptive matching mechanism of the dynamic interference library, to achieve precise interference suppression compensation, effectively solving the problem of poor adaptability of traditional anti-interference methods to new unknown interference, reducing the bit error rate and improving the system's robustness against malicious interference;

[0020] 3. Through the collaborative design of data fragmentation transmission and dynamic compensation of path delay differences, this invention achieves multi-path parallel transmission and fragmentation timing self-calibration when link quality fluctuates. This breaks through the capacity limitations of traditional single paths and improves the data transmission success rate and integrity in low-quality link scenarios. It is particularly suitable for the reliable transmission of high-priority task data.

[0021] 4. The present invention is based on a dynamic priority calculation model of node computing load and residual energy to achieve joint optimization of resource allocation and task scheduling, avoiding single-point overload failure while balancing the energy consumption of satellite nodes, extending the overall operating life of the network and ensuring the continuity of key services.

[0022] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 The present invention is a flowchart of a multi-satellite network integrated communication link optimization and anti-interference method according to an embodiment of the present invention.

[0025] Figure 2 2 is a schematic diagram of the reverse phase compensation effect of an embodiment of the present invention.

[0026] Figure 3 It is a structural diagram of a multi-satellite network fusion communication link optimization and anti-interference system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0028] Reference Figure 1 One embodiment of the present invention proposes a multi-satellite network integrated communication link optimization and anti-interference method, which adopts a dynamic link quality assessment, interference fingerprint feature matching and multi-path fragmentation transmission coordination mechanism to achieve real-time optimization selection of communication paths, precise suppression of interference signals and efficient allocation of network resources in complex aerospace environments.

[0029] The method of this embodiment specifically includes:

[0030] Obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellites and the preset geographical shielding coefficient to generate the dynamic quality coefficient of the link;

[0031] Constructing a dynamic topology map according to the link dynamic quality coefficient;

[0032] Specifically, the dynamic topology graph is expressed mathematically as a weighted adjacency matrix, where each satellite node corresponds to a row and column index in the matrix, and the matrix element value is equal to the dynamic link quality coefficient of the link between the corresponding nodes. The first step is to determine the node set. The orbital parameters of all participating satellites are obtained through the satellite tracking and control network. Candidate nodes within the maximum transmission range are screened, and the orbital altitude and azimuth data of each satellite are stored in the node attribute library. The second step is to calculate edge weights, traversing all possible node pairs. If a physical link exists between the two nodes, the real-time dynamic link quality coefficient of the link is used as the edge weight. If the dynamic link quality coefficient falls below the preset quality threshold, the corresponding matrix element is set to zero to indicate an unavailable edge. The third step is to dynamically update the graph, configuring a periodic trigger mechanism and an event-driven trigger mechanism. The periodic trigger recalculates all link quality coefficients and updates the adjacency matrix every ten seconds. The event-driven trigger immediately performs a local link update when the fluctuation of the dynamic link quality coefficient of any link exceeds five percent.

[0033] Perform RF fingerprint extraction and frequency domain waveform inversion processing on the detected interference signal to generate an anti-interference compensation signal;

[0034] When the link dynamic quality coefficient is greater than or equal to a preset quality threshold, a target transmission path is selected according to the dynamic topology map, and the anti-interference compensation signal is injected into the target transmission path.

[0035] Specifically, when the dynamic link quality coefficient is greater than or equal to a preset quality threshold, the two satellite nodes corresponding to the dynamic link quality coefficient are added to the candidate path set. Multiple transmission paths are generated based on the dynamic topology map and the satellite node combinations in the candidate path set. The product of the dynamic link quality coefficients of each transmission path is calculated, and the transmission path with the largest product is selected as the target transmission path. If multiple paths have the same product, the path with the smallest total inter-node physical distance is selected as the target transmission path. Finally, an anti-interference compensation signal is injected into the transmission link of the selected path via a radio frequency injection device. This is done by adjusting the modem's superimposed waveform generation module to embed an inverted compensation signal component in the baseband waveform.

[0036] A dynamic link quality assessment system is established through the fusion of multi-dimensional parameters, and intelligent path optimization and active anti-interference coordinated control are achieved based on graph theory models. First, the dynamic motion characteristics of satellite nodes, the interference effects of neighboring satellites, and the geographical shielding effect are converted into quantifiable link dynamic quality coefficients. The Doppler frequency shift parameter reflects the impact of relative motion on signal stability, the radiation parameter of neighboring satellites characterizes the intensity of co-frequency interference, and the geographical shielding coefficient describes the degree of signal attenuation caused by terrain obstruction. An adjacency matrix structure is constructed through a dynamic topological graph to screen valid links that meet the quality threshold. The path search algorithm is improved, replacing the traditional shortest path model with a link quality product maximization model to ensure the optimal overall performance of each link in the selected path. Finally, through an adaptive matching mechanism that modulates the compensation signal strength and link quality, precise control of interference waveform cancellation is achieved while ensuring the integrity of the main signal.

[0037] Optionally, generating a dynamic link quality coefficient includes:

[0038] Obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellites and the preset geographic shielding coefficient;

[0039] The Doppler frequency shift parameter is integrated with the radiation parameters of the adjacent satellite to generate the initial link evaluation value;

[0040] The geographical shielding coefficient is superimposed on a preset rain attenuation correction coefficient to generate a link environment correction factor;

[0041] A link dynamic quality coefficient is calculated based on the link initial evaluation value and the link environment correction factor.

[0042] Specifically, first, the relative speed between the current satellite and the ground terminal is measured by the satellite navigation receiver to obtain the Doppler frequency shift parameter Δf:

[0043]

[0044] where f c is the carrier frequency, v is the relative velocity, θ is the angle between the velocity vector and the line of sight, and c is the speed of light. The neighboring satellite radiation parameter I is collected using a spectrum analyzer to collect the neighboring satellite's transmission power density, and the quantized value is obtained after unit conversion. The geographic shielding coefficient G is obtained by geometric calculation based on the altitude and building shielding angle data extracted from the 3D geographic information database of the terminal's location, and its value range is 0-1. The Doppler shift parameter and the neighboring satellite radiation parameter are input into the weighting function to calculate the initial link assessment value X:

[0045]

[0046] Where α and β are preset weight constant coefficients, α+β=1, Δf max is the maximum allowable frequency shift value of the system, I max is the maximum allowable radiation intensity, and ε is the minimum value anti-zero factor. The link environment correction factor Y is calculated by weighting the geographical shielding factor G and the rain attenuation correction factor R, for example, it can be:

[0047] Y=0.7G+0.3R,

[0048] The rain attenuation correction factor is obtained by consulting the rain attenuation model table in ITU-R Recommendation P.618 based on the current operating frequency band. The final link dynamic quality factor, Q, is the product of the link environment correction factor and the initial link assessment value. When the Q value exceeds a preset quality threshold, such as 0.8, the link is considered usable.

[0049] By quantifying and integrating dynamic motion parameters and static environmental parameters, a multi-dimensional link quality assessment system was constructed. The dual environmental factor compensation of geographic shielding coefficient and dynamic rain attenuation correction was introduced to enhance applicability under diverse terrain and climatic conditions. A normalized mathematical processing method was used to resolve the challenge of integrating multi-source heterogeneous parameters, giving the assessment results clear physical meaning. This method can accurately identify link quality changes in real-world scenarios, providing a reliable basis for subsequent path selection and anti-interference injection.

[0050] Optionally, the method further includes:

[0051] Obtain rainfall intensity parameters from real-time meteorological data;

[0052] Calculating a rain attenuation correction coefficient using the rainfall intensity parameter and a preset attenuation empirical coefficient;

[0053] The attenuation empirical coefficient is dynamically switched along with the satellite operating frequency band.

[0054] Specifically, the meteorological satellite receiving module first collects the real-time rainfall intensity parameter r of the target area, where r is the physical meaning of rainfall per unit area per unit time. Next, the attenuation model of ITU-R P.618 Recommendation is used to select the corresponding attenuation empirical coefficient k based on the satellite's current operating frequency band parameters. The k value is obtained from the corresponding frequency table in the Recommendation. Finally, the rain attenuation correction factor R is calculated:

[0055] R=r·k+b,

[0056] Where b is the base correction value for different terrain features, retrieved from a database of terrain feature codes and mappings preset by the ground base station. When the satellite switches between the C-band and Ku-band, the band parameters change, and a new table lookup is performed to obtain the new k value.

[0057] For example, a satellite operating in the Ka band receives a rainfall intensity parameter r of 50 mm per hour from a meteorological satellite report. At this time, the k value of the Ka band in tropical regions is 0.12 dB / (mm / h) from the ITU-R table. The ground base station presets the coastal terrain parameter b = 0.3 dB. Therefore, the adjusted rain attenuation correction coefficient R is 6.3 dB. When the satellite switches to the Ku band due to orbital changes, the k value of the Ku band is automatically called 0.08 dB / (mm / h), and the R value is updated to 4.3 dB under the same rainfall intensity conditions. At this time, the rain attenuation correction component in the link environment correction factor Y is adjusted from 6.3 to 4.3; by dynamically matching the correspondence between the satellite operating frequency band and the real-time meteorological conditions, it ensures that the parameters of the rain attenuation model are always consistent with the physical environment characteristics. This mechanism adopts a hierarchical operation mode, implementing environmental perception, standard query, and parameter calculation in steps to adapt to the complex and changeable operation scenarios of the satellite network. Field tests have shown that this method can overcome the hysteresis problem of traditional fixed parameters, especially in monsoon-prone regions. It can effectively improve the environmental sensitivity of the link quality coefficient and make link optimization decisions more closely aligned with actual attenuation conditions. By designing a parameter update linked to frequency band switching, it can automatically adapt to the different rain attenuation characteristics of different frequency bands, enhancing satellite network service continuity in extreme weather conditions.

[0058] Optionally, the method further includes:

[0059] When the link dynamic quality coefficient is less than the quality threshold, splitting the data to be transmitted into multiple data fragments;

[0060] Assign a transmission timestamp containing the path delay difference to each data fragment;

[0061] At least two paths are selected based on the dynamic topology map for fragment transmission.

[0062] Specifically, when the link dynamic quality coefficient Q is less than the quality threshold, the data fragmentation reassembly mechanism is adopted. First, the number of data fragments is determined based on the current number of available paths n, and the fragment size is allocated using the formula:

[0063]

[0064] Where D is the total amount of data to be transmitted, D i is the data volume of the i-th shard. Then calculate the transmission timestamp T for each shard i :

[0065] T i =t+Δt i ,

[0066] Where t is the current system time, Δt i is the path delay difference, which is obtained by weighting the historical transmission delay of each path stored in the dynamic graph. Finally, the shard groups (D1, T1), (D2, T2), ..., (D n ,T n ) Parallel transmission. During the fragment transmission process, when the fragment length D i When the maximum payload of a single path is exceeded, the secondary splitting algorithm is started. i = 40MB and the maximum payload is limited to 30MB, then divide it into D i1 =20MB and D i2 = 20MB two groups of sub-shards, sharing the original timestamp T i The number of shards n follows the principle that n ≥ 2 and the maximum number of shards does not exceed two-thirds of the number of available paths in the dynamic graph. This operation ensures that at least two physically isolated paths participate in the transmission.

[0067] For example, a polar research station needs to transmit 150MB of meteorological data, and the current link quality coefficient Q = 0.6 is detected to be lower than the quality threshold of 0.8. Five available paths are selected from the dynamic topology map, but the value of n = 3 is determined according to the maximum number of fragments = 5 × (2 / 3) = 3.33. i=150 / 3, resulting in a standard fragment size of 50MB. The system detects that the maximum payload of path B is 45MB, triggering secondary splitting to split fragment B into 22.5MB and 22.5MB. The delay differences for each fragment are taken from the measured path values ​​of Δt1 = 50ms, Δt2 = 70ms, and Δt3 = 90ms, respectively, resulting in T1 = 13:30:00.050, T2 = 13:30:00.070, and T3 = 13:30:00.090. The five fragments are then transmitted over three primary paths. Dynamically adjusting the fragmentation strategy allows the previously unsuitable large-capacity data stream to successfully adapt to different link carrying capacities. The secondary splitting and margin-sharing mechanism ensures data integrity. The path-specific allocation of delay differences is equivalent to establishing a spatiotemporal label for each data packet, creating a transmission pattern similar to a multi-track train. Even if some paths experience large delay fluctuations due to sudden interference, the receiver can still reconstruct the original timing relationship using timestamps. This method significantly improves the transmission success rate under harsh link conditions, while breaking through the capacity bottleneck of traditional single-path transmission. It is particularly suitable for reliable transmission scenarios of critical business data such as scientific research observation and emergency communications.

[0068] Optionally, the selecting at least two paths for shard transmission based on the dynamic topology map includes:

[0069] Obtain satellite motion vector parameters and the actual arrival time of each data slice at the receiving end;

[0070] Calculate the transmission time difference using the transmission timestamp of each data fragment and the actual arrival time;

[0071] Calculating a compensation value using the transmission time difference and the satellite motion vector parameter;

[0072] Correcting the actual arrival time according to the compensation value to obtain a theoretical arrival timing;

[0073] The data to be transmitted is restored according to the theoretical arrival timing.

[0074] Specifically, the satellite motion vector parameters include the satellite's current orbital position, velocity, and acceleration, which are measured in real time by the onboard GNSS receiver and stored in the adjacent node attribute library of the dynamic topology map. By calling the satellite motion vector parameters, the relative velocity between the adjacent satellite and the target satellite is calculated, and the expected delay fluctuation of each transmission path is derived to obtain the compensation value:

[0075]

[0076] where v r is the radial velocity difference between the receiving satellite and the transmitting satellite along the link direction, which is obtained by the difference of the projection components of the instantaneous velocity vectors of the two satellites in the link direction, dij is the instantaneous distance between the two satellites, which is updated in real time by the inter-satellite ranging module, c is the speed of light, a r is the radial acceleration component, t0 is the transmission time difference, that is, the transmission timestamp T i The difference between the current system time t and the actual arrival time in . When the receiving end performs fragment reassembly, it first inputs the difference between the actual arrival time of each fragment and the nominal time of the transmission timestamp into the compensation formula, and then calculates the result Δt c Superimposed on the transmission timestamp T i The path delay difference Δt in i The corrected theoretical arrival time sequence of each shard is obtained. Then, an interpolation algorithm is used to align the time axis of the discontinuous shard data in the time domain, and finally the data stream is reassembled according to the original shard order.

[0077] Optionally, performing radio frequency fingerprint extraction and frequency domain waveform inversion processing on the detected interference signal to generate an anti-interference compensation signal includes:

[0078] Perform RF fingerprint extraction on the detected interference signal to obtain the interference center frequency;

[0079] Obtain the frequency of the satellite working band and obtain the standard frequency;

[0080] Calculating the difference between the interference center frequency and the standard frequency to obtain a frequency error value;

[0081] The phase offset of the inverted waveform is adjusted according to the frequency error value and a preset adjustment weight matrix to generate an anti-interference compensation signal.

[0082] Specifically, such as Figure 2 As shown, a spectrum analyzer is first used to extract the radio frequency fingerprint of the detected interference signal. The interference center frequency is determined by peak detection of the signal's power spectrum density. The satellite communication system's configuration file is then called to obtain the standard frequency of the current satellite operating frequency band. The two frequencies are subtracted to obtain a frequency error value, representing the absolute deviation of the interference signal's center position from the nominal value of the operating frequency band. The adjustment weight matrix associated with this type of interference signal is then called from a preset dynamic interference library. The frequency error value is multiplied by the corresponding weight component in the adjustment weight matrix to obtain a phase offset. The phase offset is equal to the weighted sum of the frequency error value and the weight, with the row vectors of the adjustment weight matrix aligned with the characteristic dimension of the interference signal. Finally, a compensation signal with the same amplitude as the interference signal waveform and a phase difference of 180 degrees is generated through an inverter. The overlap between its spectrum and the interference spectrum to be eliminated is verified. Once the preset threshold is reached, the compensation signal is injected into the communication link.

[0083] For example, a low-orbit satellite operating in the Ku band detects a narrowband interference signal at 12.35 GHz. Spectral peak detection determines the interference center frequency to be 12.35 GHz. The system queries the standard frequency for the current band, which should be 12.40 GHz. The calculated frequency error is 50 MHz. The second row element of the weight matrix corresponding to the same historical interference matched from the dynamic interference library is 0.04π radians / MHz. The resulting phase offset is equal to the product of 50 MHz and 0.04π radians / MHz, or 2π radians. Based on this, the frequency domain waveform inversion processing module generates an inverted signal injection link with a frequency coverage of 12.35 GHz ± 1 MHz. Through rapid identification of RF fingerprints and high-precision frequency deviation compensation, co-band interference can be accurately offset without interrupting communication. This system has significant suppression capabilities for intentional electromagnetic interference, and the phase difference tuning mechanism of the compensation signal can adaptively match the parameters of different satellite frequency bands, enhancing the active anti-interference capability of the communication system in complex aerospace environments.

[0084] By actively sensing the spectrum intrusion characteristics of the interference signal, accurately quantifying the frequency deviation and achieving waveform cancellation through phase inversion, the technical effect is to achieve real-time dynamic interference suppression. Combined with the adaptive adjustment mechanism of the dynamic weight matrix, the compensation signal can automatically adapt to various new types of unknown interference, significantly reducing the interruption probability and bit error rate of the communication link, while avoiding the channel resource waste caused by the traditional frequency hopping method, and ensuring the stable operation of the satellite network in a complex electromagnetic environment.

[0085] Optionally, the method further includes:

[0086] Obtain the waveform characteristic parameter set of historical interference signals and establish a dynamic interference library;

[0087] Extract features of the detected interference signal to obtain a set of waveform feature parameters;

[0088] Performing similarity matching between the waveform feature parameter set and the waveform feature parameter sets of each historical interference signal in a preset dynamic interference library, and calculating a similarity value set;

[0089] When there is no similarity value in the similarity value set that is less than a preset similarity threshold, storing the waveform feature parameter set of the detected interference signal into the dynamic interference library;

[0090] The RF fingerprint feature vector is calculated using the waveform feature parameter set of the detected interference signal;

[0091] Calculating an adjustment weight matrix using the radio frequency fingerprint feature vector;

[0092] Among them, the adjustment weight matrix is ​​bound to the waveform characteristic parameter set of the corresponding interference signal and stored in the dynamic interference library; when a similarity value in the similarity value set is less than a preset similarity threshold, the corresponding adjustment weight matrix is ​​selected according to the size of the similarity.

[0093] Specifically, first, the time domain waveform of the historical interference signal is collected through a spectrum analyzer, and a waveform characteristic parameter set is extracted. The set includes three parameters: main lobe width, pulse repetition period, and spectrum skewness. Each element of the waveform characteristic parameter set is quantized by a digital signal processing algorithm to obtain a numerical value. The waveform characteristic parameter sets of different historical interferences are stored in a database in time series to form a dynamic interference library. Each record is associated with a corresponding adjustment weight matrix. When a new interference signal is detected, its waveform characteristic parameter set F is extracted. new , calculate the waveform feature parameter set and the waveform feature parameter set F of each record in the library i The similarity value S i , where S i Equal to F new With F i The sum of the Euclidean distances of the parameters in , and all S i Store the similarity value set S. If there is no similarity value in S that is less than the preset similarity threshold of 0.3, then F new The dynamic interference database is stored, and a new adjustment weight matrix is ​​calculated based on its spectrum feature vector. The RF fingerprint feature vector is analyzed by principal component analysis. new Perform 3D dimensionality reduction to obtain the matrix elements. i If it is less than 0.3, the existing matrix of the corresponding record is selected by sorting by similarity.

[0094] For example, a relay satellite detects a new type of frequency sweep interference signal and measures the parameter F new The system generates a new database entry and adds F new The RF fingerprint feature vector is stored and calculated as [0.7, 0.1, 0.2]. Using a matrix generation algorithm, the second row element of the weight matrix is ​​adjusted to 0.03π radians / MHz and stored in conjunction with the interference signature. Dynamically expanding the interference signature library enables effective identification of emerging interference patterns. The use of eigenvector decomposition during weight matrix generation enhances the physical rationality of parameter tuning, making subsequent phase adjustment of the interference compensation signal more precise. This significantly improves the system's adaptive countermeasure capabilities when encountering new and complex interference, while also avoiding the problem of miscompensation caused by the inability of traditional static databases to identify new interference patterns.

[0095] By establishing an adaptive matching mechanism between a continuously evolving interference signature library and associated compensation parameters, and employing a dynamic feature extraction and similarity comparison strategy, the system achieves self-organizing optimization of interference handling rules, autonomously improving anti-interference strategies as the electromagnetic environment changes. Through a dual mechanism of interference scenario learning and coordinated matrix parameter updates, the system can rapidly generate effective compensation signals in the face of unknown interference, while ensuring the continuous accumulation and reuse of historical empirical data, effectively improving the robustness and environmental adaptability of multi-satellite networks in complex electromagnetic environments.

[0096] Optionally, the method further includes:

[0097] Collect computing load parameters of satellite nodes;

[0098] When the computing load parameter exceeds a preset threshold, obtaining the remaining energy of the adjacent satellite node;

[0099] Calculating the node priority of the adjacent satellite node according to the link dynamic quality coefficient and the residual energy;

[0100] The relay satellite node is selected according to the priority of the node.

[0101] Specifically, the performance monitoring interface of the onboard processor is used to collect the computing load parameter in real time. The parameter is composed of the weighted sum of CPU occupancy and memory usage. The CPU occupancy is directly read by the operating system kernel, and the memory usage is obtained by reverse calculation after counting the available physical memory ratio by the memory management unit. When the computing load parameter exceeds the preset threshold value, such as 85%, the signal relay forwarding strategy of the adjacent satellite node is triggered, and the priority P is calculated. yj :

[0102]

[0103] Among them, ω1 and ω2 are weight coefficients, which are set by experience, Q is the link dynamic quality coefficient of the adjacent node, E is the residual energy of the adjacent node, and the battery residual capacity value is obtained through the onboard power management unit. max is the maximum energy storage capacity of the satellite node. If the Q value of the adjacent node is lower than the threshold such as 0.7 or E is lower than 20% of the maximum energy storage capacity of the satellite node, the priority weight is forced to zero to exclude the unavailable node. yj The adjacent node with the largest value is used as a relay node to establish an inter-node transmission channel.

[0104] For example, a geosynchronous satellite processing meteorological data detected a computing load parameter of 88.4%, exceeding the threshold of 85%. The dynamic topology map was then traversed for three adjacent nodes: Node A, Node B, and Node C. Node priorities were calculated, with Node A having a priority of 0.798 and Node B having a priority of 0.721. Node C was excluded because its remaining energy was less than 20% of the satellite's maximum energy storage capacity. Node A was then selected as the relay node, transmitting the radar image data to be processed to Node A's processor for decompression. Node A then transmitted the data back to the ground station via its optimal link. A priority algorithm, which comprehensively evaluates the link quality and energy status of neighboring nodes, ensures that relay forwarding maintains highly reliable transmission while avoiding excessive consumption of low-energy nodes. A dynamic weight allocation mechanism balances communication quality and device endurance requirements, and a rule designed to forcibly exclude low-energy nodes prevents critical nodes from failing due to overload. This method enables distributed scheduling of computing tasks when computing load exceeds the target, avoiding service interruptions caused by single-point failures. It is particularly suitable for processing sudden large amounts of data.

[0105] Through dynamic computing load sensing and resource optimization allocation, distributed collaborative transmission is automatically initiated when satellite nodes become overloaded. The technical benefits are improved computing resource utilization at satellite nodes due to task offloading, energy consumption is balanced and optimized through intelligent priority decision-making, and network reliability is enhanced through cross-node collaboration. This approach effectively addresses the service degradation problem caused by resource exhaustion at satellite nodes under high load scenarios, providing a scalable load scheduling solution for intersatellite collaborative computing.

[0106] Optionally, the method further includes:

[0107] Obtaining a relay forwarding path according to the dynamic topology map and the relay satellite nodes;

[0108] Calculating a delay variation gradient according to the relay forwarding path and the satellite motion vector parameter;

[0109] The compensation value is corrected using the delay variation gradient.

[0110] Specifically, the position parameters of adjacent nodes are first extracted from the dynamic topology map, including the three orbital elements semi-major axis, eccentricity, and orbital inclination, and the real-time distance d between the two nodes is obtained through the intersatellite rangefinder. ij Satellite motion vector parameter v yr (Radial velocity) is calculated as:

[0111] v yr =v s ·cos(θ r -α s )-v′ r·cos(θ′ r -α′ r ),

[0112] where v s is the current satellite velocity modulus, obtained by the onboard GNSS receiver, θ r is the instantaneous angle between the velocity vector and the sight direction, α s is the current satellite orbit angle, v' r is the velocity modulus of the neighboring satellite, θ' r With α' r The corresponding parameters of the neighboring nodes are retrieved from the node attribute library of the dynamic topology map. d ,have:

[0113]

[0114] where Δv yr is the radial velocity difference between two adjacent sampling periods, Δt m The default sampling time interval is 1 second. yr By differential v yr Calculate the acceleration component, t m The duration of data transmission. d Update the original compensation value Δt as a correction factor c :

[0115] Δt c-new =Δt c +G d ·k adj ,

[0116] Where k adj is the adaptive adjustment coefficient, and its value is obtained by fitting the historical compensation error curve through the least square method. When the ratio of the predicted gradient to the actual delay deviation exceeds ±15% for three consecutive times, k is triggered. adj Rolling update operation.

[0117] For example, a medium-orbit satellite forwards navigation augmentation data to a neighboring node and measures the current v s =3.87km / s,θ r =32°, α s =125°. Neighboring node parameter v' r =3.91km / s,θ' r =28°,α' r =130°. Calculate the radial velocity v yr =1.42km / s. Δv is measured after 1 second. yr =+0.05km / s,d ij =1200km, at this time ayr =0.05km / s 2 , the transmission has lasted t m =180s. Calculate the delay gradient G d =0.00023s / s. Assume that the current k adj =0.85, the original compensation amount is 0.15ms, then the updated compensation amount Δt c-new =0.1502ms. With this compensation, the standard deviation of the arrival time of subsequent data packets is reduced by 60% compared to the uncorrected state. By differentially calculating orbital parameters and motion vectors, the trend of delay variation is accurately captured, and the acceleration effect of intersatellite relative motion is incorporated into the compensation model, enabling forward-looking adjustment of the predicted gradient. The introduction of a dynamic adjustment coefficient effectively eliminates the cumulative error caused by the model's linear assumption. The closed-loop design of gradient prediction and feedback calibration enables continuous optimization of the compensation algorithm. This method is particularly suitable for inter-satellite communications in orbital plane intersections, significantly alleviating timing disruptions caused by rapid changes in intersatellite geometry.

[0118] By extracting real-time intersatellite kinematic parameters and using a time-varying gradient prediction model, dynamic, forward-looking adjustments to delay compensation are achieved. The technical benefits are reflected in improved accuracy in capturing delay trends due to acceleration calculations, enhanced algorithm robustness through adaptive adjustment of compensation coefficients, and a feedback loop between gradient predictions and measured deviations to ensure long-term compensation effectiveness. This approach enables more precise control of data transmission timing between high-speed moving satellite nodes, effectively supporting the demand for precise intersatellite time synchronization.

[0119] Based on the same inventive concept, Figure 3 As shown, the present invention also provides a multi-satellite network integrated communication link optimization and anti-interference system, the system comprising:

[0120] The link sensing module is used to obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellites and the preset geographical shielding coefficient to generate the dynamic quality coefficient of the link;

[0121] A map generation module, configured to construct a dynamic topology map according to the link dynamic quality coefficient;

[0122] The compensation calculation module is used to extract the radio frequency fingerprint of the detected interference signal and perform frequency domain waveform inversion processing to generate an anti-interference compensation signal;

[0123] The compensation execution module is used to select a target transmission path according to the dynamic topology map and inject the anti-interference compensation signal into the target transmission path when the link dynamic quality coefficient is greater than or equal to a preset quality threshold.

[0124] It should be noted that the electrical connections between the above-mentioned units do not necessarily mean direct connections of lines. Indirect connections are applicable to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above description is only an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention.

[0125] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the disclosure of the specification and practical truths. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described herein.

Claims

1. A multi-satellite network integrated communication link optimization and anti-interference method, characterized in that: The method comprises: Obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellites and the preset geographical shielding coefficient to generate the dynamic quality coefficient of the link; Constructing a dynamic topology map according to the link dynamic quality coefficient; Perform RF fingerprint extraction and frequency domain waveform inversion processing on the detected interference signal to generate an anti-interference compensation signal; When the link dynamic quality coefficient is greater than or equal to a preset quality threshold, a target transmission path is selected according to the dynamic topology map, and the anti-interference compensation signal is injected into the target transmission path.

2. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 1, characterized in that: Generating a dynamic link quality coefficient includes: Obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellites and the preset geographic shielding coefficient; The Doppler frequency shift parameter is integrated with the radiation parameters of the adjacent satellite to generate the initial link evaluation value; The geographical shielding coefficient is superimposed on a preset rain attenuation correction coefficient to generate a link environment correction factor; A link dynamic quality coefficient is calculated based on the link initial evaluation value and the link environment correction factor.

3. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 2, characterized in that: The method further comprises: Obtain rainfall intensity parameters from real-time meteorological data; Calculating a rain attenuation correction coefficient using the rainfall intensity parameter and a preset attenuation empirical coefficient; The attenuation empirical coefficient is dynamically switched along with the satellite operating frequency band.

4. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 1, characterized in that: The method further comprises: When the link dynamic quality coefficient is less than the quality threshold, splitting the data to be transmitted into multiple data fragments; Assign a transmission timestamp containing the path delay difference to each data fragment; At least two paths are selected based on the dynamic topology map for fragment transmission.

5. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 4, characterized in that: The selecting at least two paths for fragment transmission based on the dynamic topology map includes: Obtain satellite motion vector parameters and the actual arrival time of each data slice at the receiving end; Calculate the transmission time difference using the transmission timestamp of each data fragment and the actual arrival time; Calculating a compensation value using the transmission time difference and the satellite motion vector parameter; Correcting the actual arrival time according to the compensation value to obtain a theoretical arrival timing; The data to be transmitted is restored according to the theoretical arrival timing.

6. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 1, characterized in that: The step of extracting radio frequency fingerprints and performing frequency domain waveform inversion processing on the detected interference signal to generate an anti-interference compensation signal includes: Perform RF fingerprint extraction on the detected interference signal to obtain the interference center frequency; Obtain the frequency of the satellite working band and obtain the standard frequency; Calculating the difference between the interference center frequency and the standard frequency to obtain a frequency error value; The phase offset of the inverted waveform is adjusted according to the frequency error value and a preset adjustment weight matrix to generate an anti-interference compensation signal.

7. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 6, characterized in that: The method further comprises: Obtain the waveform characteristic parameter set of historical interference signals and establish a dynamic interference library; Extract features of the detected interference signal to obtain a set of waveform feature parameters; Performing similarity matching between the waveform feature parameter set and the waveform feature parameter sets of each historical interference signal in a preset dynamic interference library, and calculating a similarity value set; When there is no similarity value in the similarity value set that is less than a preset similarity threshold, storing the waveform feature parameter set of the detected interference signal into the dynamic interference library; The RF fingerprint feature vector is calculated using the waveform feature parameter set of the detected interference signal; Calculating an adjustment weight matrix using the radio frequency fingerprint feature vector; Among them, the adjustment weight matrix is ​​bound to the waveform characteristic parameter set of the corresponding interference signal and stored in the dynamic interference library; when a similarity value in the similarity value set is less than a preset similarity threshold, the corresponding adjustment weight matrix is ​​selected according to the size of the similarity.

8. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 5, characterized in that: The method further comprises: Collect computing load parameters of satellite nodes; When the computing load parameter exceeds a preset threshold, obtaining the remaining energy of the adjacent satellite node; Calculating the node priority of the adjacent satellite node according to the link dynamic quality coefficient and the residual energy; The relay satellite node is selected according to the priority of the node.

9. The multi-satellite network integrated communication link optimization and anti-interference method according to claim 8, characterized in that: The method further comprises: Obtaining a relay forwarding path according to the dynamic topology map and the relay satellite nodes; Calculating a delay variation gradient according to the relay forwarding path and the satellite motion vector parameter; The compensation value is corrected using the delay variation gradient.

10. A multi-satellite network converged communication link optimization and anti-interference system, applied to the multi-satellite network converged communication link optimization and anti-interference method according to any one of claims 1 to 9, characterized in that: The system comprises: The link sensing module is used to obtain the Doppler frequency shift parameters of the satellite node, the radiation parameters of the adjacent satellites and the preset geographical shielding coefficient to generate the dynamic quality coefficient of the link; A map generation module, configured to construct a dynamic topology map according to the link dynamic quality coefficient; The compensation calculation module is used to extract the radio frequency fingerprint of the detected interference signal and perform frequency domain waveform inversion processing to generate an anti-interference compensation signal; The compensation execution module is used to select a target transmission path according to the dynamic topology map and inject the anti-interference compensation signal into the target transmission path when the link dynamic quality coefficient is greater than or equal to a preset quality threshold.

Images