Satellite communication beam switching method

Abstract

The invention belongs to the technical field of satellite communication, and discloses a satellite communication beam switching method. The positions of a satellite and an aircraft are determined under a geocentric inertial coordinate system, an elevation angle is calculated, and visible wave beams are screened to form a neighbor cell list. And screening out a candidate beam list from the neighbor cell list based on the received power threshold. And if the list is not empty, constructing a decision matrix containing the receiving power, the signal-to-noise ratio, the number of available channels and the satellite residual service time for each candidate beam. And objectively calculating the weight of each performance index based on a matrix by using a CRITIC method. And by using the weight, a TOPSIS model based on Tanimoto similarity optimization is adopted to calculate the relative close degree of each candidate wave beam. And finally, selecting the candidate beam with the maximum relative closeness as the optimal beam to trigger switching. According to the method, through the objective weight and the optimization model, unnecessary frequent switching is reduced, and the communication stability and the system resource utilization rate are improved.

Description

Technical Field

[0001] This application belongs to the field of satellite communication technology, and relates to beam management and resource allocation in satellite communication, specifically a satellite communication beam switching method. Background Technology

[0002] In recent years, with the continuous growth of global airspace traffic, reliable and continuous remote monitoring of aircraft has become increasingly important. Low Earth Orbit (LEO) satellite communication networks, with their wide-area coverage, low transmission latency, and high communication capacity, have become an ideal platform for achieving wide-area aviation monitoring communications. In this scenario, aircraft, as high-speed mobile user terminals, need to maintain continuous communication with multiple LEO satellites rapidly passing overhead. Each satellite typically employs multi-beam antenna technology to improve capacity and coverage flexibility; therefore, during flight, aircraft not only frequently traverse the coverage areas of different satellites but may also cross different beams within the coverage area of ​​a single satellite.

[0003] This dynamic process triggers numerous beam switching demands. The rationality of beam switching decisions directly impacts the stability of regulatory communication links, the continuity of data transmission, and the utilization efficiency of the entire satellite network resources. Inappropriate switching can lead to frequent link reconstruction, increasing the risk of communication interruptions and control signaling overhead, and even causing localized network congestion. Especially in densely populated airspace or under complex weather conditions, how to scientifically and promptly select the target beam for switching becomes a key technical challenge.

[0004] To address this challenge, research has gradually shifted from the traditional strongest signal method, which relies on a single signal strength, to multi-attribute decision-making methods that comprehensively consider multiple performance indicators. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), a classic multi-attribute decision-making method, has been introduced into beam switching selection. It ranks candidate schemes by calculating their proximity to the ideal target. However, directly applying TOPSIS to the highly dynamic LEO satellite beam switching scenario still has two significant limitations: First, the weighting of various performance indicators (such as received power, signal-to-noise ratio, channel availability, and satellite service time) in the model usually relies on expert experience or fixed static configurations. This subjective or static weighting makes it difficult to accurately capture and adapt to the impact of real-time changes in network load, user distribution, and service priorities on the importance of indicators, resulting in decision results that cannot objectively reflect the optimal trade-off under the current network conditions. Second, traditional TOPSIS typically uses Euclidean distance to measure the difference between candidate schemes and the ideal solution. When there are complex correlations and conflicts among various attribute indicators (for example, a beam with a high signal-to-noise ratio may have a short remaining service time), the Euclidean distance is not sensitive enough to the differences between indicators, and may not be able to accurately distinguish candidate solutions with advantages and disadvantages in multiple attributes, thereby reducing the discriminativeness and accuracy of the decision.

[0005] Therefore, when existing beam switching methods adopt multi-attribute decision-making, the attribute weights depend on subjective settings, and the traditional Euclidean distance used for scheme ranking is difficult to accurately measure the complex conflict relationships between multi-dimensional attributes. Summary of the Invention

[0006] To address the problems of existing beam switching decisions relying on subjective experience for attribute weights and the insufficient sensitivity of traditional distance metrics to multi-dimensional attribute differences, this application proposes a TOPSIS beam switching method based on the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting method and Tanimoto similarity optimization. By objectively calculating dynamic weights and employing a more refined similarity metric, a scientific trade-off between multiple attributes is achieved, thereby improving communication link stability and overall system resource utilization efficiency.

[0007] To achieve the above technical objectives, this application adopts the following technical solution: In one aspect of this application, a satellite communication beam switching method is provided, comprising the following steps: S1. In the geocentric inertial coordinate system, determine the satellite position based on the orbital parameters of the low-orbit satellite constellation, determine the aircraft position based on the aircraft's latitude and longitude, calculate the elevation angle between the satellite and the aircraft, and include the beams covered by satellites with elevation angles greater than the preset minimum threshold into the neighboring cell list. S2. Calculate the average received power of the current serving beam and each beam in the neighboring cell list respectively. For each beam in the neighboring cell list, if its average received power minus the average received power of the current serving beam meets a preset power threshold, then add the beam to the candidate beam list. S3. If the candidate beam list is not empty, beam switching selection is performed, and multiple performance index values ​​of each candidate beam in the candidate beam list are calculated to construct a decision matrix. Each row corresponds to a candidate beam, and each column corresponds to a performance index. The performance index includes at least average received power, signal-to-noise ratio, number of available channels, and remaining service time of the satellite to which the beam belongs. S4. Based on the decision matrix, calculate the objective weights of each performance index using the CRITIC method; S5. Using the objective weights, the TOPSIS model based on Tanimoto similarity optimization is used to comprehensively evaluate each candidate beam in the candidate beam list and calculate the relative proximity of each candidate beam. S6. Select the candidate beam with the highest relative proximity as the optimal beam, trigger the aircraft to initiate a switching request to the optimal beam to establish a new communication link, and complete the beam switching.

[0008] In one implementation, based on the track inclination angle Perigeal argument Right ascension of ascending node and the semi-major axis length of the satellite orbit The satellite's coordinates in the geocentric inertial coordinate system are calculated using the following formula. : .

[0009] In one implementation, based on the aircraft's longitude ,latitude and Earth's radius The position of the aircraft in the geocentric inertial coordinate system is calculated using the following formula. :

[0010] in, This is the Earth's rotational angular rate. For time.

[0011] In one implementation, step S4, calculating the objective weights of each performance metric using the CRITIC method, includes: S41. Perform dimensionless processing on the decision matrix to obtain a standardized matrix; S42. Calculate the standard deviation of each performance index in the standardized matrix; S43. Calculate the correlation coefficient matrix between each performance index, and calculate the conflict degree of each performance index based on the correlation coefficient matrix. S44. Calculate the information content based on the standard deviation and conflict degree of each performance index; S45. Determine the objective weight of each performance indicator based on its information content.

[0012] In one implementation, step S5 involves the Tanimoto similarity-optimized TOPSIS model comprehensively evaluating each candidate beam in the candidate beam list, including: S51. Perform vector normalization on the decision matrix to obtain a normalized decision matrix; S52. Use the objective weights to weight the normalized decision matrix to obtain a weighted standardized decision matrix; S53. Determine the positive ideal solution and negative ideal solution of the weighted standardized decision matrix; S54. Calculate the Tanimoto similarity between each candidate beam and the positive ideal solution and the negative ideal solution, respectively; S55. Calculate the relative proximity of each candidate beam to the positive and negative ideal solutions based on their Tanimoto similarity.

[0013] In one implementation, in step S54, the formula for calculating the Tanimoto similarity between the candidate beam and the positive ideal solution is:

[0014] in, For the weighted standardized decision matrix, the first... The candidate beam in the first The values ​​of each performance metric For the positive ideal solution in the th case The values ​​on each performance metric.

[0015] In one implementation, in step S54, the formula for calculating the Tanimoto similarity between the candidate beam and the negative ideal solution is:

[0016] in, The negative ideal solution is in the th . The values ​​on each performance metric.

[0017] In one implementation, in step S55, the formula for calculating the relative proximity of the candidate beams is: .

[0018] In one implementation, if the candidate beam list is empty in step S3, the aircraft maintains its connection with the currently serving beam.

[0019] The beneficial effects of this application are as follows: By introducing the CRITIC method to dynamically calculate the weights of each performance index, the limitations of subjective experience or static configuration are overcome, enabling the weight allocation to objectively reflect the real-time network status. The Tanimoto similarity method is used instead of traditional Euclidean distance, enhancing the decision model's ability to distinguish nonlinear differences between different attributes and improving evaluation accuracy when multi-dimensional indices conflict. This application's method comprehensively considers multiple factors such as received power, signal-to-noise ratio, channel availability, and satellite service time, effectively suppressing unnecessary frequent handovers while ensuring communication link quality, thereby improving the overall stability and resource utilization efficiency of the low-Earth orbit satellite monitoring communication system. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the beam switching selection algorithm according to an embodiment of this application; Figure 2 This is a schematic diagram of a scenario according to an embodiment of this application; Figure 3 This is a comparison of the received power of different algorithms in the embodiments of this application; Figure 4 This is a comparison of the signal-to-noise ratio of different algorithms in the embodiments of this application; Figure 5 This application compares the number of available channels for different algorithms in its embodiments. Figure 6 This is a comparison of the number of inter-satellite handovers using different algorithms in the embodiments of this application. Detailed Implementation

[0021] The technical solution of this application will be clearly and completely described below with reference to specific embodiments. However, those skilled in the art will understand that the embodiments described below are only some embodiments of this application, not all embodiments, and are only used to illustrate this application, and should not be regarded as limiting the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To address the inherent shortcomings of traditional multi-attribute decision-making methods in beam switching selection under high-speed dynamic environments of low-Earth orbit satellites, this application constructs an enhanced decision-making framework that combines objective weight allocation with refined similarity measurement. Traditional methods rely on subjective experience in setting attribute weights, making it difficult to adapt to real-time changes in network states. Furthermore, their proximity calculation based on Euclidean distance is insensitive to complex correlations and conflicts between multi-dimensional attributes, resulting in insufficient decision discrimination. Therefore, this application makes key improvements to the standard TOPSIS model: First, it introduces the CRITIC objective weighting method, which automatically generates weights by quantifying the degree of variation within each attribute index and its conflict with other indicators, enabling the weight values ​​to dynamically and objectively reflect the true importance of each index under the current network conditions. Second, it uses the Tanimoto similarity coefficient instead of Euclidean distance to calculate the closeness between candidate solutions and the ideal solution. This coefficient can more effectively capture and weigh the common features and differences between multiple attribute values, and is particularly suitable for handling scenarios where index values ​​have inverse relationships, thereby improving the ability to distinguish similar candidate beams. By organically integrating the above two improvements into the TOPSIS decision-making process, this application forms a beam selection mechanism that can adapt to highly dynamic environments and finely handle multi-dimensional index trade-offs, ultimately achieving the goal of optimizing the overall system resource utilization while maintaining communication quality.

[0023] In one specific implementation, a satellite communication beam switching method is provided, comprising the following steps: S1. In the geocentric inertial coordinate system, determine the satellite position based on the orbital parameters of the low-Earth orbit satellite constellation, determine the aircraft position based on the aircraft's latitude and longitude, calculate the elevation angle between the satellite and the aircraft, and include the beams covered by satellites with elevation angles greater than a preset minimum threshold into the neighboring cell list.

[0024] In some embodiments, the satellite's orbit is determined by its orbital inclination. Perigeal argument Right ascension of ascending node and the semi-major axis length of the satellite orbit Key parameters are defined. Using these parameters, the satellite's coordinates in the geocentric inertial coordinate system are calculated using the following spatial geometric transformation formula. : .

[0025] In some embodiments, the aircraft's location is determined by its geodetic longitude. ,latitude and Earth's radius A joint decision. Considering the Earth's rotation, the geodetic coordinates will be converted to coordinates in a geocentric inertial coordinate system. The conversion formula is as follows:

[0026] in, This is the Earth's rotational angular rate. This is the time calculated from the reference time.

[0027] In some embodiments, the elevation angle between the satellite and the aircraft is calculated, and the beams covered by satellites with elevation angles greater than a preset minimum threshold are included in the neighboring cell list. Specifically: Based on the calculated satellite and aircraft position information, the elevation angle of the satellite observed from the aircraft is further calculated. Angle of elevation It is a direct criterion for determining whether a satellite is visible to an aircraft. A minimum elevation angle threshold is preset. Threshold It is usually determined by the system communication link budget, antenna performance, and the need to avoid obstruction from ground obstacles.

[0028] Iterate through all low-Earth orbit satellites and calculate the elevation angle of each satellite relative to the aircraft. If the elevation angle of a certain satellite satisfy If the satellite is visible to the aircraft, it is determined that the satellite is visible to the aircraft. All beams within the communication coverage area of ​​all visible satellites that can serve the aircraft are aggregated to form a neighboring cell list. This neighboring cell list contains the set of all potential beams from which the aircraft may switch access at the current time and in the near future.

[0029] S2. Calculate the average received power of the current serving beam and each beam in the neighboring cell list respectively. For each beam in the neighboring cell list, if its average received power minus the average received power of the current serving beam meets a preset power threshold, then add the beam to the candidate beam list.

[0030] First, the system needs to acquire and calculate key power measurements. On one hand, it monitors the signal strength of the communication link between the aircraft and the currently connected serving beam, and calculates the average received power of the current serving beam. On the other hand, it simultaneously measures or predicts the signal strength of each beam in the neighboring cell list at the aircraft, and calculates the average received power of each beam accordingly.

[0031] Subsequently, each beam is evaluated based on a preset power threshold. The power threshold is a positive threshold value pre-set according to system communication quality requirements, handover strategies, and factors to avoid ping-pong handover. For each beam in the neighboring cell list, the system calculates the difference between its average received power and the average received power of the currently serving beam, and compares this difference with the power threshold.

[0032] Next, a filtering logic is executed to construct a candidate beam list. Only when the average received power of a neighboring cell beam meets the preset power threshold is the beam considered to have a significant advantage in signal strength relative to the currently serving beam and possess the potential to become a handover target. The system filters out all beams that meet the preset power threshold and aggregates them to form the candidate beam list.

[0033] S3. If the candidate beam list is not empty, beam switching selection is performed, and multiple performance index values ​​of each candidate beam in the candidate beam list are calculated to construct a decision matrix.

[0034] The system checks if the candidate beam list is empty. If the list is not empty, it indicates that there are one or more potential target beams with a greater advantage in received power, and the process continues.

[0035] First, for each candidate beam in the candidate beam list, multiple key performance metrics are acquired or calculated in parallel or sequentially. These performance metrics reflect the beam's communication quality, resource status, and service sustainability from different dimensions.

[0036] In some embodiments, the selected performance metrics include average received power, signal-to-noise ratio (SNR), number of available channels, and remaining service time of the satellite to which the beam belongs. The average received power directly characterizes the basic situation of signal strength and link budget; the SNR is the ratio of signal power to noise power in the communication link between the aircraft and the candidate beam, used to measure the transmission quality and reliability of the communication link; the number of available channels is obtained from the network side or by querying resource management status, determining the number of channels that the candidate beam is currently not occupied and can be immediately allocated to new connections, reflecting the current load level and resource sufficiency of the candidate beam; the remaining service time of the satellite to which the beam belongs is calculated or estimated based on the satellite orbital dynamics model and the aircraft's predicted position, representing the estimated remaining duration that the satellite currently providing service to the candidate beam can continuously cover and serve the aircraft, used to predict the potential stability of the connection after handover and avoid connecting to satellites that are about to leave the line of sight.

[0037] Secondly, after acquiring the performance index values ​​of all candidate beams, the system organizes this data in a structured manner to construct a decision matrix. In the decision matrix, each row corresponds to a specific candidate beam, and each column corresponds to a performance index. For example, the first column stores the received power value of each beam, the second column stores the signal-to-noise ratio value, the third column stores the available channel value, and the fourth column stores the remaining service time of the satellite.

[0038] If the list is empty, it indicates that no beam in the current neighboring cell list has a significant advantage in received power compared to the currently serving beam. In this case, the system determines that no handover procedure needs to be initiated, and the aircraft will maintain its existing connection with the current serving beam, thereby avoiding unnecessary handover signaling overhead and potential interruption risks.

[0039] S4. Based on the decision matrix, calculate the objective weights of each performance index using the CRITIC method.

[0040] Since the various performance metrics (average received power, signal-to-noise ratio, number of available channels, and remaining service time) have different dimensions and orders of magnitude, direct comparison or aggregation can lead to bias. Therefore, the decision matrix is ​​dimensionless to obtain a standardized matrix.

[0041] In some embodiments, the formula for calculating the normalized matrix is:

[0042] in, For the first The candidate beam in the first The original values ​​of each indicator; and These represent the positions of all candidate beams at the t-th... The maximum and minimum values ​​of each indicator.

[0043] After processing, the values ​​of all elements in the standardized matrix are located in the interval [0,1].

[0044] Secondly, each indicator is calculated based on the standardized matrix. The standard deviation of all candidate beam values.

[0045] In some embodiments, the mean of each indicator is calculated first: ; Then calculate the standard deviation:

[0046] in, This indicates the number of candidate beams.

[0047] The standard deviation quantifies the dispersion of the index data among candidate beams. The greater the dispersion, the more information the index provides in distinguishing different candidate beams.

[0048] Next, the correlation coefficients between the indicators are calculated and the degree of conflict is determined.

[0049] In some embodiments, any two distinct indices in the normalization matrix are calculated. and The Pearson correlation coefficients between them form a correlation coefficient matrix. Correlation coefficient matrix for:

[0050] in, Indicates the first Each indicator in all The numerical average value across the candidate beams; This indicates the number of performance metrics.

[0051] For a specific indicator The degree of conflict is calculated by summing its correlation coefficients with all other indicators. :

[0052] in, Indicators and Pearson correlation coefficient between them The value range of is [-1, 1]. This is achieved by... According to the query, the closer the absolute value is to 1, the stronger the linear correlation between the two indicators.

[0053] Then, the information content of each indicator is calculated by combining the standard deviation and the degree of conflict.

[0054] In some embodiments, for indicators Its information content The standard deviation of this indicator Its degree of conflict The product determines: .

[0055] Information content Comprehensively reflects the indicators The total amount of evaluation information contained in both data volatility (discriminating power) and differences from other indicators (independent contribution). The greater the amount of information, the more important the indicator may be in decision-making.

[0056] Finally, the information content is normalized to obtain the final objective weights of each performance indicator.

[0057] In some embodiments, the information content of each indicator is... Divide by the sum of the information content of all indicators to obtain the weight of that indicator. :

[0058] Weight Driven entirely by the decision matrix data itself, it avoids subjective assumptions and objectively reflects the actual relative importance of each performance indicator in the comprehensive evaluation under the current candidate beam set.

[0059] S5. Using the objective weights, the TOPSIS model based on Tanimoto similarity optimization is used to comprehensively evaluate each candidate beam in the candidate beam list and calculate the relative proximity of each candidate beam.

[0060] First, the decision matrix is ​​vector normalized to eliminate the influence of the dimensions of each indicator and to scale the data to a uniform scale.

[0061] In some embodiments, for each element in the decision matrix Its normalized value is calculated using the following formula: .

[0062] A normalized decision matrix is ​​generated through normalization. ,in, Indicates the number of candidate beams. This represents the number of performance metrics.

[0063] Secondly, the normalized decision matrix is ​​weighted using the determined objective weights.

[0064] In some embodiments, each normalized value is... Multiply by the objective weight of its corresponding indicator The weighted standardized decision matrix is ​​obtained. : .

[0065] Subsequently, the benchmarks for evaluation are determined, namely the positive ideal solution and the negative ideal solution. The positive ideal solution is composed of the maximum value in each column of the weighted standardized decision matrix, representing the theoretically optimal virtual solution; the negative ideal solution is composed of the minimum value in each column, representing the theoretically worst virtual solution.

[0066] In some embodiments, candidate beams Similarity to Tanimoto solution with the positive ideal solution The calculation formula is: ; Candidate Beam Tanimoto similarity to negative ideal solution The calculation formula is: ; in, For the weighted standardized decision matrix, the first... The candidate beam in the first The values ​​of each performance metric For the positive ideal solution in the th case The values ​​of each performance metric For the negative ideal solution in the th case The values ​​on each performance metric.

[0067] Finally, based on the two similarities mentioned above, the candidate beam is calculated. Relative closeness .

[0068] In some embodiments, the calculation formula is as follows: .

[0069] S6. Select the candidate beam with the highest relative proximity as the optimal beam, trigger the aircraft to initiate a switching request to the optimal beam to establish a new communication link, and complete the beam switching.

[0070] Example This embodiment designs a beam switching selection method for low-Earth orbit satellite surveillance aircraft. Based on the CRITIC weighting method and the TOPSIS multi-attribute decision model optimized by Tanimoto coefficients, it comprehensively considers multiple performance indicators such as beam received power, signal-to-noise ratio, number of available channels, and remaining satellite service time. Through objective weight calculation and Tanimoto similarity evaluation, it achieves scientific and reasonable beam switching decisions, reduces unnecessary frequent switching, and improves the stability of surveillance communication and system resource utilization. (Refer to...) Figure 1 As shown, the technical solution is as follows: Step 1: Refer to Figure 2 As shown, a low-Earth orbit satellite constellation environment is built, and the aircraft flight trajectory is preset. The coordinate system for modeling and outputting the satellite orbit is ECI, and the orbit is established as a circular orbit.

[0071] Step 2: Determining the position of a low-Earth orbit satellite mainly involves the orbital inclination ( ), perigee argument ( ), ascending node right ascension ( ) and the semi-major axis length of the satellite orbit ( The coordinates of the satellite in the ECI coordinate system can be obtained from the following formula. : .

[0072] The position of the aircraft in the geocentric inertial coordinate system can be obtained from the following formula. : .

[0073] Angle of elevation between aircraft and satellite There is a minimum threshold ,satisfy The satellite is visible to aircraft, and all beams within the coverage area of ​​the visible satellite are included in the neighboring cell list. .

[0074] Step 3: Set a power threshold Calculate the average received power of the currently serving beam. as well as Average received power of mid-wave beam When satisfied At that time, add this beam to the candidate beam list. .

[0075] Step 4: If If not empty, perform beam switching selection; otherwise, maintain the current connection. Calculation The decision matrix is ​​composed of the average received power, signal-to-noise ratio, remaining channel number, and remaining service time of the satellite to which the beam belongs among all candidate beams. :

[0076] in, Indicated as respectively No. The average received power, signal-to-noise ratio, number of remaining channels, and remaining service time of the satellite to which the beam belongs for each candidate beam.

[0077] Step 5: Calculate the objective weights of each attribute using the CRITIC method. 1) For the decision matrix X Perform dimensionless processing: .

[0078] 2) Calculate the standard deviation of each indicator: .

[0079] 3) Calculate the correlation coefficient matrix between the indicators. And define the degree of conflict:

[0080] in, Indicators i and j The correlation coefficient between them, It can be found through a search.

[0081] 4) Calculate the information content by combining standard deviation and conflict level: .

[0082] 5) Final determination of weights: .

[0083] Step 6: Perform optimal beam selection using the Tanimoto-TOPSIS model. 1) For the decision matrix X Perform vector normalization: .

[0084] 2) Calculate the weighted standardized decision matrix: .

[0085] 3) Determine the positive and negative ideal solutions:

[0086] 4) Calculate the Tanimoto similarity between the candidate beam and the positive and negative ideal solutions:

[0087] 5) Calculate the relative proximity of the candidate beams:

[0088] Step 7: Select the beam with the highest relative proximity as the optimal beam. The aircraft sends a switching request to the selected optimal beam to establish a new communication link and complete the beam switching process.

[0089] To verify the effectiveness of the method proposed in this invention, its performance was compared and analyzed with the traditional strongest signal method and the entropy weight method-TOPSIS method.

[0090] The verification results show that the beam switching selection method of the TOPSIS model based on the CRITIC weighting method and Tanimoto coefficient optimization (hereinafter referred to as CRITIC-tTOPSIS) has advantages in many aspects.

[0091] like Figure 3The CRITIC-tTOPSIS achieved an average received power of -95.71 dBW, a 0.24 dB improvement over the strongest signal method (-95.95 dBW). The strongest signal method, relying solely on instantaneous signal strength for switching, while selecting the strongest power beam at a single point in time, resulted in frequent switching, causing drastic fluctuations in the received signal and consequently reducing the average received power. In contrast, the CRITIC-tTOPSIS exhibited a smoother received power curve and a significantly reduced switching frequency, demonstrating superior received signal stability. This is because its multi-attribute decision framework balances received power with other key performance indicators, avoiding blind switching driven by a single parameter and thus ensuring signal reception stability.

[0092] like Figure 4 The average signal-to-noise ratio (SNR) of CRITIC-tTOPSIS is 15.47 dB, which is about 0.23 dB higher than the maximum power algorithm (15.23 dB). This slight improvement in SNR directly reflects the multi-objective optimization logic. CRITIC-tTOPSIS is not limited to optimizing a single metric, but seeks a balance among multiple performance dimensions, achieving a steady improvement in SNR while ensuring communication quality meets application requirements.

[0093] like Figure 5 The average number of available channels in CRITIC-tTOPSIS reached 14.19, nearly twice that of the strongest signal method (7.83), and higher than that of the entropy weight method-TOPSIS (13.97). This improvement is due to the objective weighting mechanism of CRITIC, which can dynamically quantify the importance of each indicator according to the real-time network status, making the algorithm more inclined to select beams with sufficient resources during the decision-making process, effectively improving the spectrum utilization of the entire system.

[0094] like Figure 6 The CRITIC-tTOPSIS method requires only 23 inter-satellite handovers, a 28% reduction compared to the 32 handovers of the strongest signal method, and lower than the 25 handovers of the entropy weight method-TOPSIS method. This reduction in handover frequency directly improves the stability of the communication link. The core reason is that the algorithm incorporates the satellite's remaining service time into its multi-attribute decision-making process, accurately avoiding unnecessary handovers caused by fluctuations in a single parameter, and reducing signaling overhead and interruption risks associated with link reconstruction.

[0095] Although the embodiments of this application have been described above in conjunction with the accompanying drawings, this application is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of this application, and these are all within the scope of protection of this application.

Claims

1. A satellite communication beam switching method, characterized in that, Includes the following steps: S1. In the geocentric inertial coordinate system, determine the satellite position based on the orbital parameters of the low-orbit satellite constellation, determine the aircraft position based on the aircraft's latitude and longitude, calculate the elevation angle between the satellite and the aircraft, and include the beams covered by satellites with elevation angles greater than the preset minimum threshold into the neighboring cell list. S2. Calculate the average received power of the current serving beam and each beam in the neighboring cell list respectively. For each beam in the neighboring cell list, if its average received power minus the average received power of the current serving beam meets a preset power threshold, then add the beam to the candidate beam list. S3. If the candidate beam list is not empty, beam switching selection is performed, and multiple performance index values ​​of each candidate beam in the candidate beam list are calculated to construct a decision matrix. Each row corresponds to a candidate beam, and each column corresponds to a performance index. The performance index includes at least average received power, signal-to-noise ratio, number of available channels, and remaining service time of the satellite to which the beam belongs. S4. Based on the decision matrix, calculate the objective weights of each performance index using the CRITIC method; S5. Using the objective weights, the TOPSIS model based on Tanimoto similarity optimization is used to comprehensively evaluate each candidate beam in the candidate beam list and calculate the relative proximity of each candidate beam. S6. Select the candidate beam with the highest relative proximity as the optimal beam, trigger the aircraft to initiate a switching request to the optimal beam to establish a new communication link, and complete the beam switching.

2. The satellite communication beam switching method according to claim 1, characterized in that, According to the orbital inclination angle Perigeal argument Right ascension of ascending node and the semi-major axis length of the satellite orbit The satellite's coordinates in the geocentric inertial coordinate system are calculated using the following formula. : 。 3. The satellite communication beam switching method according to claim 1, characterized in that, According to the longitude of the aircraft ,latitude and Earth's radius The position of the aircraft in the geocentric inertial coordinate system is calculated using the following formula. : in, This is the Earth's rotational angular rate. For time.

4. The satellite communication beam switching method according to claim 1, characterized in that, In step S4, calculating the objective weights of each performance index using the CRITIC method includes: S41. Perform dimensionless processing on the decision matrix to obtain a standardized matrix; S42. Calculate the standard deviation of each performance index in the standardized matrix; S43. Calculate the correlation coefficient matrix between each performance index, and calculate the conflict degree of each performance index based on the correlation coefficient matrix. S44. Calculate the information content based on the standard deviation and conflict degree of each performance index; S45. Determine the objective weight of each performance indicator based on its information content.

5. The satellite communication beam switching method according to claim 1, characterized in that, In step S5, the TOPSIS model based on Tanimoto similarity optimization performs a comprehensive evaluation of each candidate beam in the candidate beam list, including: S51. Perform vector normalization on the decision matrix to obtain a normalized decision matrix; S52. Use the objective weights to weight the normalized decision matrix to obtain a weighted standardized decision matrix; S53. Determine the positive ideal solution and negative ideal solution of the weighted standardized decision matrix; S54. Calculate the Tanimoto similarity between each candidate beam and the positive ideal solution and the negative ideal solution, respectively; S55. Calculate the relative proximity of each candidate beam to the positive and negative ideal solutions based on their Tanimoto similarity.

6. The satellite communication beam switching method according to claim 5, characterized in that, In step S54, the formula for calculating the Tanimoto similarity between the candidate beam and the positive ideal solution is: in, For the weighted standardized decision matrix, the first... The candidate beam in the first The values ​​of each performance metric For the positive ideal solution in the th case The values ​​on each performance metric.

7. The satellite communication beam switching method according to claim 6, characterized in that, In step S54, the formula for calculating the Tanimoto similarity between the candidate beam and the negative ideal solution is: in, The negative ideal solution is in the th . The values ​​on each performance metric.

8. The satellite communication beam switching method according to claim 7, characterized in that, In step S55, the formula for calculating the relative proximity of the candidate beams is: 。 9. The satellite communication beam switching method according to claim 1, characterized in that, If the candidate beam list is empty in step S3, the aircraft maintains its connection with the currently serving beam.

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